Treating subclinical hypothyroidism in individuals with or without mental health problems -A Delphi based expert consensus study in two countries.

Background: Subclinical hypothyroidism (SCH) is a common endocrine problem with prevalence estimates between 4% and 20%. Symptoms are often non-specific but can substantially affect well-being leading to repeated medical consultations. The effect of thyroid hormone replacement therapy (THRT) in patients with SCH remains uncertain. Current guidelines, limited by the lack of high-quality evidence, have been controversial with limited adherence in clinical practice. Methods: Three-round modified Delphi method to establish consensus regarding diagnosis and treatment of individuals with SCH with and without affective disorder or anxiety, conducted with clinicians from three specialties, general practice, endocrinology and psychiatry, and two countries, Sweden and the United Kingdom. Results: Sixty clinicians, 20 per specialty, were recruited. Fifty-three (88%) participants completed all three rounds. The participants reached consensus on five of the 26 practice statements that (a) repeated testing was required for the diagnosis of subclinical hypothyroidism, (b) antibody screening should usually occur, and (c and d) antibody screening would strengthen the indication for thyroid hormone replacement therapy in both individuals with or without affective disorder or anxiety. The participants disagreed with (e) a requirement of a TSH threshold ≥ 20 mIU/L for thyroid hormone replacement therapy start. Psychiatrists and GPs but not endocrinologists, agreed that there was a frequent discrepancy between laboratory results and clinical symptoms, and disagreed that testing for thyroid dysfunction was overused in patients presenting with depression or anxiety, or fatigue. Conclusions: In many aspects, attitudes toward diagnosing and treating SCH remain diverse. The inability of our Delphi panel to achieve consensus on most items and the disagreement with a TSH ≥ 20 mIU/L threshold for treatment suggest that the concept of SCH may need rethinking with a better understanding of the hypothalamic-pituitary-thyroid physiology. Given that the scientific evidence is currently not conclusive, guidelines in this area should not be taken as definitive.

Neurological Dysfunction in Long COVID Should Not Be Labelled as Functional Neurological Disorder.

There have been suggestions that Long COVID might be purely functional (meaning psychological) in origin. Labelling patients with neurological dysfunction in Long COVID as having functional neurological disorder (FND) in the absence of proper testing may be symptomatic of that line of thought. This practice is problematic for Long COVID patients, as motor and balance symptoms have been reported to occur in Long COVID frequently. FND is characterized by the presentation of symptoms that seem neurological but lack compatibility of the symptom with a neurological substrate. Although diagnostic classification according to the ICD-11 and DSM-5-TR is dependent predominantly on the exclusion of any other medical condition that could account for the symptoms, current neurological practice of FND classification allows for such comorbidity. As a consequence, Long COVID patients with motor and balance symptoms mislabeled as FND have no longer access to Long COVID care, whereas treatment for FND is seldom provided and is ineffective. Research into underlying mechanisms and diagnostic methods should explore how to determine whether motor and balance symptoms currently diagnosed as FND should be considered one part of Long COVID symptoms, in other words, one component of symptomatology, and in which cases they correctly represent FND. Research into rehabilitation models, treatment and integrated care are needed, which should take into account biological underpinnings as well as possible psychological mechanisms and the patient perspective.

Sleep health among people with severe mental ill health during the COVID-19 pandemic: Results from a linked UK population cohort study.

Objectives: Sleep problems are a transdiagnostic feature of nearly all psychiatric conditions, and a strong risk factor for initial and recurrent episodes. However, people with severe mental ill health (SMI) are often excluded from general population surveys, and as such the extent and associates of poor sleep in this population are less well understood. This study explores sleep health in an SMI sample during the COVID-19 pandemic, using multiple regression to identify risk factors, including daily routine, wellbeing and demographics. Methods: An existing cohort of people with an SMI diagnosis were sampled. Participants were invited to complete a self-report survey about their health and the impacts of COVID-19 and associated public health measures. Sleep duration, efficiency, and quality were measured using items from the Pittsburgh Sleep Quality Index (PSQI). Results: Two hundred forty-nine adults (aged 21-84 years) completed the survey. Mean sleep duration and efficiency were similar to general population estimates, at 7 h 19 min and 78%, respectively. However, 43% reported "bad" sleep quality that was associated with being younger in age as well as disturbed routine and declined wellbeing. Indeed, 37% reported a disturbed routine during the pandemic. Conclusions: High estimates of perceived poor sleep quality in the SMI population align with previous findings. Supporting people with SMI to maintain routine regularity may work to protect sleep quality and wellbeing. Future research should more closely examine sleep health in people with SMI, using accessible and scalable measures of objective and subjective sleep, examining longitudinal trends.

Predicting relapse or recurrence of depression: systematic review of prognostic models.

BACKGROUND: Relapse and recurrence of depression are common, contributing to the overall burden of depression globally. Accurate prediction of relapse or recurrence while patients are well would allow the identification of high-risk individuals and may effectively guide the allocation of interventions to prevent relapse and recurrence. AIMS: To review prognostic models developed to predict the risk of relapse, recurrence, sustained remission, or recovery in adults with remitted major depressive disorder. METHOD: We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2021. We included development and external validation studies of multivariable prognostic models. We assessed risk of bias of included studies using the Prediction model risk of bias assessment tool (PROBAST). RESULTS: We identified 12 eligible prognostic model studies (11 unique prognostic models): 8 model development-only studies, 3 model development and external validation studies and 1 external validation-only study. Multiple estimates of performance measures were not available and meta-analysis was therefore not necessary. Eleven out of the 12 included studies were assessed as being at high overall risk of bias and none examined clinical utility. CONCLUSIONS: Due to high risk of bias of the included studies, poor predictive performance and limited external validation of the models identified, presently available clinical prediction models for relapse and recurrence of depression are not yet sufficiently developed for deploying in clinical settings. There is a need for improved prognosis research in this clinical area and future studies should conform to best practice methodological and reporting guidelines.

The development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study.

BACKGROUND: Most patients who present with depression are treated in primary care by general practitioners (GPs). Relapse of depression is common (at least 50% of patients treated for depression will relapse after a single episode) and leads to considerable morbidity and decreased quality of life for patients. The majority of patients will relapse within 6 months, and those with a history of relapse are more likely to relapse in the future than those with no such history. GPs see a largely undifferentiated case-mix of patients, and once patients with depression reach remission, there is limited guidance to help GPs stratify patients according to risk of relapse. We aim to develop a prognostic model to predict an individual's risk of relapse within 6-8 months of entering remission. The long-term objective is to inform the clinical management of depression after the acute phase. METHODS: We will develop a prognostic model using secondary analysis of individual participant data drawn from seven RCTs and one longitudinal cohort study in primary or community care settings. We will use logistic regression to predict the outcome of relapse of depression within 6-8 months. We plan to include the following established relapse predictors in the model: residual depressive symptoms, number of previous depressive episodes, co-morbid anxiety and severity of index episode. We will use a "full model" development approach, including all available predictors. Performance statistics (optimism-adjusted C-statistic, calibration-in-the-large, calibration slope) and calibration plots (with smoothed calibration curves) will be calculated. Generalisability of predictive performance will be assessed through internal-external cross-validation. Clinical utility will be explored through net benefit analysis. DISCUSSION: We will derive a statistical model to predict relapse of depression in remitted depressed patients in primary care. Assuming the model has sufficient predictive performance, we outline the next steps including independent external validation and further assessment of clinical utility and impact. STUDY REGISTRATION: ClinicalTrials.gov ID: NCT04666662.

Prognostic models for predicting relapse or recurrence of major depressive disorder in adults.

BACKGROUND: Relapse (the re-emergence of depressive symptoms after some level of improvement but preceding recovery) and recurrence (onset of a new depressive episode after recovery) are common in depression, lead to worse outcomes and quality of life for patients and exert a high economic cost on society. Outcomes can be predicted by using multivariable prognostic models, which use information about several predictors to produce an individualised risk estimate. The ability to accurately predict relapse or recurrence while patients are well (in remission) would allow the identification of high-risk individuals and may improve overall treatment outcomes for patients by enabling more efficient allocation of interventions to prevent relapse and recurrence. OBJECTIVES: To summarise the predictive performance of prognostic models developed to predict the risk of relapse, recurrence, sustained remission or recovery in adults with major depressive disorder who meet criteria for remission or recovery. SEARCH METHODS: We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2020. We also searched sources of grey literature, screened the reference lists of included studies and performed a forward citation search. There were no restrictions applied to the searches by date, language or publication status . SELECTION CRITERIA: We included development and external validation (testing model performance in data separate from the development data) studies of any multivariable prognostic models (including two or more predictors) to predict relapse, recurrence, sustained remission, or recovery in adults (aged 18 years and over) with remitted depression, in any clinical setting. We included all study designs and accepted all definitions of relapse, recurrence and other related outcomes. We did not specify a comparator prognostic model. DATA COLLECTION AND ANALYSIS: Two review authors independently screened references; extracted data (using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS)); and assessed risks of bias of included studies (using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)). We referred any disagreements to a third independent review author. Where we found sufficient (10 or more) external validation studies of an individual model, we planned to perform a meta-analysis of its predictive performance, specifically with respect to its calibration (how well the predicted probabilities match the observed proportions of individuals that experience the outcome) and discrimination (the ability of the model to differentiate between those with and without the outcome). Recommendations could not be qualified using the GRADE system, as guidance is not yet available for prognostic model reviews. MAIN RESULTS: We identified 11 eligible prognostic model studies (10 unique prognostic models). Seven were model development studies; three were model development and external validation studies; and one was an external validation-only study. Multiple estimates of performance measures were not available for any of the models and, meta-analysis was therefore not possible. Ten out of the 11 included studies were assessed as being at high overall risk of bias. Common weaknesses included insufficient sample size, inappropriate handling of missing data and lack of information about discrimination and calibration. One paper (Klein 2018) was at low overall risk of bias and presented a prognostic model including the following predictors: number of previous depressive episodes, residual depressive symptoms and severity of the last depressive episode. The external predictive performance of this model was poor (C-statistic 0.59; calibration slope 0.56; confidence intervals not reported). None of the identified studies examined the clinical utility (net benefit) of the developed model. AUTHORS' CONCLUSIONS: Of the 10 prognostic models identified (across 11 studies), only four underwent external validation. Most of the studies (n = 10) were assessed as being at high overall risk of bias, and the one study that was at low risk of bias presented a model with poor predictive performance. There is a need for improved prognostic research in this clinical area, with future studies conforming to current best practice recommendations for prognostic model development/validation and reporting findings in line with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

The role of relapse prevention for depression in collaborative care: A systematic review.

BACKGROUND: Relapse (the re-emergence of depression symptoms before full recovery) is common in depression and relapse prevention strategies are not well researched in primary care settings. Collaborative care is effective for treating acute phase depression but little is known about the use of relapse prevention strategies in collaborative care. We undertook a systematic review to identify and characterise relapse prevention strategies in the context of collaborative care. METHODS: We searched for Randomised Controlled Trials (RCTs) of collaborative care for depression. In addition to published material, we obtained provider and patient manuals from authors to provide more detail on intervention content. We reported the extent to which collaborative care interventions addressed four relapse prevention components. RESULTS: 93 RCTs were identified. 31 included a formal relapse prevention plan; 42 had proactive monitoring and follow-up after the acute phase; 39 reported strategies for optimising sustained medication adherence; and 20 of the trials reported psychological or psycho-educational treatments persisting beyond the acute phase or focussing on long-term health/relapse prevention. 30 (32.3%) did not report relapse prevention approaches. LIMITATIONS: We did not receive trial materials for approximately half of the trials, which limited our ability to identify relevant features of intervention content. CONCLUSION: Relapse is a significant risk amongst people treated for depression and interventions are needed that specifically address and minimise this risk. Given the advantages of collaborative care as a delivery system for depression care, there is scope for more consistency and increased effort to implement and evaluate relapse prevention strategies.

Impact of telephone delivered case-management on the effectiveness of collaborative care for depression and anti-depressant use: A systematic review and meta-regression.

BACKGROUND: The health service delivery framework collaborative care is an effective intervention for depression. However, uncertainties remain about how to optimise its delivery at scale. Structured case management is a core component of collaborative care; its delivery via the telephone may improve access. AIMS: To examine using meta-regression if telephone delivered case management diminishes the clinical effectiveness of collaborative care on depressive symptoms and anti-depressant use relative to face-to-face delivery methods. METHODS: Randomised controlled trials were eligible if they included collaborative care interventions for adults with depression identified using self-report measures or diagnostic interviews and reported depression outcomes. Sociodemographics, intervention characteristics, depressive symptoms, and anti-depressant use were extracted. Random effects univariable and multivariable meta-regression analyses were used to examine the moderating effect of telephone delivered case-management on outcomes. RESULTS: Ninety-four trials were identified comprising of 103 comparisons across 24, 132 participants with depression outcomes and 67 comparisons from 15,367 participants with anti-depressant use outcomes. Telephone delivered case management did not diminish the effects of collaborative care on depressive symptoms (ß = -0.01, 95% CI -0.12 to 0.10; p = 0.86). Telephone delivered case management decreased anti-depressant medication use (relative risk 0.76, 95% CI 0.63 to 0.92; p = 0.005); this effect remained when assessed simultaneously alongside other study-level moderators of collaborative care. CONCLUSION: Using remote platforms such as the telephone to deliver case management may be a feasible way to implement collaborative care with no loss of effectiveness on depressive symptoms. However, adherence to anti-depressant medication may decrease when telephone case management is used.

Are there researcher allegiance effects in diagnostic validation studies of the PHQ-9? A systematic review and meta-analysis.

OBJECTIVES: To investigate whether an authorship effect is found that leads to better performance in studies conducted by the original developers of the Patient Health Questionnaire (PHQ-9) (allegiant studies). DESIGN: Systematic review with random effects bivariate diagnostic meta-analysis. Search strategies included electronic databases, examination of reference lists and forward citation searches. INCLUSION CRITERIA: Included studies provided sufficient data to calculate the diagnostic accuracy of the PHQ-9 against a gold standard diagnosis of major depression using the algorithm or the summed item scoring method at cut-off point 10. DATA EXTRACTION: Descriptive information, methodological quality criteria and 2×2 contingency tables. RESULTS: Seven allegiant and 20 independent studies reported the diagnostic performance of the PHQ-9 using the algorithm scoring method. Pooled diagnostic OR (DOR) for the allegiant group was 64.40, and 15.05 for non-allegiant studies group. The allegiance status was a significant predictor of DOR variation (p<0.0001).Five allegiant studies and 26 non-allegiant studies reported the performance of the PHQ-9 at recommended cut-off point of 10. Pooled DOR for the allegiant group was 49.31, and 24.96 for the non-allegiant studies. The allegiance status was a significant predictor of DOR variation (p=0.015).Some potential alternative explanations for the observed authorship effect including differences in study characteristics and quality were found, although it is not clear how some of them account for the observed differences. CONCLUSIONS: Allegiant studies reported better performance of the PHQ-9. Allegiance status was predictive of variation in the DOR. Based on the observed differences between independent and non-independent studies, we were unable to conclude or exclude that allegiance effects are present in studies examining the diagnostic performance of the PHQ-9. This study highlights the need for future meta-analyses of diagnostic validation studies of psychological measures to evaluate the impact of researcher allegiance in the primary studies.

Cortisol awakening response and spatial working memory in man: a U-shaped relationship.

OBJECTIVE: The association between hypothalamic-pituitary-adrenal (HPA)-axis function and cognition has long been investigated. An inverted U-shaped relationship has been described between various measures of HPA-axis function and neuropsychological performance in animals and man. Work with glucocorticoid receptor manipulation has corroborated these findings, with particular effects observed in relation to spatial working memory (SWM). As HPA-axis dysfunction is frequently found in patients with psychiatric illness, research in this area has potential implications for the treatment of the commonly observed cognitive impairment in such disorders. Here, we present the results of a pilot study examining the relationship between cortisol awakening response (CAR) and cognitive functions known to be susceptible to HPA-axis manipulation. METHODS: Nineteen healthy male volunteers were recruited, and their CAR and performance in a task of SWM were assessed. RESULTS: A highly significant quadratic relationship was observed between the CAR and SWM error rate (R(2)=0.63, p=0.001). CONCLUSION: We provide novel evidence supporting the existence of an inverted U-shaped relationship between corticosteroid levels and cognitive function in humans.