Research Article

INTRODUCTION

The most recent outbreak of severe acute respiratory syndrome (SARS) has been caused by coronavirus-2 (SARS-CoV-2) – a new single-strand, positive-sense-RNA beta-coronavirus first reported in 2019 in Wuhan, China. The virus has spread to nearly all countries across the world.^1-4

SARS-CoV-2 infection, also known as Coronavirus Disease 2019 (COVID-19), replicates mainly in the upper and lower respiratory tract. The transmission of COVID-19 from symptomatic and asymptomatic patients is usually through respiratory droplets, generated by coughing and sneezing or through contact with contaminated surfaces.^4,5 The disease has an incubation period of approximately 5.2 days.⁶

Most infections are mild and uncomplicated.⁴ After one week of the onset of disease, 5-10% of patients tend to develop pneumonia, needing hospitalisation.^4,6 Some of these patients develop further complications, often leading to death.^4,6 The overall case fatality rate is 1.4%, with a noticeably higher rate after the sixth decade of life.⁴

People aged ≥ 60 years, especially with underlying medical conditions – such as cardiovascular disease, hypertension, diabetes mellitus (DM), chronic respiratory disease, cancer, immunodeficiency, obesity – and those of male-sex, have an increased risk of dying.^4,7-12 Risk of severe adverse outcome is also associated with an increased number of associated co-morbidities.¹⁰

The impact of active cancer, endocrine disorders, autoimmune inflammatory rheumatic diseases etc. on COVID-19 outcomes has been investigated widely.^13-18 Divergent views have emerged regarding the role of renin angiotensin aldosterone system (RAAS) inhibitors, steroids, and immunomodulators in COVID-19 mortality.

The objective of our study was to evaluate the risk posed by epidemiological and demographic variables in our local population. We also sought to analyse the impact of co-morbidities on in-hospital mortality in confirmed COVID-19 patients.

METHODS

Study design:

We conducted a retrospective analysis of demographics characteristics (age and sex) and medical co-morbidities – hypertension, chronic heart failure, ischaemic heart disease, DM, thyroid disorders, asthma, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD) (eGFR < 60 mL/min/1.73 m2), chronic liver disease, active malignancy, immunosuppression, post-transplant status, chronic inflammatory arthritis and other rheumatic disorders – in all patients with confirmed COVID-19, who were admitted in two peripheral district general hospitals under a single National Health Service (NHS) trust serving primarily the rural population of western England.

Inclusion and Exclusion Criteria:

To determine COVID-19 status, nose and throat-swab specimens were obtained for real-time reverse transcription polymerase chain reactions (rt-PCR) in all adult (≥18 years) patients, attending one of the two district general hospitals (Royal Shrewsbury Hospital, Shrewsbury; and Princess Royal Hospital, Telford) under Shrewsbury & Telford Hospitals NHS Trust (SaTH) in the period from 1^st March to 15^th May 2020.

Patients who tested positive (either by N gene and ORF1ab gene positive / ORF1ab gene positive or N gene positive) and required subsequent in-hospital management were included in the study. Patients who were discharged after initial senior review (usually by a consultant physician), or brought in as a cardiac-respiratory arrest, were excluded. Re-admissions to the hospital beyond 48 hours following hospital discharge due to COVID-19 were excluded from the study. Patients diagnosed solely on radiological or clinical findings without a positive rt-PCR test were not included in our study.

We analysed the data based on the index-admission (including failed-discharge: re-admission within 48 hours following hospital discharge). No follow-up data was collected post-hospital discharge of these patients.

Data collection & analysis:

A list of all confirmed COVID-19 patients over a 76-day period was identified from the trust microbiology database. A search of the electronic patient records was completed by four members of our team. Supplementary data was gleaned from existing hospital paper records. Patient demographics, presenting symptoms, associated co-morbidities, medications, admission and discharge dates, intensive therapy unit (ITU) admissions, renal profile, referral source and outcomes were recorded in the specifically designed electronic datasheet.

Study Outcome:

The impact of epidemiological and demographic characteristics, and pre-existing medical conditions on the mortality of confirmed COVID-19 patients requiring in-hospital treatment was analysed.

RESULTS

A total of 303 confirmed COVID-19 (rt-PCR positive) samples were collected over a 76-day period. Five patients had been tested twice, and this was accounted for. Thirty-five patients were excluded from the study: twenty-four of them discharged after initial senior review without requiring in-hospital treatment, seven brought in with cardio-pulmonary resuscitation (CPR) in progress, three had inadequate data, and one was <18 years old. Of the 263 patients admitted, 70 (26.6%) died in hospital (Figure-1).

Figure-1: Flowchart of sampling and analysis

We stratified the mortality rates among the admitted patients by age (Table-1). A chi-square test of independence revealed that the mortality rate was significantly related to an advanced age (χ² =27.078, p<0.001). The age and sex distributions of admissions and mortality are shown in Figure-2 (a, b, c).

Table-1: Medical admissions and mortality stratified by age

N: number of patients, m: male, f: female.

Figure-2 (a,b,c): Age, Sex, Admission and Mortality pyramids

We considered two age cohorts - below 60 and ≥60 years of age and other relevant demographic parameters (sex and residence in own-home/care-home) to analyse the impact on mortality rates (Table-2). Of the admitted patients, 159 (60.5%) were male, and 104 (39.5%) were female. The mortality rate was strongly associated with advanced age ≥60 years (χ² =17.120, p<0.001) but independent of sex distribution (χ² =1.784, p=0.182). However, it was also affected by the care facility (χ² =18.146, p<0.001) with a higher mortality rate among the group of patients with residence in a long-term care-home.

Table-2: Admission and Mortality stratified by demographic variables

N: Number of patients; Care-home: Long-term care in residential or nursing home.

To identify the strength of the associations, we conducted a univariate logistic regression analysis with mortality as the dependent variable and the demography and presence/absence of the co-morbidities as the independent variable (Table-3). We found that age as a continuous predictor had an odds ratio of 1.058 (p<0.001), which translated to increased odds of dying by 5.8% for every year of advanced age. Using age as a categorical predictor with the other two categories, the odds of death for patients aged below 60 years was found to be 0.195 times the odds of death for the patients aged 60 years or above.

Table-3: Univariate logistic regression analysis of the demographic variables and co-morbidities

^*Cardiovascular: Hypertension, chronic heart failure, ischaemic heart disease, chronic arrhythmias; ^§Respiratory: Asthma, chronic obstructive pulmonary disease, interstitial lung disease, asbestosis; ^$DM & Endocrine: Diabetes Mellitus (DM), thyroid disorders, Addison's; ^£Renal: Chronic Kidney Disease (eGFR < 60 mL/min/1.73 m2), on dialysis, chronic nephritis, renal transplant; ^§§Rheumatic: Rheumatoid arthritis, inflammatory Polyarthritis, chronic vasculitis Syndrome, connective tissue disorders; ^±Liver & hepato-biliary: Fatty liver, non-alcoholic steatohepatitis, liver transplant; ^€Thyroid: Hypothyroidism, hyperthyroidism, post-thyroidectomy; ^€€Long-term oral steroids: Prednisolone, hydrocortisone, dexamethasone; ^$$Immunomodulators: Methotrexate, tacrolimus, sirolimus, mycophenolate, dapsone, sulfasalazine and azathioprine.

Based on the Charlson Comorbidity Index (CCI) score, the severity of co-morbidities was categorised into four cohorts: mild/no co-morbidity (CCI:0), moderate (CCI:1-2), severe (CCI:3-4), and very severe (CCI≥5) [Table-4(4a)].

Table-4: Impact of CCI score and specific medical-conditions on admission and mortality
4a) Admission and mortality stratified by CCI score based cohorts

4b) Admission and mortality stratified by specific medical-conditions

N: Number of patients; DM: Diabetes Mellitus; *RAAS-inhibitors; ^§Non RAAS-inhibitors.

The impact of CCI score-based cohorts on mortality are shown in Figure-3 (a-f). CCI value also predicted significant association with odds ratio 1.255 (p<0.001). If the CCI score was utilised as a categorical predictor with the other two parameters (age and place of primary care), it remained a significant predictor with the odds of death for the patients with CCI-scores between 0-4 turning out to be 44.8% (p=0.005) of the odds of death for the patients with CCI scores ≥5 (Table-3).

Figure-3(a - f): Pie-chart representing impact of CCI score-based cohorts on mortality


a) Overall admitted patients: discharge and mortality; b) CCI score 0: discharge and mortality; c) CCI score 1-2: discharge and mortality; d) CCI score 3-4: discharge and mortality; e) CCI score ≤4: discharge and mortality; f) CCI score ≥5: discharge and mortality.

Interestingly, the eGFR at presentation turned out to be a significant predictor of mortality (OR=0.961, p<0.001). Of the co-morbidities, pre-existing renal disease was found to be an important predictor of mortality with OR=1.996 (p=0.027). Long-term oral steroids were another significant predictor of mortality, with the odds of death for the patients with long-term oral steroids use being 341.2% (p=0.016) of the odds of death for the patients without such medication. Patients with no background medical conditions (OR=0.181, p=0.022) fared better, with significantly lower odds of death compared to patients with at least one known medical condition (Table-3).

We also analysed the mortality of our patients with specific medical condition-based cohorts [Table-4(4b)]. A high mortality of 52.9% [OR (95%CI): 4.053(1.091–15.063), p=0.037] was observed in patients who were on long-term oral steroids. A 33.3% [OR (95%CI):1.510(0.791–2.883), p=0.212] mortality rate was observed among in-patients with known diabetes on pharmacotherapy.

Many of the demographic variables and the co-morbidities were inter-related – the odds of death for a patient coming from their own-home was only 26% (OR=0.263, p<0.001) of the odds for those residing in a long-term care-home (Table-3). To offset the possibility of any confounding effect, we utilised multiple logistic regression analysis with all the important variables taken together (Table-5). Taking consideration of confounding effects, only age, care facility, presence of active malignancy and long-term oral steroids were found to be significant predictors of mortality. Interestingly, the presence of active malignancy was found to have a lower risk of death – this is possibly due to a bias on account of a relatively small number of patients in that subset of our study. Age was the most significant predictor of mortality, followed by a primary area of the care facility and the presence of active malignancy.

Table-5: Multiple logistic regression analysis of the demographic variables and co-morbidities

DM: Diabetes Mellitus

DISCUSSION

COVID-19 has taken 800,000 lives world-wide as reported by the World Health Organisation (WHO) on August 30, 2020. A recent systematic review and meta-analysis have reported the association of COVID-19 with a severe disease course in about 23% of infected patients and has a mortality of about 6%.¹⁹ The mortality rate varies in different geographical areas. In-hospital mortality was significantly higher in the United States of America (USA) (22.23%) and Europe (22.9%) compared to Asia (12.65%) – (p<0.0001).²⁰ However, there was no significant difference when compared to each other (p=0.49).²⁰ Our study showed a 26.6% in-hospital mortality.

The mean age of the patients in our study was 68.74 years (SD:16.89) – 60.5% of them were male and 39.5% female. 70.7% of these patients were aged ≥60 years. Univariate analysis showed that the mortality rate was significantly age-dependent (OR=1.058, p<0.001) – mortality (33.9%) was higher in patients aged ≥60 years, rising sharply ≥80 years to 44.0% (χ² =27.078, p<0.001). Our results were consistent with other studies.²¹

Among the demographic characteristics, mortality-risk was independent of sex distribution (χ² =1.784, p=0.182) in our study. This is in contrast to a meta-analysis, which reported the association between male-sex and COVID-19 mortality (OR =1.81; 95%CI:1.25–2.62).²² Multicentric studies in the United Kingdom (UK) would be warranted to see the trend in the local population.

Long-term care-home residents suffered 50.0% mortality (χ² =18.146, p<0.001). The London School of Economics report on May 14, 2020, estimated that the COVID-19 related deaths of care-home residents contributed to 54% of all excess deaths in England and Wales. Our study findings indicate long-term care-homes as hot-spots requiring shielding and protective measures against COVID-19 – a conclusion corroborating other studies.²³

We aimed to define the predictive-role of co-morbidities on COVID-19 mortality, an aspect that has been probed earlier as well.^7-12 The CCI score remains a reliable method to measure co-morbidity.²⁴ For admission to intensive care, NICE recommended CCI-score ≥ 5 requires critical care advice to help in treatment decision regarding the essential benefit of organ support for seriously unwell COVID-19 patients. We examined the predictive mortality-risk of CCI scores among the admitted patients.

The mortality rate in cohorts with CCI ≤4 and CCI scores ≥5 were 20.3% and 36.2% respectively. The odds of death for CCI ≤4 cohort was less than half (44.8%) compared with CCI scores ≥5 cohort. Based on this finding, we strongly recommend CCI scoring as a clinical risk-stratification tool in COVID-19.

We examined the impact of organ specific co-morbidities on in-hospital mortality in our study as well. Patients with no background medical conditions showed a low mortality rate 6.9% [OR (95%CI): 0.181(0.042–0.782), p=0.022] and had better outcomes with significantly lower odds of death, compared to patients with at least one medical condition on univariate logistic regression analysis (Table-3). The mortality rate was 3.2% in CCI-0 cohort [Table 4(4a)].

The impact of COVID-19 on patients with CKD, glomerulo-nephropathies, on dialysis dependent patients and post renal transplant patients remains unclear. Patients with SARS-CoV-2 infection were frequently found to have renal dysfunction – the latter was associated with greater complications and in-hospital mortality.²⁵ A mortality rate of 3.6%, was reported in patients attending an outpatient haemodialysis centre.²⁶ Another study has concluded 3.07-fold (95%CI:1.43–6.61)mortality among renal failure patients.²⁷ We found, the pre-existing renal disease to be a cause of significant concern with 37.7% mortality [OR(95%CI): 1.996(1.082 – 3.681), p=0.027] with the eGFR at presentation being a significant predictor (OR=0.961, p <0.001) (Table-3).

The use of steroids in COVID-19 continues to be explored.The RECOVERY trial in UK, after evaluation at 28 days, concluded that dexamethasone reduced deaths by one-third in ventilated patients [age-adjusted rate ratio (RR) 0.65; 95% CI: 0.48–0.88; p=0.0003], and by one-fifth in other patients receiving supplemental oxygen with or without non-invasive ventilation (RR 0.80; 95%CI: 0.67 to 0.96; p=0.0021), although no benefit was observed in mild or moderate cases not requiring oxygen support (17.0% vs.13.2%; RR 1.22; 95% CI, 0.93e1.61; p¼0.14). In contrast, a systematic review concluded that the results from retrospective studies are heterogeneous, and it was difficult to assign a definite protective role of corticosteroids in this setting.²⁸ We found long-term oral steroids use to be a significant predictor of mortality – 52.9% [OR(95%CI): 3.412(1.261–9.23), p=0.016] – this was 341.2% of the odds of death for the patients without any long-term oral steroids use (Table-3). The sample size of this cohort was relatively small with 9 deaths out of 17 patients. However, based on our results, it may be safe to suggest that further population-based studies would be required to determine the impact of long-term oral corticosteroid use in COVID-19.

A major proportion of endocrine disorders are of autoimmune aetiology. The impact of thyroid disorders on COVID-19 is yet to be studied widely.^15,16 We found no increased risk of mortality [OR (95%CI): 0.914 (0.285–2.934), p=0.880] in patients with thyroid disorders. However, 33.33% [OR(95%CI): 1.510(0.791–2.883), p=0.212] mortality was seen among the diabetic patients on pharmacotherapy in our study [Table-4(4b)].

Pre-existing hypertension is an accepted risk factor for COVID-19 mortality.^26,27 However, the role RAAS-inhibitors and upregulation of ACE-2 receptors in COVID-19 mortality call for targeted clinical research for further clarification.²⁹ A meta-analysis of four studies showed that patients treated with RAAS-inhibitors had a lower risk of mortality [RR: 0.65(95%CI:0.45–0.94), P=0.20].³⁰ We did not observe any significant mortality-risk difference between RAAS-inhibitors treatment group [OR(95%CI): 0.760(0.365–1.586), p=0.465] and non RAAS-inhibitor treatment groups [OR(95%CI): 0.704(0.253–1.964), p=0.503] [Table-4(4b)]. We recommend the continuation of RAAS-inhibitors during COVID-19 unless there exist other compelling medical reasons for their discontinuation.

A prospective study in the UK concluded that the mortality from COVID-19 in cancer patients appeared to be driven principally by age, gender, and co-morbidities.¹³ The study could not identify evidence suggesting cancer patients on cytotoxic chemotherapy, or other anticancer treatment, were at an increased risk of mortality from COVID-19 compared to the general population.¹³ We also did not detect any increased risk of mortality in patients with active malignancy [OR(95%CI): 0.078(0.008–0.725), p=0.025)] (Table-5).

The impact of various non-specific immunomodulators in COVID-19 outcome remains inconclusive.¹⁴ Our study did not reveal any significant predictive mortality-risk with the use of long-term immunomodulators (methotrexate, tacrolimus, sirolimus, mycophenolate, dapsone, sulfasalazine and azathioprine) on multiple logistic regression analysis. We reached the same conclusion with patients suffering from chronic rheumatic disorders on similar analysis (Table-5).

Our study had some unique characteristics. We analysed all the eligible samples over a consecutive 76-day period at the initial peak of the pandemic. The study was conducted across two district general hospitals, allowing an insight into two differently located rural populations. We conducted univariate and multiple logistic regression analysis of the demographic variables and co-morbidities to examine the predictive-risk of contributing factors in COVID-19 mortality. The association between CCI scores and in-hospital mortality was also analysed in detail. We included demographic characteristics such as age, sex and residence in a long-term care-home while factoring in the associations.

Our study was not without limitations, though. We were unable to study the predictive-risk of obesity, socioeconomic status and ethnicity due to inadequate data. The “White British” group consisted of 80.61% of admitted patients, and no ethnicity was documented in 17.11% of our patients (Table-6, Figure-4).

Table-6: Medical admissions and mortality stratified by ethnicity

N: Number of patients

Figure-4: Bar charts showing Admission and Mortality stratified by Ethnicity

We relied solely on electronic database and hospital records to conduct the study retrospectively. The few subsets of patients such as those on prescribed long-term oral steroids, immunomodulators, thyroid disorders, chronic liver disease, and active malignancy had relatively small sample sizes with possible introduction of bias. We did not categorise diabetic patients into insulin dependent/non-insulin dependent or well/poorly glycaemic control cohorts. We did not aim to split the respiratory group into well or poorly controlled asthma or COPD subsets. Patients on a long-term steroid inhalation treatment were not included in the steroid cohort – a more extensive population-based study may be better suited for such an analysis.

CONCLUSIONS

Patients aged ≥ 60 years, residence in a long-term care-home, pre-existing renal disease, multiple co-morbidities (especially those with CCI ≥ 5), and patients on long-term oral steroids need to be considered as having a high risk of dying from COVID-19, along with other established risk factors such as hypertension, diabetes and chronic respiratory disease. RAAS-inhibitors need not be discontinued due to COVID-19. Further studies are necessary to establish links between long-term oral steroids use, chronic rheumatic disease, non-specific immunomodulators and COVID-19 mortality.

Introduction

Parkinson’s disease (PD) is the second most common neurodegenerative disease. It is associated with loss of dopamine leading to motor disorders ¹. However, non-motor symptoms such as anxiety, stress, and depression as well as cognitive impairment are also abundant among patients ². It has been hypothesized that non-motor symptoms can affect the quality of life in PD patients ³. The current therapeutic approach relies on dopamine substitution, which has no curative effect and does not improve non-motor symptoms. Studies have shown that meditation and other relaxation techniques can provide relief in non-motor symptoms. Mindfulness-based stress reduction (MBSR) is a technique used for improving stress-related symptoms in long-term conditions such as stroke, cancer, and PD ^4-6. It involves focused attention, open monitoring, and self-awareness of body movements in a non-judgemental state in the present moment. Studies have shown that mindfulness improves brain plasticity in some areas of interest. The areas of plasticity are involved in emotional regulation and processing ^{7, 8}. Thus, we hypothesized that mindfulness techniques could also have a positive effect on non-motor symptoms of PD patients which can enhance the quality of life after training sessions. This clinical trial aimed to investigate the impact of mindfulness training on the quality of life of PD patients.

Materials and Methods

Participants and Ethical issues

This randomized clinical trial was conducted at the neurology outpatient clinic of Imam Reza and Razi University-Hospital. Participants were 40 patients aged 67.95 ± 6.8 years (56-80) with a definite diagnosis of PD who were receiving dopaminergic drugs for at least one year. Twenty-seven of the patients were males, and 13 were females. They all were married, and 4 of them reported a family history of PD. Participants were randomly categorized into two experiment and control groups with 20 patients in each. For randomization, a list of random numbers was used based on the computer program and applied to the patients at the time of their neurologist visit at the clinic.

The inclusion criteria were: definite diagnosis of idiopathic PD based on UK Brain Bank criteria, mild and moderate forms of disease according to Hoehn and Yahr (HY) staging (1-3), stable and normal dosage of PD medications within last six months, normal cognitive function or mild cognitive impairment according to Mini-Mental State Examination (MMSE) score 17-30, enthusiasm and commitment to participate in mindfulness training sessions and to practice the required works at home.

The patients with the following criteria were excluded: focal neurologic deficit, abnormal brain imaging findings suggestive of brain lesions, other medical conditions that would affect the quality of life, use of antiepileptic drugs and symptoms of psychosis,

The protocol of the study was reviewed and confirmed by the local ethics committee of Tabriz University of Medical Sciences (IR.TBZMED.REC.1397.551). All patients received an informed written consent to participate in the study and to the use of their information. This trial was registered on the IRCT.ir website (IRCT20181007041258N1).

Mindfulness Training sessions

The interventions included 8-week mindfulness-based stress reduction (MBSR) sessions each for 2hours with a 15-minute break between the first and second hours. The sessions followed by a one-day retreat program between sixth and seventh sessions and took for 7 hours. The patients were asked to practice the requested homework at least for 30 minutes after each session. The protocol of the training sessions was conducted as per the steps described by Kabat-Zinn ⁹. The sessions were performed by a psychiatrist with over 5-year of experience in MBSR instructions. The instructions were based on the teaching of three techniques: body scanning, mindfulness meditation, and gentle yoga. The sessions focused on physical and mental awareness of body, how to diminish the physiological effect of pain and stress, how to perform less emotional reaction when facing distress, mental calmness in challenges through life, non-judgmental awareness, equity in stress management and joy of every moment.

Controls

The patients in the control group received eight 1-hour sessions during the same time as the experiment group. The sessions centered on basic information about PD based on brochures published by the American Parkinson Disease Association with topics: medications, symptoms of the disease, mood and sleep, and connecting with resources.

Assessments

All participant's general data, regarding age, gender, type of medication, and duration of disease were gathered according to patients' self-report and the information documented in patients' clinical records. Two neurologists assessed the HY stage, disease severity, and probable motor disturbance at baseline (within patient recruitment within one week before the initial session). The assessments of the quality of life were conducted at baseline (on the day of the first training session before the class), and after the experiment.

For the evaluation of the quality of life, the PDQ-39 questionnaire was used. PDQ-39 is a 39-item questionnaire based on the patient report of health status. It evaluates eight scales of daily activities including Mobility (MOB), Activities of daily living (ADL), Emotional well-being (EMO), Stigma (STI), Social support (SOC), Cognitions (COG), Communication (COM) and Bodily discomfort (BOD) and how these scales are being affected by PD. Participants are required to choose one of five orders of responses based on how often due to their disease, they have faced difficulties defined in each item. The final score of each item is calculated as a percentage score. The overall score is measured by calculating the mean percentage score of eight items as Parkinson`s disease summary Index(PDSI). The assessments were conducted in-person by the principal investigator (N.Gh) who was blinded about the group of study which patients were enrolled in.

Statistical Analysis

The scores of each item were described as mean ± SD. The between-group and within-group comparisons were made by the independent and sample T-test, respectively. The chi-square test was performed for the comparison of categorical variables. To investigate the change in the quality of life each PDQ-39 item scores and the PDSI scores were compared before and after the experiment in either control or experiment group by splitting the data into two study groups and comparing the mean scores of each item using independent T-test. All the analyses were performed using SPSS software version 19.0 (IBM Corp., Armonk, N.Y., USA). The boxplot figures were drawn using medCalc.ink software. Figures of the change in questionnaires' scores were provided by GraphPad.prism v.6.0.7 Ink software.

Results

All the 40 patients completed the training sessions in 8 weeks. The primary assessment was made after the last MBSR session and during their first neurologic visit at the clinic.

The general characteristics of the patients in each experiment and control group are shown in Table 1. The baseline characteristic data did not differ significantly between the two study groups.

As it is demonstrated in Table 2, at baseline, the PDQ-39 item scores did not differ significantly between two study groups, except for the SOC score, which was significantly higher in control subjects compared to the experiment group (35.80 ± 9.7 vs 29.11 ± 8.7, p = 0.02).

Quality of life assessment

The statistical analysis revealed a lower mean score in all PDQ-39 items in the experiment group compared to control subjects; however, the difference was insignificant for MOB, ADL, EMO, STI, COG, COM, and BOD and was only significant for SOC (34.13 ± 9.7 vs 26.19 ± 7.7 for control and experiment group, respectively. P = 0.007) (Table 2).

On the other hand, the within-group analysis yielded a significant improvement in the mean score of subjects in the experiment group. Their mean PDSI score was 31.88 ± 6.5 after one month compared to the baseline score of 33.93 ± 6.2 (p < 0.001). However, the mean scores of the participants in the control group did not significantly differ from the baseline.

A comparison of the delta values between the experimental and control groups showed MOB, ADL, and EMO to be significantly different.

The classification of the patients based on the stage of disease by HY revealed a significant improvement in PDSI score in patients in the experiment group at the severe stage (III). In contrast, the PDQ-39 item scores did not significantly differ (except for the ADL) after the training for mindfulness. The analysis also showed that patients in milder stages (I) have significant improvement after the experiment. However, the same improvement was noted in the control group (Table 3).

Table 1. Patients' demographic data in each study group

Note: Abbreviations: Female (F), Male (M), Hoehn Yahr (HY), Parkinson's disease (PD)

Table 2. PDQ-39 score items and PDSI before and after the mindfulness sessions in control and experiment group pf patients

Note: Abbreviations: Confidence Interval (CI), Mobility (MOB), Activities of daily living (ADL), Emotional well-being (EMO), Stigma (STI), Social support (SOC), Cognitions (COG), Communication (COM), Bodily discomfort (BOD), Parkinson`s disease summary Index (PDSI). ^ϯ: P value of the differences before and after the experiment in each group; ^*: P value of the differences between mean score of experiment and control group; ^€: 95% CI of the differences between mean score of experiment and control group.

Table 3. The quality of life in patients with different stages of PD before and after the mindfulness sessions in each experiment and control group

Note: Abbreviations: Confidence Interval (CI), Mobility (MOB), Activities of daily living (ADL), Emotional well-being (EMO), Stigma (STI), Social support (SOC), Cognitions (COG), Communication (COM), Bodily discomfort (BOD), Parkinson`s disease summary Index (PDSI)

Discussion

Significant improvement in the quality of life between the patients who received mindfulness training and the control group was observed in this clinical trial of people with Parkinson’s disease within eight weeks of trial.

Overall PDSI decreased modestly in the experiment group by 2.05 points and decreased in the control group by 0.27 points after the experiment.

Among the PDQ items, MOB, ALD, and EMO significantly improved in the experiment group compared to the control group. These results show that mindfulness training has a significant impact on not only motor symptoms of the disease but also the non-motor emotional wellbeing of the patients. The most significant effect of mindfulness training was on patients’ daily activity, which was also obvious in the severe cases of the disease.

Up to now, a few trials have been conducted on the effect of mindfulness training on PD ^10-13. The effect of mindfulness on different features of motor and non-motor symptoms has been measured. However, the outcome was discrepant regarding the time duration of the follow-up and improvement in the measured symptoms.

Similar to our findings, Geong son et al. found a significant difference in the quality of life and ADL of 33 experiment patients who received mindfulness training in comparison to 30 control subjects ¹³. Some other studies found mindfulness an effective modality for a few subscales of PDQ-39^{11, 12}.

In a clinical trial by Cash et al. 39 patients were enrolled in 8-week mindfulness sessions and their EMO and COG improved after the experiment¹¹. In a similar study conducted by Advocat et al., the effect of mindfulness training on the quality of life in 35 PD patients was compared with 37 control subjects within seven weeks and six months. In a two-step analysis, ADL was the only improved factor in experiment group ¹⁴.

In contrast, Dissanayaka et al. examined the effect of mindfulness on fourteen PD patients in the 8-week training program and compared the results with baseline at post-intervention assessment and 6-month follow up ¹⁵. Their results did not yield a significant improvement in any subscales of the quality of life in primary and secondary evaluation. Similarly, non-significant results were reported by Rodgers et al. and Pickut et al. ¹².

Birtwell et al. also assessed the long-term efficacy (16 weeks) of mindfulness training on STI and EMO in thirteen individuals with PD. They found an insignificant change in these two subscales of PDQ-39 ¹⁶.

In the present study, EMO and ADL were more subjective to the short-term effect of mindfulness training. The results of Rodger’s et al. study were consistent with our primary outcome. Their between-group analysis revealed a significant difference in depression subscale of DASS-21 after mindfulness intervention in PD patients ¹⁷. Cash et al. also found depression to improve after mindfulness interventions in PD patients ¹¹.

In contrary to our findings, the difference between PD experiment and control subjects was not meaningful in Pickut et al.'s study ¹². COG was unaffected to mindfulness training in our study. This finding was supported by the clinical trial of Cash et al. They found an insignificant change in PD patients' cognitive function in immediate pots-intervention assessment ¹¹.

On the other hand, Dissanayaka et al. found post-interventional improvement in PD patients' cognition by obtaining PD Cognitive Rating Scale (PDCRS), extended for six months ¹⁵.

Similarly, Geong son et al. showed a significant difference between experiments who received mindfulness training with controls in the mean score of Korean Montreal Cognitive Assessment ¹³.

As described above, there are discrepant results regarding the role of mindfulness stress reduction sessions on quality of life in PD patients. Our results were consistent with some studies and contrary to others. The main factors that might have affected these differences are the size of the sample, including a control group in the study, subjective mood changes in the patients, the severity of the disease, and the likelihood of practicing the Learned lessons at home.

Mindfulness-based interventions aim to improve the current wellbeing of the individuals by self-awareness of present emotions and body movements. It might also help individuals to manage daily stress, have a better judgment of their own, and adjust to daily life. There is also evidence suggesting that mindfulness training leads to neuroplasticity in the brain areas which are involved in emotions ¹⁸.

Studies have also suggested that early therapeutic interventions are more practical in terms of diminishing the probable severity of the disease in the future ^{13, 18}. In our study, patients in the early stages had improvement in their overall quality of life, which was also noted in controls of the same stage, too. However, a meaningful change in the quality of life of patients at the severe stage of PD was recorded after the training sessions. We suggest a long term follow up of the patients in each group and with different stages of the disease to find if the mindfulness training would help in diminishing the progress of the disease.

This study was a pilot study in which MBSR showed a great impact in improving the quality of life in PD patients. However, there were limitations in the study that must be considered. First, the sample size of the study was not large comparing to the prevalence of the disease, and it was constrained by other important factors such as disease severity and level of education. Patients needed to have a minimum level of education to be able to attend the sessions and apply them in their routine life. Second, the psychological nature of the intervention limited the blindness of the patients to the intervention.

We did not perform an intention to treat analysis or crossover randomization as all the randomly selected patients completed the trial, and none dropped out of the clinical trial.

Conclusion

In our study, mindfulness training improved the overall quality of life in PD patients. However, long-term follow up on a large-scale population is required to evaluate the impact of mindfulness-based stress reduction on each item.

Introduction:

According to DSM V, delirium is defined as disturbance in attention (i.e., reduced ability to direct, focus, sustain, and shift attention) and awareness (reduced orientation to the environment). This disturbance develops over a short period of time and it represents an acute change from baseline attention and awareness, and tends to fluctuate in severity during the course of a day.

The focus of the researchers has shifted from treatment to prevention of the syndrome. There is a need to study risk factors for prevention of delirium¹. Data on delirium in the intensive care unit is scarce in the Indian subcontinent².

A multicenter study indicated risk factors significantly contributing to delirium were related to patient characteristics (smoking, daily use of more than 3 units of alcohol, living alone at home), chronic pathology (pre-existing cognitive impairment), acute illness (use of drains, tubes, catheters, use of psychoactive medication, a preceding period of sedation, coma, mechanical ventilation) and the environment (isolation, absence of visit, absence of visible daylight, transfer from another ward, use of physical restraints)¹. Psychoactive medications can provoke a delirious state. Lorazepam has an independent and dose related temporal association with delirium³.

Each additional day spent in delirium is associated with 20% increased risk of prolonged hospitalisation and 10% increased risk of death⁴.

Hence, the present study was done to assess risk factors and precipitating factors of delirium in a medical intensive care unit of a tertiary care hospital.

Materials and methods:

This is an observational study done over a period of 1 year in a tertiary care medical college hospital located in southern part of India. Ethical committee approval for the study was obtained from the institutional ethical committee.

All patients admitted to medical intensive care unit in our tertiary care hospital, were screened for presence of delirium during the first 72 hours of admission using Richmond Agitation Sedation Scale (RASS) and Confusion Assessment Method for ICU (CAM-ICU). Patients with delirium were classified as delirious and the remaining as non-delirious patients. Comatose patients (RASS score -4 or -5) were excluded from the study.

Patients were initially screened with Richmond Agitation Sedation Scale (RASS). It is a 10-point scale, with 4 levels of agitation (+1 to +4) and 5 levels of sedation (-1 to -5). Level zero indicates calm and alert patient. Patients with RASS score of -4 or -5 (deep sedation and unarousable patients) were excluded from the study. Patients with RASS score of +4 to -3 were then screened for presence of delirium using Confusion Assessment Method for ICU (CAM-ICU). CAM–ICU has 4 criteria:

1) Acute onset and fluctuating course of delirium

2) Inattention

3) Disorganized thinking

4) Altered level of consciousness

The diagnosis of delirium requires the presence of criteria 1 and 2 and of either criterion 3 or 4.

Risk factors for developing delirium were assessed in the study population. Risk factors are those proven factors which may also be present before patient’s admission to intensive care unit, and which predispose the patient to develop delirium. Risk factors were compared between delirious and non-delirious patients. Risk factors which were assessed were history of diabetes and hypertension, history of previous stroke, history of previous cognition impairment, history of previous psychiatric illness, history of previous trauma, history of previous episodes of delirium, history of bowel and bladder disturbances prior to admission (such as constipation and urinary retention respectively), history of alcohol abuse (consumption of more than 2 units of alcohol), history of smoking (more than 10 cigarettes per day), history of consumption of substances other than cigarettes and alcohol (such as cannabis, cocaine etc.), history of uncorrected visual or hearing disturbances before admission, history of usage of barbiturates (such as phenobarbital), benzodiazepines (such as alprazolam, chlordiazepoxide, clobazam, clonazepam) & opioids (such as morphine) before admission, history of usage of sedatives (such as haloperidol, midazolam, fentanyl) and pain killers (such as morphine, tramadol) at the time of admission. Metabolic risk factors which were compared between delirious and non-delirious subjects were uraemia, hyponatremia, hyperbilirubinemia, metabolic and respiratory acidosis.

Precipitating factors weredefined as factors that were the likely causes of delirium in delirious patients. Precipitating factors for delirium which were looked into were exposure to toxins (alcohol/drugs), deranged metabolic parameters, infections and central nervous system causes.

SPSS21 software was used to calculate statistics. Independent t-sample test and the Pearson Chi-square test were used to calculate differences between delirious and non-delirious subjects. Odds ratios (OR) was calculated for all factors using univariate binary logistic regression.

Results:

Total number of patients enrolled in the study was 1582, of which 406 were diagnosed with delirium. Percentage of patients developing delirium within first 72 hours of admission was 25.7%. Hypoactive delirium was present in 52% and hyperactive delirium in 48% of patients. Patients who experienced delirium (57.5 + 17 years) were older compared to their non-delirious (53.3 + 18.1 years) counterparts (p value <0.0001). Among delirious subjects, majority were in the age group of 61-70 years (Figure 1).

Figure 1- Age distribution among delirious patients

38.2% of delirious patients and 39.3% of non-delirious patients were females. 61.8% of delirious patients and 60.7% of non-delirious patients were males.

Alcohol consumption [OR = 6.54 (95% CI 3.76-11.4, p = 0.0001)], previous psychiatric illness [OR = 3.73 (95% CI 1.712-8.159, p = 0.033)], previous cognition impairment [OR = 2.739 (95% CI 1.509-4.972, p = 0.001)], sedatives usage at the time of admission [OR = 2.488 (95% CI; 1.452-4.264), p = 0.001)], visual disturbances [OR = 2.227 (95% CI; 1.328-3.733, p = 0.002)], bowel and bladder disturbances [OR = 1.677 (95% CI 1.044-2.693, p = 0.032)] were significant risk factors contributing to delirium after univariate analysis (Table 1). Metabolic acidosis [OR = 1.996 (95% CI 1.469-2.711, p = 0.0001)] and hyperbilirubinemia [OR = 1.448 (95% CI 1.111-1.886, p = 0.006)] were significant metabolic parameters contributing to delirium after univariate analysis (Table 2).

Precipitating factors (Table 3) for delirium are those factors that were considered the most likely causes of delirium among the delirious patients. Precipitating factors for delirium were classified into toxins, deranged metabolic parameters, infections and central nervous system causes, of which metabolic parameters were most common. Among metabolic parameters, uraemia (25.1%), hepatic encephalopathy (22.7%) and hyponatremia (19.5%) contributed to the majority of cases with delirium.

Table 1 – Univariate analysis of risk factors of delirium

Table 2- Univariate analysis of metabolic parameters

Table 3- Precipitating factors of delirium in the present study

Discussion:

Delirium is classified into hyperactive, hypoactive and mixed type. Hyperactive subtype is present if there is definite evidence in the previous 24 hours of at least two out of the following factors - increased quantity of motor activity, loss of control activity, restlessness, wandering. Hypoactive subtype is present if there is definite evidence in the previous 24 hours of at least two of the following factors - decreased amount of activity, decreased speed of actions, reduced awareness of surroundings, decreased amount of speech, decreased speed of speech, listlessness, reduced alertness, withdrawal. Mixed subtype is present if there is evidence of both hyperactive and hypoactive subtypes in the previous 24 hours⁵. Percentage of patients with hypoactive delirium was high in this study (52%). Hypoactive delirium often carries relatively poor prognosis, occurs more commonly in elderly patients and is frequently overlooked or misdiagnosed as having depression or a form of dementia.

In the present study, delirium was more prevalent in the elderly population. Most of the elderly patients will have multiple risk factors making them more vulnerable to delirium. Delirium is often the only sign of an underlying serious medical illness in an elderly patient and particular attention should be given to identify and correct the underlying illness.

History of alcohol consumption of more than 2 units per day, prior to admission of the patient, was the major risk factor contributing to delirium in this study (OR = 6.54). This was similar to other studies done by Bart¹ et al & Ouimet⁶ et al where consumption of more than 3 units of alcohol (OR 3.23) & 2 units of alcohol (OR 2.03) respectively, was a significant risk factor for delirium. Patients with a previous psychiatric illness were at increased risk for delirium in this study (OR – 3.73). However, other studies explaining its importance in contributing to delirium were not available. Previous cognition impairment was a significant risk factor contributing to delirium (OR = 2.73). The study by Bart¹ et al found that previously diagnosed dementia was an important risk factor (OR = 2.41). Positive correlation with dementia was reported by McNicoll et al⁷ (RR 1.4) and Pisani et al⁸ (OR 6.3). Usage of sedatives (OR = 2.48) at the time of admission was a significant risk factor for developing delirium. Bart¹ et al found that use of psychoactive medication may disturb the neurotransmission in the brain provoking a delirious state and use of benzodiazepines is a risk factor for delirium (OR – 3.34). Pandharipande³ et al found that Lorazepam was an independent risk factor for daily transition to delirium (OR – 1.2). Pisani⁸ et al found that use of benzodiazepines was a significant risk factor for developing delirium with odds ratio of 3.4. Uncorrected visual disturbances were a significant risk factor for developing delirium in this study (OR-2.22). Inouye⁹ et al found that vision impairment (adjusted relative risk – 3.5) was an independent baseline risk factor for delirium. Bowel and bladder disturbances were a significant risk factor contributing to delirium in this study (OR – 1.67). Morley¹⁰ opined that constipation is a frequent, often overlooked precipitating factor for delirium. Tony¹¹ et al was of the opinion that a careful history and physical, including a rectal examination with consideration of disimpaction, may be helpful in assessing and managing delirious patients. Waardenburg¹² concluded that significant urinary retention can precipitate or exacerbate delirium, a disorder referred to as cystocerebral syndrome. Liem and Carter¹³ suggested that increased sympathetic tone and catecholamine surge triggered by the tension on the bladder wall may contribute to delirium. Metabolic acidosis and hyperbilirubinemia were significant metabolic parameters contributing to delirium in this study.Similar findings were reported by Aldemir¹⁴ et al.

Among delirious patients, most common precipitating factors for delirium in this study were uraemia (25.1%), hepatic encephalopathy (22.7%) and hyponatremia (19.5%). Alterations of serum electrolytes, renal function predispose to delirium¹⁵. Hyponatremia causes delirium and the mechanism is not well understood^{16, 17}. Blood urea nitrogen/creatinine ratio greater than 18 is an independent risk factor for delirium in general medical patients⁹.Hepatic failure leads to hyperammonemia, which leads to excessive NMDA (N-methyl-D-aspartate) receptor activation, resulting in dysfunction of glutamate-nitric oxide-cGMP pathway and causing impaired cognitive function in hepatic encephalopathy¹⁸.Excess activation of NMDA receptors results in neuronal degeneration and death¹⁹. In hepatic failure, there may be a shift in regional cerebral blood flow and cerebral metabolic rates from cortex to subcortex resulting in delirium²⁰.

Patients who develop delirium during their stay in hospital have higher 6-month mortality rates, longer hospital stay, increased economic burden and a higher incidence of cognitive impairment at hospital discharge²¹. Limitation of this study was long term follow up of patients who developed delirium was not done.

Conclusion:

Delirium is common in intensive care unit patients and hypoactive delirium is more common. Major risk factor contributing to delirium was alcohol consumption before admission. Most common precipitating factors contributing to delirium were deranged metabolic parameters.

Delirium in ICU patients especially hypoactive delirium is easily missed. Hence, all ICUs should implement both RASS and CAM-ICU for early detection of delirium. Future research needs to be directed at development of scoring systems for detection of delirium, which are easy to use and are accurate.

Introduction

Bipolar affective disorder (BPAD) is one of the commonest psychiatric disorders with a lifetime prevalence of about 3% in the general population and is the sixth leading cause of disability worldwide (1,2).This disorder is characterised by repeated episodes in which the patient’s mood and activity levels are significantly disturbed. This disturbance consists on some occasions of an elevation of mood and increased energy and activity (mania or hypomania), and on other occasions of a lowering of mood and decreased energy and activity (depression) (3). As the illness starts early in life, i.e., during teens or early adulthood, persons suffering from BPAD have symptoms of illness for the major part of their life (4, 5).

In India, since professional services, both in public and private sectors are not adequately developed due to shortage of trained human resources and infrastructure, the family support system plays a major role in caring for people with mental illnesses (6). The primary caregiver is identified as an adult relative (a spouse, parent or spouse equivalent) living with a patient, who is involved in the care of the patient on a day-to-day basis, takes the responsibility for bringing the patient to the treatment facility, stays with the patient during the inpatient stay, provides financial support and/or is contacted by the treatment staff in case of emergency (7). Intensive involvement in the care of the patient is often associated with significant caregiver burden.

Caregiver burden can be defined as the presence of problems, difficulties or adverse effects which affect the lives of caregivers of patients with various disorders or illnesses, e.g. members of the household or family (8). Family burden is broadly divided into objective and sub­jective burden. While the notion of the objective family burden relates to measurable problems (e.g. patients’ troublesome behaviours), the idea of subjective family burden is bound to caregivers’ emotions arising in response to the objective difficulties (9). Multiple studies across the world have shown that bipolar disorder is associated with significant caregiver burden (10-31). In view of the high caregiver burden, it is now suggested that the emphasis in psychiatric rehabilitation needs to shift from a patient-focused approach to a combined patient and caregiver-focused approach. Although there are studies from different parts of the country, there is a lack of data on caregiver burden from Kashmir, which is often faced with turmoil, which can influence caregiver burden. The present study is an effort in this direction to assess caregiver burden and its correlates among primary caregivers of patients with bipolar disorder.

Methodology

The present study was conducted on primary caregivers of patients with BPAD. Primary caregivers were defined as those caregivers who were closely involved in the care of the patient during the acute episodes and during the maintenance period in terms of bringing the patient to the hospital, supervising the medications and liaison with the treating team.

The study sample comprised of 100 caregivers of 100 patients diagnosed with BPAD as per the International Classification of Diseases classification of mental and behavioural disorders, 10^th revision (ICD-10) (3), attending either the outpatient or inpatient services at the Department of Psychiatry, SKIMS, Bemina, Srinagar. The study was approved by the Ethics Committee of the institute and all the participants were recruited after obtaining written informed consent.

To be included in the study, the caregivers were required to be involved in the care of patients, aged 18 or above, living with the patient for at least 1 year and were a family member taking care of patients without any wages. Caregivers who were diagnosed with psychiatric illness and staying with the patient for less than 12 months were excluded.

The caregivers were assessed by following scales:

Family Burden Interview Schedule (FBIS) (32):This is a semi-structured interview schedule having 24 items, each of which is scored on a 3-point scale, i.e. 0 indicating no burden, 1 indicating a moderate level of burden and 2 suggesting severe burden. The items of the objective burden of the scale are divided into 6 domains, i.e. financial burden, disruption of routine family activities, disruption of family leisure, disruption of family interaction, physical health and mental health. Subjective burden is evaluated by a single item. This scale has been widely used in previous studies from India (26, 33-35).

DUKE-UNC Functional Social Support Questionnaire (FSSQ) (36):The Duke-UNC Functional Social Support Questionnaire (FSSQ) is an 8-item instrument to measure the strength of the person's social support network (36). Responses to each item were scored as 1 (‘much less than I would like’), 2 (‘less than I would like’), 3 (‘some, but would like more’), 4 (‘almost as much as I would like’) and 5 (‘as much as I would like’). The scores from all eight questions are summed (maximum 40) and then divided by 8 to get an average score. The higher score indicates better perceived social support. Cronbach’s alpha for this scale is 0.84.

Hindi General Health Questionnaire (GHQ-30) (37):The modified version of Goldberg's General Health Questionnaire (GHQ) (38) was used. This is a screening device for identifying minor psychiatric disorders in the general population and within the community or non-psychiatric clinical settings such as primary care or general medical outpatients. The self-administered questionnaire focuses on two major areas: the inability to carry out normal functions and the appearance of new and distressing phenomena. In each question of the 30-item GHQ, the caregivers were asked to choose among: Better than usual or same as usual = 0, less than usual or much less than usual = 1.The results were evaluated by the two-step assessment method (0-0-1-1-method). The minimum GHQ-30 total score was 0 and the maximum total score of GHQ-30 was 30. A cut-off of 6 was used to categorize those with and without psychiatric morbidity. Cronbach’s alpha value of the GHQ-30 was 0.93. The Kappa coefficient was 0.64 (p<0.001).

The recorded data was compiled and entered into a spreadsheet (Microsoft Excel) and then exported to data editor of SPSS Version 16.0 (SPSS Inc., Chicago, Illinois, USA). Continuous variables were summarised in the form of means and standard deviations and categorical variables were summarised as percentages. Student’s independent t-test and Chi-square tests were employed for comparing caregiver burden with different variables. 

Results

Table 1: Description of socio-demographic variables of caregivers

Table 2: Clinical profile of patients.

Table 3: Caregiver burden, social support and psychological morbidity among caregivers

Table 4: Association of caregiver burden with socio-demographic variables of caregivers

Table 5: Clinical Profile of patients with bipolar disorder

Table 6: Clinical Profile of patients with bipolar disorder

The study included nearly equal number of male and female patients. About two-thirds of the patients were married (63%). About one-third of the patients had not received any formal education and another third had completed their secondary education and one-fourth had completed graduation (Table 1).

Description of socio-demographic variables of caregivers

The study included nearly equal numbers of male and female caregivers. The majority (55%) of the caregivers were spouses of the patient. The majority of the caregivers were married (93%). Nearly half of the caregivers had not received any formal education (48%), were homemakers (44%) and three-fifths of them were from low socioeconomic status (60%). The majority of caregivers (77%) had been caring for duration of one to five years (Table 1).

Clinical profile of patients.

In the present study, the majority of patients (77%) had duration of illness in the range of 1-5 years, nearly half of them were never hospitalised, the majority (55%) of patients had one to two manic episodes, most of them (64%) had three to five episodes of depression, and the majority (75%) of them never attempted suicide or homicide. The majority of patients (73%) were compliant with medication. (Table 2)

Caregiver burden, social support and psychological morbidity among caregivers

As is evident from Table 3, the highest burden was reported in the financial domain, followed by disruption in family routine activities, disruption of family leisure, disruption of family interactions, effect on physical health of others and least burden was reported in the form of effect on mental health of others. The mean DUKE UNC FSSQ score was 3.17 (SD=0.84) with range 1.75-4.75.

Mean GHQ-30 score was 13.14(SD=5.65) with a range of 2-25. Of the 100 caregivers, about one-fourth (N=23) had a GHQ-30 score of 6 or more, indicative of psychological morbidity.

Association of caregiver burden with demographic and clinical variables

As is evident from Table 4, higher caregiver burden was associated with higher age, female gender, lack of formal education, being a homemaker, lower socioeconomic status, a nuclear family set-up, being spouse of the patient and longer duration of being in the caregiver role.

Clinical Profile of patients with bipolar disorder

In terms of clinical variables, higher objective caregiver burden was associated with duration of illness more than 10 years, higher number of hospitalisations and higher number of manic and depressive episodes. Caregivers of patients consuming >75% of the prescribed medications reported lower caregiver burden (Table 5).

Advancing age of patient and caregiver, increasing duration of care, prolonged illness, greater number of hospitalisations and higher number of episodes of either polarity were significantly associated with higher caregiver burden. In terms of association of social support and caregiver burden, higher social support was associated with significantly lower caregiver burden, whereas higher caregiver burden was associated with higher psychological morbidity (Table 6).

Discussion

Families play an important role in care of patients with chronic mental illnesses. In the process of caring for such patients, relatives face a considerable burden.

Findings of the present study suggest that higher burden was seen among the caregivers who were relatively older, of female gender, uneducated or illiterate, homemakers and from nuclear families. Compared to parents and siblings, spouses reported significantly higher levels of caregiver burden. Furthermore, the caregivers involved in the care of the patient for longer durations reported significantly higher levels of caregiver burden.

In terms of clinical variables of patients, higher caregiver burden was associated with longer duration of illness, higher number of lifetime hospitalisations, higher number of manic and depressive episodes and poor medication compliance. Poor social support was associated with a higher level of caregiver burden. Higher caregiver burden was associated with higher psychological morbidity.

Many previous studies from India have evaluated caregiver burden among caregivers of patients with bipolar disorder (10-32). There is a lack of consensus with respect to caregiver variables and their association with caregiver burden (39). Some of the studies suggest that there is no significant difference in the caregiver burden as reported by caregivers of either gender (6), whereas others suggest that females report higher caregiver burden (13, 40). Our findings support the studies which have reported higher caregiver burden among female caregivers. This finding of ours could have been influenced by the relationship of caregivers with patients. In the present study, spouses of patients formed a large proportion of caregivers and they reported significantly higher burden, in contrast to parents and siblings. Cultural issues like restriction of females to household activities with lower opportunities to vent out their distress, inability to spend time on leisure activities, financial dependency and lack of independence could also be responsible for higher perceived burden. It was noticed that caregivers from nuclear families had higher caregiver burden as compared to those from joint families. The joint family system is considered to promote interdependence and possibly is associated with sharing of caregiver burden and this may explain why caregivers from joint families reported lower caregiver burden. Similar findings have been reported in earlier studies from India (41).

Findings of the association of higher caregiver burden with duration of illness are supported by existing literature (14). This finding suggests that possibly with passing time, frequent relapses of illness lead to caregiver burnout, which leads to higher caregiver burden. Previous studies have also noted an association of higher caregiver burden with higher numbers of hospitalisation (30). Findings of the present study too support this association. Higher caregiver burden with greater numbers of hospitalisations possibly indicate more severe episodes and hospitalisation associated with more expenditures and loss of earnings. This suggests that all efforts must be made to pick up relapses at the earliest and manage them effectively to minimise the chances of progression to severe episodes and resultant need for inpatient care. Previous studies have also reported association between higher caregiver burden and higher number of episodes, especially manic episodes (14) and more severe manic episodes (42). Manic episodes of the illness are very disruptive to daily life, work and family relationships. Due to this, these episodes place great demands on family members involved in caregiving. These demands can persist even during remission, where residual symptoms are often still present and lead to caregiver burden. Available data from India suggest that in contrast to patients from the West, patients from India have a higher number of manic episodes (43). Taken together, this finding has important implications as this suggests that efforts must be made to prevent frequent relapses in patients with bipolar disorder, especially in the Indian context to reduce the caregiver burden (44).

In the present study, higher burden was also associated with a higher number of depressive episodes and this finding is supported by existing literature (16).

Long-term management of bipolar disorder requires continuation of medications with good compliance. Poor medication compliance has been shown to be associated with many negative patient-related outcomes like higher risk of relapses, suicidality, poor quality of life, higher residual or sub-syndromal symptoms etc (45, 46). The present study adds to this body of literature and suggests that poor medication compliance in patients is also associated with higher caregiver burden and this finding is supported by the existing literature (11).

Among the demographic variables of caregivers, higher age of caregivers was associated with higher caregiver burden. This finding is also supported by existing literature (6). This association possibly suggests that with increasing age, the caregivers possibly experience more burnout, lose hope and also lose physical vigour to take care of the mentally ill relative.

Accordingly, it is important for the mental health professionals to support the ageing caregivers.

To conclude, the present study suggests that BPAD is associated with higher caregiver burden. Higher caregiver burden is associated with clinical variables of the patients and demographic variables of the caregivers. Among the patient-related variables, longer duration of illness, those with a higher number of lifetime episodes of either polarity and poor medication adherence are associated with higher caregiver burden. Hence, all measures must be taken to minimise relapse in patients with BPAD. Among the demographic variables of caregivers, higher caregiver burden is reported by caregivers who were relatively older, of female gender, uneducated or illiterate, homemakers and from nuclear families.

Our findings highlight the need for additional research on interventions to reduce burden among caregivers of patients with bipolar affective disorder. For better outcomes of disease, more attention needs to be given to the primary caregivers in terms of psycho-education and counselling.

INTRODUCTION

Several studies found that refugees develop post-traumatic stress disorder (PTSD) after having endured war trauma¹, or certain circumstances related to migration like moving to a new country, being unemployed and poor housing². PTSD is described as distress and disability due to a traumatic event that occurred in the past³. In 2013, the American Psychiatric Association revised the PTSD diagnostic criteria in the fifth edition of its Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and PTSD was included in a new category, Trauma- and Stressor-Related Disorders⁴. All of the conditions included in this category required exposure to a traumatic or stressful event as a diagnostic criterion⁴. The person with PTSD often avoids trauma-related thoughts and emotions, and discussion of the traumatic event⁴. PTSD patients are invariably anxious about re-experiencing the same trauma. The trauma is usually re-lived by the person through disturbing, repeated recollections, flashbacks, and nightmares⁴. Symptoms of PTSD generally begin within the first 3 months after the provocative traumatic event, but may not begin until several years later⁴. A large number of children (10-40%), 16 or younger, who have experienced a traumatic event in their life, tend to develop PTSD later on⁵. Moreover, many families with children growing in war zones and then moving to safer places, experience trauma, stress and reduced functioning⁶. These families have different resilience rates in their survival mechanisms, coping strategies and adaptation levels⁷.

The latest war in Syria has led to the migration of large parts of the Syrian population to neighboring countries such as Lebanon, Jordan and Turkey⁸.The United Nations High Commissioner for Refugees (UNHCR) estimates that approximately 1.5 million refugees are located in Lebanon⁹. These refugees have been exposed to several types of traumatic events that may increase the incidence of mental health problems¹⁰.

We hypothesize that the proportion of positive PTSD screens would be high among Syrian refugees with the presence of some specific related risk factors. Thus, the objective of our study was to examine PTSD symptoms and to determine the associated risk factors in a sample of Syrian refugees living in North Lebanon.

METHODS

1. Study design and population

This was a cross-sectional study that aimed to assess the proportion of Syrian refugees in North Lebanon who were at high risk of developing PTSD, and to examine the association of PTSD high risk with other factors. The survey was carried out during February and March 2016. A convenient sample of Syrian refugees of both gender, aged between 14 and 45 years, living in North Lebanon, was selected out of a population of 262,151¹¹.

The estimated minimum sample size, calculated using Raosoft sample size calculator, with a margin of error of 5% and a confidence interval of 95%, was 384 refugees. A total number of 450 Syrian refugees, residing in individual tented settlements (ITSs), collective shelters (CS) or Primary Health Care Centers (PHCs) located in North Lebanon, was selected according to inclusion and exclusion set criteria.

The inclusion criteria were: Syrian refugees, aged (14-45 years), physically and mentally independent. Hence, all subjects that were younger than 14 or older than 45, speechless, deaf, physically and mentally dependent, or have undergone recent moderate or severe surgery (less than one week earlier), were excluded from the study.

2. Ethical considerations

The study protocol received approval from the Notre Dame University (NDU) Institutional Review Board (IRB). The approval comprised details about the procedure of the study and the rights of the participants. Informed consent was obtained from each participant. The questionnaires were answered anonymously, ensuring confidentiality of collected data.

3. The Interview questionnaire

The interview questionnaire was divided into six sections consisting of a total of 46 questions. The questions were dichotomous, close-ended and open-ended. A cover page described the purpose of the study, ensuring the anonymity and confidentiality, and soliciting the consent of participants. The questionnaire collected data on the demographic and socio-economic characteristics of the participants. Information about health status and stressful life events (SLE) were also obtained. The PC-PTSD (Primary Care Post-Traumatic Stress Disorder) tool was used to screen PTSD.

For the purposes of the study, subjects were classified as having/not having positive PC-PTSD. The results were used to calculate the proportion of Syrian refugees who are at high risk of developing PTSD.

PC-PTSD questionnaire: The PC-PTSD was initially developed in a Veteran Affairs primary care setting and is currently used to screen for PTSD, based on the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-4) diagnostic criteria¹². The screen consisted of 4 questions related to a traumatic life event: In the past month you (1) Have had nightmares about it or thought about it when you did not want to?; (2) Tried hard not to think about it or went out of your way to avoid situations that reminded you of it?; (3) Were constantly on guard, watchful, or easily startled?; (4) Felt numb or detached from others, activities, or your surroundings? The answers to these questions were dichotomous (Yes/No) and the total screen was considered "positive" when a patient answered "yes" to three out of four questions. PC-PTSD showed a high sensitivity (86%) and moderate specificity (57%) when using a cutoff score of 2¹³.

In order to validate the Arabic version of the PC-PTSD questionnaire, it was translated into Arabic and translated back into English. The original version of the Arabic questionnaire was pilot-tested on 10 Syrian refugees to ensure the validity of the answers, and to guarantee its reliability.

Anthropometric measurements: The main anthropometric measurements were weight and height. Participants were dressed in light clothes and barefooted, and standing height was measured to the nearest 0.1 cm using a stadiometer. Body weight was measured to the nearest 100g using an electronic scale. Body Mass Index (BMI) is a measure of weight adjusted to height (kg/m²), calculated by dividing weight (in kilograms) by the square of height (in metres). For the purposes of the study, BMI was recoded into four categories: underweight, normal, overweight and obese.

4. Data entry and statistical analysis

The Statistical Package for Social Science (SPSS) for Windows (version 22) was used for data entry and analysis.

First, bivariate analyses of categorical variables were performed using the Fisher exact tests, Chi-squared tests and Student’s t-test. The dependent variable was the high risk of PTSD, using the PC-PTSD tool. Thus, the PC-PTSD score was considered the dependent variable: a dichotomous variable of PC-PTSD (-) and PC-PTSD (+), and all variables that might be a risk factor or might lead to PTSD were set as the independent variables. Two main independent variables were: age and gender. Other variables included: marital status, place of residence, number of people and families living in the same household (crowding index), income, education status, profession, work status, lifestyle habits, medical or psychological problems, medication taken and SLE. Frequencies and percentages were calculated for qualitative variables, and mean and standard deviations for quantitative variables (BMI, crowding index). A p-value of 0.05 or less was considered to be statistically significant.

RESULTS

Table 1: Socio-demographic characteristics of the 450 Syrian refugees

Table 2: Health characteristics and migration factors of the 450 Syrian refugees

Table 3: Socio-demographic characteristics associated with positive screen for PTSD among the 450 Syrian refugees (bivariate analyses)

*Significant with p-value < 0.05

Table 4: Health characteristics and migration factors associated with positive screen for PTSD among the 450 Syrian refugees (bivariate analyses)

* Significant with p-value < 0.05

All the socio-demographic, health and migration characteristics of our sample of Syrian refugees were described in Tables 1 and 2. Out of the 450 participants, 47.3% had positive PC-PTSD. In order to study the association between the socio-demographic characteristics among the Syrian refugeesand PTSD screening, abivariate association was explored as shown in Table 3. The results indicate a significant difference between gender groups, as almost half of the women (49.9%) had a positive screen for PTSD compared to 33.3% of the men (p=0.011). Mean age was significantly higher in refugees with positive PC-PTSD (28.9 ± 7.6 years) versus those with negative PC-PTSD (26.9 ± 8.5 years) (p=0.009). PTSD screening was shown to be significantly associated with marital status. In fact, positive PC-PTSD was mostly perceived in divorced participants (80%) compared to 69.2% of widowed, 50.5% of married, and 16.7% of single subjects (p=0.000). Yet, crowding index, current place of residence, educational level, employment status, and income were not significantly associated with positive PC-PTSD (p>0.05).

The association of health characteristics and migration factors among the Syrian refugees with PTSD screening was displayed in Table 4. A significant association was observed between the presence of medical condition and positive screen for PTSD, as 61.9% of subjects suffering from a medical condition had a positive PC-PTSD, compared to 42.4% of participants without medical conditions (p=0.000). However, BMI and tobacco consumption were not significantly associated with PTSD screening (p>0.05). PTSD screening was significantly associated with the presence of psychological disorders. Thus, 90.9% of refugees who sought professional help for psychological disorders had positive PC-PTSD, versus 46.2% of those who did not (p=0.003). Positive PC-PTSD was significantly associated with the increase in the number of SLE. In fact, none of the participants without any stressful event had a positive PC-PTSD, compared to 36.5% of participants with 1-2 SLE, 58.7% of participants with 3-4 SLE and 75% of participants with 5-6 SLE (p=0.000). On the other hand, no significant association was observed between PC-PTSD and migration status (p>0.05).

DISCUSSION AND CONCLUSION

PTSD represents the most frequently occurring mental disorder occurring among refugees¹⁴. PTSD prevalence rates ranging between 15% and 80% have been reported in refugees. A study of Cambodian refugees living in the Thailand-Cambodia border camp indicated that 15% had PTSD¹⁵. A cohort study aimed to show the prevalence of PTSD among Iranian, Afghani and Somalian refugees that have moved to the Netherlands at a 7-year interval [(T1=2003) - (T2=2010)]. Results displayed a high prevalence at both T1 (16.3%) and T2 (15.2%). The reason for this high unchanged prevalence may be due to the late onset of the PTSD symptoms, and the low use of mental health care centers¹⁶. De Jong and colleagues reported that 50% of the refugees in Rwandan and Burundese camps had serious mental health problems, mainly PTSD¹⁷. While Teodorescu and colleagues aimed to illustrate the prevalence of PTSD among refugees in Norway; results showed that 80% of the refugees had PTSD¹⁸. In our study, the high proportion of positive screen for PTSD among Syrian refugees was estimated at 47.3%. In 2006, a mental health assessment demonstrated that Lebanese citizens exposed to war were more likely to develop psychiatric problems such as PTSD¹⁹. Subsequently, a cross-sectional study was done in South Lebanon on 681 citizens in 2007 (1-year after the 2006 war in Lebanon). The aim of the study was to examine the prevalence of PTSD 12 months after 2006 war cessation. Results showed that the prevalence of PTSD was 17.8%¹⁹. A recent cross sectional study, aimed to show the prevalence of PTSD and explore its relationship with various variables. The study included 352 Syrian refugees settled in camps in Turkey. An experienced psychiatrist evaluated the participants, and results demonstrated that 33.5% of study participants had PTSD, mainly female refugees, people who experienced 2 or more SLE, or those who had a family history of psychiatric disorder²⁰.

PTSD has been associated with a wide range of traumatic events: emotional or physical abuse²¹, sexual abuse²², parental break-up²³, death of a loved one²⁴, domestic violence²⁵, kidnapping²⁶, military services²⁷, war trauma²⁸, natural disasters²⁹ and medical conditions including cancer³⁰, heart attack³¹, stroke³², intensive-care unit hospitalization³³, and miscarriage³⁴.

Our findings should be interpreted taking into account several limitations. The first limitation is the use of screening tools, instead of the more accurate diagnosis of the clinician, in order to detect PTSD. Given that a standardized screening tool for PTSD was used, our rates are likely an overestimate of the true prevalence rates. Secondly, this study was conducted with a limited sample of Syrian refugees and therefore should not be generalized to all refugees of other eras or from other countries. The third limitation is represented by the lack of information on the presence of other Axis I psychiatric comorbidities such as anxiety or mood disorder that could facilitate the development of PTSD or influence its manifestations^35-36.

Refugees are an important group to examine, given the high prevalence of mental health disorders. Although refugees are evaluated for health problems, currently there are no standardized screening and clinical practice guidelines for assessing PTSD in all refugees. Therefore, we may be missing opportunities to detect and treat these harmful and potentially fatal conditions. Our findings suggest the need to consider a standardized screening tool for PTSD in this population. In addition, a far greater percentage of patients may have “PTSD symptoms,” that are abnormal but do not meet full criteria of the DSM5 for PTSD diagnosis, but still cause functional impairment and may later develop into a diagnosable PTSD. Given the overall high prevalence, one possible model for evaluation would be a stepped screening approach: Positive screens for PTSD could trigger a standardized clinical diagnosis for PTSD with more comprehensive assessment and early intervention. Considering the high cost of treating individuals with PTSD, screening and intervention strategies should be addressed. Greater awareness among providers and increased targeted assessment and treatment efforts may increase early detection of a wide range of PTSD, preventing more serious future health problems and functional impairment among refugees.

Introduction

Endocrine disorders are frequently accompanied by psychological disturbances. Conversely, psychiatric disorders, to significant extent demonstrate consistent pattern of endocrine dysfunctions. [1] Endocrinopathies manifests as myriad of psychiatric symptoms, as hormones affect a variety of organ systems function. The presence of psychiatric symptoms in patients with primary endocrine disorders provides a new insight for exploring link between hormones and affective function.[2] Disturbance of hypothalamic-pituitary-thyroidal axis is of considerable interest in psychiatry and is known to be associated with a number of psychiatric abnormalities.[3]Thus, the main focus of psychoneuroendocrinology is on identifying changes in basal levels of pituitary and end-organ hormones in patients with psychiatric disorders. Psychiatric symptoms may be the first manifestations of endocrine disease, but often are not recognized as such. Patient may experience a worsening of the psychiatric condition and an emergence of physical symptoms with the progression of the disorder.[4] Psychiatric manifestations of endocrine dysfunction include mood disturbances, anxiety, cognitive dysfunction, dementia, delirium, and psychosis. While dealing with treatment-resistant psychiatric disorder, endocrinopathies should also be considered as a possible cause in management. Psychotropics medicine may worsen the psychiatric symptoms and improves only once the underlying endocrine disturbance is corrected. [5]The lifetime prevalence of depression and anxiety is 11.8% to 36.8% and 5.0% to 41.2% respectively in the group with previously known thyroid disorder. [6,7].The occurrence of major depression in DM is mostly estimated around 12% (ranging from 8-18%). 15-35 % of individuals with DM report milder types depression. [8]. Depressive symptom is seen in almost half of patients with Cushing's syndrome and these experience moderate to severe symptoms. Some patients with Cushing's syndrome also experience psychotic symptoms [9]. Patients suffering from Addison's disease may be misdiagnosed with major depressive disorder, personality disorder, dementia, or somatoform disorders [4, 10]. Women with hyperandrogenic syndromes are at an increased risk for mood disorders, and the rate of depression among women with PCOS has been reported to be as high as 50 percent. Central 5-HT, system dysregulation that causes depression might simultaneously affect peripheral insulin sensitivity, or vice versa, possibly via behavioral or neuroendocrinological pathways, or both. [10]

Hollinrake 2007 showed prevalence of depression has shown it to be four times that of women without PCOS. Hollinrake screened patients with PCOS for depression and found total prevalence of depressive disorders which included women diagnosed with depression before the study, was 35% in the PCOS group[11]. No specific psychiatric symptoms have been consistently associated with acromegaly or gigantism or with elevated GH levels. Adjustment disorder may occur from changes in physical appearance and from living with a chronic illness [11]. Sheehan’s syndrome (SS) refers to the occurrence of varying degree of hypopituitarism after parturition (1). It is a rare cause of hypopituitarism in developed countries owing to advances in obstetric care and its frequency is decreasing worldwide. Reports of psychoses in patients with Sheehan’s syndrome are rare. [13] Psychiatric disturbances are commonly observed during the course of endocrine disorders .The underlying cause can be hyper- or hyposecretion of hormones, secondary to the pathogenic mechanisms. medical or surgical treatment of endocrine diseases, or due to genetic aberrations[14]. Psychiatric disorders frequently mimic the symptoms of endocrinological disorders. In view of sizable number of patients seeking treatment from our department present with comorbid endocrinolgical disorders, we planned the present study to investigate psychiatric morbidity preferably anxiety and depression pattern among endocrinolgical disorders patients. With this background, we studied the depression and anxiety in different endocrinogical disorders.

Methods

The present study was conducted in the SMHS Hospital of Government medical college Srinagar and the study sample was drawn from patients attending the endocrinogical OPD in the Department of Medicine at Government Medical College Hospital Srinagar (SMHS).The study was conducted over a period of one and half year, from April 2011 to September 2012 in patients attending the Department of Medicine Government Medical College Hospital Srinagar enrolling 152 cases of Endocrinological disorders. All patients were first examined by Consultant endocrinologist. The patients were then selected using simple random sampling choosing every alternate patient. General information including age, sex, residence, economic status, past history of thyroid disorders, family history of psychiatric disorders was included. An endocrinology specialist first examined the patients, while a psychiatrist administers Hospital Anxiety and Depression scale (HADS). Hospital Anxiety and Depression scale (HADS) was used for purpose of screening anxiety and depressive disorders in patients suffering from different endocrinogical disorders. Hospital Anxiety and Depression scale (HADS) is used for purpose of screening anxiety and depressive disorders in patient suffering from chronic somatic disease. HADS contain 14 items and consist of two subscales: anxiety and depression with seven question each. Each question is rated on four point scale (0 to 3) giving maximum total score of 21 each for anxiety and depression. Score of 11 or more is considered a case of psychological morbidity, while as score of 8-10 represents borderline and 0-7 as normal. The forward backward procedure was applied to translate HADS from English to Urdu by a medical person and professional translator. [15]

The participating physicians subjected select patient of chronic Endocrinological disorders to HADS Questionnaire and recorded scores both for anxiety and for depression.

The patients were subjected to inclusion and exclusion criteria as given below:

Inclusion criteria

1. All endocrinological disorders.

2. Both sexes will be included.

3. Age > 15 yrs.

4. Those who will give consent.

Exclusion criteria

1. Those who don’t consent.

2. If diagnoses is not clear.

3. Age less than 15 years.

4. Presence of pregnancy or a history of pregnancy in the last six months.

5. Those who are on steroids or drugs known to interfere with thyroid function

General description, demographic data and psychiatric history was be recorded using the semi structured interview which was pretested

Statistical methods: Statistical analyses were performed using the SPSS, version 16.0 for Windows. A secure computerized database was established and maintained throughout the study. Patient names were replaced with unique identifying numbers. Descriptive statics were used to generate a profiles of each illness group based on presence of depression only, anxiety only and those with both anxiety and depression. To determine whether there were any significant differences between each illness group in the prevalence of depression and anxiety disorders , an unadjusted 3×2×2 test chi square was conducted. Data were analyzed by the Pearson chi-squared test and t test. P<0.05 was considered as the significance level in the evaluations.

Consent: Informed consent was obtained from each patient; those who were considered incapable of consenting were allowed to participate with consent of their closest family member or custodian. All patients were informed about the nature of the research within the hospital and willingly gave their consent to participate. Information sheets and preliminary interviews made it clear that the choice to consent or otherwise would have no bearing on the treatment offered. The project ensured the anonymity of the subjects by replacing patient names with unique identifying numbers before the statistical procedures began.

Results

A total of 152 patients from the endocrinological departments of Govt. Medical College, Srinagar hospitals were taken up for study. They were evaluated in detail with regard to socio-demographic profile regard to presence of psychiatric co-morbidity by HADS and the results have been presented below in the tabulated form .Only patients who consented for complete interview and respond to all HADS questions were considered in final analyses.

Out of total 152 subjects 71 were males (46.72%)), and 81 were females (53.28%) (Table 1). Most of cases belong to 35-45 year age group (26.3%) followed by age group 25- 35 years (24.3%) and 67.7% were married and 18.4% were unmarried. More than half (51.97 %) of the study subjects were from nuclear families and 82 (53.9%) were illiterate and majority 84(55.4 %) belonging to middle class family. The socio-demographic profile of the studied patients is shown in Table-2 .

Out of 152 patients with endocrine disorders, 56(37%) patients elicited HADS score of 10 or less indicating absent or doubtful association anxiety or depression. 96 (63.15%) patients were found positive to HADS Questionnaire with anxiety/depression score of 11 or more. The mean HADS score for anxiety alone, depression alone and anxiety/depression patients were 13.42, 15.7 and 25.62 respectively. On the basis of HADS screening, 96(63.157%) patients had varying degree of psychiatric co morbidity. 27 (28.12%) had anxiety alone, 30(43.47%) had depression alone where 39(40.62%) as patients had anxiety and depression both.(Table 3) The breakdown of total number of different Endocrinological disorders is given in table. Maximum psychiatric comorbidity is found in thyroid patients (69.35%) followed by diabetic patients (68.05). (Table 4).

Table 1: Age and sex distribution

Table 2: Demographic Characteristics of the Studied Patients

Table 3: Result of HADS Scoring

Table 4: Types of endocrinological disorders

Table-5 Psychiatric Co-morbidity across Socio-demography of the Patients

Discussion

This study is the first to offer data on psychiatric morbidity among endocrine patients in the Kashmiri population. 63.15% (96) patients were found positive to HADS questionnaire with anxiety/depression score of 11 or more in our study. The results of this study suggest patient suffering from endocrinological disorders are likely to have a co-morbid psychiatric disorder. [5, 16].Depressive disorders and anxiety disorders are the commonest psychiatric disorders in endocrinogical patients. [3].Numerous studies have shown a high correlation between depression and endocrinological disorders and this study supports these findings, with 43.47 %( 30) of the participants having depressive symptoms on the HADS. [3, 16] 40.62% (39) respondents had both depressive symptoms and an anxiety disorder. 28.12% (27) participants were diagnosed with an anxiety disorder, which is slightly higher than the lifetime prevalence of anxiety disorder in men [16]. Our findings of a high proportion of respondents with endocrinological disorders (45.7%) Female were more in number than their male counterparts 59(61.4%) vs. 37(38.54%) and the majority of men presenting with endocrinological disorders were between the ages of 35 and 45 years has also been reported in a previous studies. [4, 8].The findings of our study suggest that psychiatric disorders are highly prevalent in endocrinological disorders and is largely unrecognized in the primary care setting. Endocrine disorders of different kinds, irrespective of treatment have been associated with Psychological distress. Psychological wellbeing of endocrine disorders may provide new insights in clinical endocrinology. Further psychological disorders comorbid with endocrinological disorders adds to their disability as well as cost to the individual and the society.[17] Most of the clinicians do not suspect this important association of endocrinological disorders in the beginning resulting in delayed diagnosis. Thus, the high prevalence of anxiety and depression in endocrinological disorders in our study supports a case for screening for these disorders in endocrinological clinics. Furthermore, recognition and treatment of these comorbidities could improve patient outcomes. Future studies should focus on replicating or refuting these findings in larger samples as well as in testing interventions aimed at targeting psychological morbidities in this patient group. Under-recognition of psychiatric morbidity is not an uncommon phenomenon, and has been found in similar local studies of psychiatric morbidity in other medical illnesses[8].Thus, more attention should be paid to recognizing psychiatric morbidities in this group of patients.. The reasons for increase in the frequency of psychiatric disorders are multi-factorial. Being chronic illness leads to psychological stress .The major limitation of our study was relatively small sample size. Another limitation of our study is its crossectional design, which does not allow us to determine direction of causality in the relationship between endocrinological disorders and depression/anxiety. More community based studies are required to assess the magnitude of the problem and to lay down principles to help such patients.In order to clarify the temporal relationship prospective studies with a bigger sample size are essential in the future. As far as we are aware, this is a first of its kind study in kashmir. Endocrinological disorders accounts for a huge proportion of referrals to psychiatric clinics and misery is added upon an already devastating metabolic disease. To add the cost associated with psychiatric morbidity accounts individual and to the society are substantial. Thus, the high prevalence of anxiety and depression in endocrinological disorders in our study supports a case for screening for these disorders in Endocrinological clinics. Furthermore, recognition and treatment of these comorbidities could improve patient outcomes.

Introduction

Anterior knee pain or patellofemoral pain is a common clinical presentation especially in females. It is a challenging clinical problem. The specific cause can be difficult to diagnose as the aetiology remains poorly understood and there are various pathologic entities that can result in pain in the anterior aspect of knee.

Multiple surgical options have been used to treat the condition. Lateral retinacular release is one of these options and has been used to treat anterior knee pain with variable results^1-5. The aim of this study was to assess isolated patella lateral retinaculum release as a treatment for anterior knee pain.

Materials and Methods

We performed a retrospective review of all the patients who underwent isolated arthroscopic lateral patella retinacular release under a single surgeon between July 2007 and July 2010. Exclusion criteria included significant patellar instability and severe mal-alignment on both radiological and clinical assessment and additional procedures including cartilage debridement, meniscal tear repair/excision or patella stabilization.

Data was collected from case notes (demographics, pre-operative and intra-operative findings and any post-operative complications), archived radiographs and postal questionnaires including pre and post procedure Oxford Knee Score (OKS), as well as patient satisfaction. Patient satisfaction questions included a grading of satisfaction of 1(completely dissatisfied) - 10 (completely satisfied) and whether patient would reconsider the procedure if given the choice again.

Independent factors assessed were age, sex, tight lateral retinaculum, osteoarthritic x-ray changes of all compartments, intraoperative findings of grade of arthritis and lateral subluxation and postoperative physiotherapy. The primary outcome assessed was patient reported outcome measures, including the improvement in post procedure OKS and patient satisfaction scores. SPSS Version 20 was used for analysis.

Preoperative and Postoperative OKS – total and components - were compared using Wilcoxon Signed Rank Test. The Mann Whitney U test was used for nominal data and Kruskal-Wallis test was used for continuous data for total OKS. Individual OKS components compared were ability to kneel and ability to climb stairs - more representative of patellofemoral joint.

Results

59 patients were identified with male to female ratio of 1:1.5. The mean age was 58.7 (range 25 to 77). 40 patients (67%) returned completed forms. Four patients had further surgery; three total knee replacement and one subsequent arthroscopic procedure for meniscal tears. These patients were excluded from the study. Four patients had bilateral procedures. Therefore after the exclusions for further surgery and those who failed to return completed forms 36 patients were included, on whom 40 procedures had been performed. Changes of osteoarthritis - graded according to Kellgren and Lawrence system - on the medial and lateral facets of the patella were noted on preoperative Merchant views (Table 1) and the tibiofemoral compartment as well.

Table 1 – Pre-Operative Radiographic grades of Patellofemoral change

All patients had undergone standardized preoperative physiotherapy regimen with no significant benefit. Two had, had intra-articular hyaluronic acid injection with no benefit.

All procedures were performed by a single surgeon (PE) and intraoperative findings of cartilage Outerbridge grade were noted in all compartments. Closed lateral retinacular release was performed with Smiley’s knife from just below lower end up to the upper border of patella.

Mean follow up duration was 20.43 months +/- 10.64. Patients were divided into three groups of follow up durations. 6-12 months had 6, 12-18months had 18 and >18months had 16 cases. The best results were in 12-18 month follow up but no statistically significant difference was found between different groups. There was no significant difference in age and gender distribution amongst different durations of follow up. Also there was no significant difference in age, gender and different durations of follow up between responders and non-responders of the questionnaire. There were no reported postoperative complications.

24(60%) underwent post-operative physiotherapy. The mean OKS improved from 23.05 (range11-40) to 35.30 (range14-48) [p value <0.0001]. Individual components of OKS, particularly ability to climb stairs and ability to kneel, also showed statistically significant improvements (Figure 1, Figure 2).

Fig 1 – OKS – ability to climb stairs

Fig 2 – OKS – ability to kneel

Univariate analysis showed improvement of total OKS and OKS for ability to kneel were significantly associated with higher grade of radiographic lateral patellofemoral joint wear (p value 0.025 and 0.042 respectively) and postoperative physiotherapy (p value 0.018 and 0.003) and improvement in OKS for ability to climb stairs was significantly associated with higher grade cartilage wear, noted intraoperatively, for trochlea (p value 0.042) and patella (p value 0.022).

However the OKS components lost this significance if there was Outerbridge Grade 3 or more wear in tibiofemoral articulation.

The procedure had a high mean satisfaction score of 8.2 (range 4 to 10), and 32 of 36 patients would have the procedure again if needed.

Discussion

Anterior Knee pain or patella pain syndrome is a very common clinical problem faced by orthopaedic surgeons. However the aetiology remains poorly understood. Mori et al⁶identified evidence of degenerative neuropathy in 29 out of 35 histologically examined specimens of resected lateral retinaculum; thus suggesting it may originate in the lateral retinaculum. Lateral Retinacular release would denervate this tissue producing symptomatic relief. Osterneier et al⁷ measured patellofemoral contact pressures and kinematics using fresh-frozen cadaver specimens both before and after lateral release. They concluded that release could decrease pressure on the lateral patella facet in flexion but did not stabilize the patella or medialise patella tracking. This possibly explains our finding of improvement with lateral patellofemoral joint wear.

Arthroscopic lateral release remains a controversial topic because of lack of well-designed randomised studies. Fulkerson and Shea⁸ suggested that knees showing lateral patellar tilt without subluxation were more likely to benefit from a lateral release in the absence of grade III or grade IV changes in the articular cartilage. Korkala et al⁹ showed that a lateral release tended to improve symptoms in patients with grade II to grade IV chondromalacia. Our findings concur that greater the patellofemoral articulation cartilage wear the more significant the improvement.

Lodhi et al¹⁰ performed a prospective study of elderly patients with patellofemoral osteoarthritis and pain which conservative management had failed to improve and concluded that the procedure improves function and provides significant pain relief successfully deferring need for arthroplasty; therefore they recommended the procedure in middle aged to elderly patients with symptomatic patellofemoral osteoarthritis.

Twaddle and Parkinson¹¹ suggested lateral release to be an effective, reliable and durable procedure in ‘carefully selected patients’ through their retrospective study.

Our study has deficiencies regarding single surgeon series and retrospective review. However it reflects some of the findings from previous studies suggesting that it is an effective procedure to improve symptoms associated with cartilage changes in patellofemoral articulation without significant tibiofemoral joint osteoarthritis. Further well designed randomized controlled trials are needed to give a more definitive answer.

Conclusion

Isolated lateral patella retinacular release can be effective for anterior knee pain in carefully selected patients, (without significant instability or mal-alignment, with high patellofemoral but low tibiofemoral wear), who have failed conservative management. It particularly improves patients’ ability to kneel and climb stairs, giving a high satisfaction score. The grade of wear of patellofemoral cartilage is the most significant factor in determining this, with post-operative physiotherapy further augmenting the good results.