Alex J. Mitchell

Prevalence of depression, anxiety, and adjustment disorder in oncological, haematological, and palliative-care settings: a meta-analysis of 94 interview-based studies

BACKGROUND Substantial uncertainty exists about prevalence of mood disorders in patients with cancer, including those in oncological, haematological, and palliative-care settings. We aimed to quantitatively summarise the prevalence of depression, anxiety, and adjustments disorders in these settings. METHODS We searched Medline, PsycINFO, Embase, and Web of Knowledge for studies that examined well-defined depression, anxiety, and adjustment disorder in adults with cancer in oncological, haematological, and palliative-care settings. We restricted studies to those using psychiatric interviews. Studies were reviewed in accordance with PRISMA guidelines and a proportion meta-analysis was done. FINDINGS We identified 24 studies with 4007 individuals across seven countries in palliative-care settings. Meta-analytical pooled prevalence of depression defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases (ICD) criteria was 16·5% (95% CI 13·1-20·3), 14·3% (11·1-17·9) for DSM-defined major depression, and 9·6% (3·6-18·1) for DSM-defined minor depression. Prevalence of adjustment disorder alone was 15·4% (10·1-21·6) and of anxiety disorders 9·8% (6·8-13·2). Prevalence of all types of depression combined was of 24·6% (17·5-32·4), depression or adjustment disorder 24·7% (20·8-28·8), and all types of mood disorder 29·0% (10·1-52·9). We identified 70 studies with 10,071 individuals across 14 countries in oncological and haematological settings. Prevalence of depression by DSM or ICD criteria was 16·3% (13·4-19·5); for DSM-defined major depression it was 14·9% (12·2-17·7) and for DSM-defined minor depression 19·2% (9·1-31·9). Prevalence of adjustment disorder was 19·4% (14·5-24·8), anxiety 10·3% (5·1-17·0), and dysthymia 2·7% (1·7-4·0). Combination diagnoses were common; all types of depression occurred in 20·7% (12·9-29·8) of patients, depression or adjustment disorder in 31·6% (25·0-38·7), and any mood disorder in 38·2% (28·4-48·6). There were few consistent correlates of depression: there was no effect of age, sex, or clinical setting and inadequate data to examine cancer type and illness duration. INTERPRETATION Interview-defined depression and anxiety is less common in patients with cancer than previously thought, although some combination of mood disorders occurs in 30-40% of patients in hospital settings without a significant difference between palliative-care and non-palliative-care settings. Clinicians should remain vigilant for mood complications, not just depression. FUNDING None.

Rate of progression of mild cognitive impairment to dementia – meta‐analysis of 41 robust inception cohort studies

Objective:  To quantify the risk of developing dementia in those with mild cognitive impairment (MCI).

Clinical diagnosis of depression in primary care: a meta-analysis

BACKGROUND Depression is a major burden for the health-care system worldwide. Most care for depression is delivered by general practitioners (GPs). We assessed the rate of true positives and negatives, and false positives and negatives in primary care when GPs make routine diagnoses of depression. METHODS We undertook a meta-analysis of 118 studies that assessed the accuracy of unassisted diagnoses of depression by GPs. 41 of these studies were included because they had a robust outcome standard of a structured or semi-structured interview. FINDINGS 50 371 patients were pooled across 41 studies and examined. GPs correctly identified depression in 47.3% (95% CI 41.7% to 53.0%) of cases and recorded depression in their notes in 33.6% (22.4% to 45.7%). 19 studies assessed both rule-in and rule-out accuracy; from these studies, the weighted sensitivity was 50.1% (41.3% to 59.0%) and specificity was 81.3% (74.5% to 87.3%). At a rate of 21.9%, the positive predictive value was 42.0% (39.6% to 44.3%) and the negative predictive value was 85.8% (84.8% to 86.7%). This finding suggests that for every 100 unselected cases seen in primary care, there are more false positives (n=15) than either missed (n=10) or identified cases (n=10). Accuracy was improved with prospective examination over an extended period (3-12 months) rather than relying on a one-off assessment or case-note records. INTERPRETATION GPs can rule out depression in most people who are not depressed; however, the modest prevalence of depression in primary care means that misidentifications outnumber missed cases. Diagnosis could be improved by re-assessment of individuals who might have depression. FUNDING None.

A meta-analysis of the accuracy of the mini-mental state examination in the detection of dementia and mild cognitive impairment

The MMSE is the most widely used cognitive test but its accuracy and clinical utility in diagnosing cognitive disorders is not fully known. A meta-analysis of 34 dementia studies and five mild cognitive impairment (MCI) studies was conducted, separated into high and low prevalence settings. In memory clinic settings the MMSE had a pooled sensitivity (Se) of 79.8%, a specificity (Sp) of 81.3%, a positive predictive value (PPV) of 86.3% and a negative predictive value (NPV) of 73.0%. In mixed specialist hospital settings the Se, Sp, PPV and NPV were 71.1%, 95.6%, 94.2% and 76.4%, respectively. In non-clinical community settings the MMSE had a pooled Se of 85.1%, a Sp of 85.5%, a PPV of 34.5% and an NPV of 98.5%. In those studies conducted purely in primary care the Se, Sp, PPV and NPV were 78.4%, 87.8%. 53.6% and 95.7%, respectively. Thus the case-finding ability of the MMSE was best when confirming a suspected diagnosis in specialist settings with correct identification made in 27/30 positive results. It was modestly effective at ruling-out dementia in specialist settings. Conversely, in non-specialist settings, the MMSE was best at ruling out dementia, achieving about 29/30 correct reassurances with less than three false negatives out of every 100 screens. Regarding use of the MMSE in identifying MCI, limited evidence was found with only five robust studies comparing MCI with healthy subjects and three comparing Alzheimers disease with MCI. Provisionally, the MMSE had very limited value in making a diagnosis of MCI against healthy controls and modest rule-out accuracy. It had similarly limited ability to help identify cases of Alzheimers disease against MCI. In conclusion the MMSE offers modest accuracy with best value for ruling-out a diagnosis of dementia in community and primary care. For all other used it should be combined with or replaced by other methods.

Prevalence of Metabolic Syndrome and Metabolic Abnormalities in Schizophrenia and Related Disorders—A Systematic Review and Meta-Analysis

Individuals with schizophrenia have high levels of medical comorbidity and cardiovascular risk factors. The presence of 3 or more specific factors is indicative of metabolic syndrome, which is a significant influence upon future morbidity and mortality. We aimed to clarify the prevalence and predictors of metabolic syndrome (MetS) in adults with schizophrenia and related disorders, accounting for subgroup differences. A PRISMA systematic search, appraisal, and meta-analysis were conducted of 126 analyses in 77 publications (n = 25,692). The overall rate of MetS was 32.5% (95% CI = 30.1%-35.0%), and there were only minor differences according to the different definitions of MetS, treatment setting (inpatient vs outpatient), by country of origin and no appreciable difference between males and females. Older age had a modest influence on the rate of MetS (adjusted R(2) = .20; P < .0001), but the strongest influence was of illness duration (adjusted R(2) = .35; P < .0001). At a study level, waist size was most useful in predicting high rate of MetS with a sensitivity of 79.4% and a specificity of 78.8%. Sensitivity and specificity of high blood pressure, high triglycerides, high glucose and low high-density lipoprotein, and age (>38 y) are shown in supplementary appendix 2 online. Regarding prescribed antipsychotic medication, highest rates were seen in those prescribed clozapine (51.9%) and lowest rates of MetS in those who were unmedicated (20.2%). Present findings strongly support the notion that patients with schizophrenia should be considered a high-risk group. Patients with schizophrenia should receive regular monitoring and adequate treatment of cardio-metabolic risk factors.

Depression screening and patient outcomes in cardiovascular care: a systematic review

CONTEXT Several practice guidelines recommend that depression be evaluated and treated in patients with cardiovascular disease, but the potential benefits of this are unclear. OBJECTIVE To evaluate the potential benefits of depression screening in patients with cardiovascular disease by assessing (1) the accuracy of depression screening instruments; (2) the effect of depression treatment on depression and cardiac outcomes; and (3) the effect of screening on depression and cardiac outcomes in patients in cardiovascular care settings. DATA SOURCES MEDLINE, PsycINFO, CINAHL, EMBASE, ISI, SCOPUS, and Cochrane databases from inception to May 1, 2008; manual journal searches; reference list reviews; and citation tracking of included articles. STUDY SELECTION We included articles in any language about patients in cardiovascular care settings that (1) compared a screening instrument to a valid major depressive disorder criterion standard; (2) compared depression treatment with placebo or usual care in a randomized controlled trial; or (3) assessed the effect of screening on depression identification and treatment rates, depression, or cardiac outcomes. DATA EXTRACTION Methodological characteristics and outcomes were extracted by 2 investigators. RESULTS We identified 11 studies about screening accuracy, 6 depression treatment trials, but no studies that evaluated the effects of screening on depression or cardiovascular outcomes. In studies that tested depression screening instruments using a priori-defined cutoff scores, sensitivity ranged from 39% to 100% (median, 84%) and specificity ranged from 58% to 94% (median, 79%). Depression treatment with medication or cognitive behavioral therapy resulted in modest reductions in depressive symptoms (effect size, 0.20-0.38; r(2), 1%-4%). There was no evidence that depression treatment improved cardiac outcomes. Among patients with depression and history of myocardial infarction in the ENRICHD trial, there was no difference in event-free survival between participants treated with cognitive behavioral therapy supplemented by an antidepressant vs usual care (75.5% vs 74.7%, respectively). CONCLUSIONS Depression treatment with medication or cognitive behavioral therapy in patients with cardiovascular disease is associated with modest improvement in depressive symptoms but no improvement in cardiac outcomes. No clinical trials have assessed whether screening for depression improves depressive symptoms or cardiac outcomes in patients with cardiovascular disease.

Pooled Results From 38 Analyses of the Accuracy of Distress Thermometer and Other Ultra-Short Methods of Detecting Cancer-Related Mood Disorders

Ultra-short screening tools involving fewer than five questions have been recommended as a simple method of detecting distress, anxiety, or depression in cancer settings. Such methods have practical appeal, but their diagnostic accuracy is unclear. A literature search limited to diagnostic validity studies of ultra-short screening in cancer settings identified 38 analyses, including 19 assessing the Distress Thermometer alone, involving a total of 6,414 unique patients. The pooled ability of ultra-short methods to detect depression was given by a sensitivity of 78.4%, a specificity of 66.8%, a positive predictive value (PPV) of 34.2%, and a negative predictive value (NPV) of 93.4%. Thus these tools were very good at excluding possible cases of depression but poor at confirming a suspected diagnosis. The pooled ability of ultra-short methods to detect anxiety was given by a sensitivity of 77.3% and a specificity of 56.6% (PPV, 55.2%; NPV, 80.25%) and for distress a sensitivity of 78.3% and a specificity of 66.5% (PPV, 59.7%; NPV, of 82.8%). Results using the Distress Thermometer alone were similar. Scores of integrated accuracy, using the Youden index and diagnostic odds ratio, suggested modest overall accuracy with least success in diagnosing anxiety disorders. Ultra-short methods were modestly effective in screening for mood disorders. Their rule-in ability was poorer than their rule-out ability. Ultra-short methods cannot be used alone to diagnose depression, anxiety, or distress in cancer patients but they may be considered as a first-stage screen to rule out cases of depression.

Quality of life and its assessment in multiple sclerosis: integrating physical and psychological components of wellbeing

Health-related quality of life (HRQoL) has been more intensively studied in multiple sclerosis (MS) than in any other neurological disorder. Traditional medical models of impairment and disability are an incomplete summary of disease burden. Quality of life can be thought of as the sum of all sources of satisfaction (including anticipated sources) minus all threats (including anticipated threats). Many psychosocial factors-including coping, mood, self-efficacy, and perceived support-influence the quality of life of patients with MS more than biological variables such as weakness or extent of MRI lesions. Neuropsychiatric complications such as cognitive impairment and fatigue are also important predictors, even in those patients in the early stages of the disease. We review generic and specific HRQoL measures to help clinicians choose the most appropriate therapies. Subjective (self-report) HRQoL measures may serve to alert clinicians to areas that would otherwise be overlooked. Studies of new interventions should include an assessment of HRQoL not just impairment or disability alone.

Screening for Distress and Unmet Needs in Patients With Cancer: Review and Recommendations

PURPOSE This review summarizes the need for and process of screening for distress and assessing unmet needs of patients with cancer as well as the possible benefits of implementing screening. METHODS Three areas of the relevant literature were reviewed and summarized using structured literature searches: psychometric properties of commonly used distress screening tools, psychometric properties of relevant unmet needs assessment tools, and implementation of distress screening programs that assessed patient-reported outcomes (PROs). RESULTS Distress and unmet needs are common problems in cancer settings, and programs that routinely screen for and treat distress are feasible, particularly when staff are supported and links with specialist psychosocial services exist. Many distress screening and unmet need tools have been subject to preliminary validation, but few have been compared head to head in independent centers and in different stages of cancer. Research investigating the overall effectiveness of screening for distress in terms of improved recognition and treatment of distress and associated problems is not yet conclusive, but screening seems to improve communication between patients and clinicians and may enhance psychosocial referrals. Direct effects on quality of life are uncertain, but screening may help improve discussion of quality-of-life issues. CONCLUSION Involving all stakeholders and frontline clinicians when planning screening for distress programs is recommended. Training frontline staff to deliver screening programs is crucial, and continuing to rigorously evaluate outcomes, including PROs, process of care, referrals, and economic costs and benefits is essential.

Risk of metabolic syndrome and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder: a systematic review and meta-analysis

Metabolic syndrome (MetS) and its components are highly predictive of cardiovascular diseases. The primary aim of this systematic review and meta‐analysis was to assess the prevalence of MetS and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder, comparing subjects with different disorders and taking into account demographic variables and psychotropic medication use. The secondary aim was to compare the MetS prevalence in persons with any of the selected disorders versus matched general population controls. The pooled MetS prevalence in people with severe mental illness was 32.6% (95% CI: 30.8%‐34.4%; N = 198; n = 52,678). Relative risk meta‐analyses established that there was no significant difference in MetS prevalence in studies directly comparing schizophrenia versus bipolar disorder, and in those directly comparing bipolar disorder versus major depressive disorder. Only two studies directly compared people with schizophrenia and major depressive disorder, precluding meta‐analytic calculations. Older age and a higher body mass index were significant moderators in the final demographic regression model (z = −3.6, p = 0.0003, r2 = 0.19). People treated with all individual antipsychotic medications had a significantly (p<0.001) higher MetS risk compared to antipsychotic‐naïve participants. MetS risk was significantly higher with clozapine and olanzapine (except vs. clozapine) than other antipsychotics, and significantly lower with aripiprazole than other antipsychotics (except vs. amisulpride). Compared with matched general population controls, people with severe mental illness had a significantly increased risk for MetS (RR = 1.58; 95% CI: 1.35‐1.86; p<0.001) and all its components, except for hypertension (p = 0.07). These data suggest that the risk for MetS is similarly elevated in the diagnostic subgroups of severe mental illness. Routine screening and multidisciplinary management of medical and behavioral conditions is needed in these patients. Risks of individual antipsychotics should be considered when making treatment choices.