Getting depression clinical practice guidelines right: time for change?

Validity and Usage of the Seasonal Pattern Assessment Questionnaire (SPAQ) in a French Population of Patients with Depression, Bipolar Disorders and Controls

The Seasonal Pattern Assessment Questionnaire (SPAQ), by Rosenthal et al. (1984), is by far the most used questionnaire to evaluate seasonal effects on mood and behavior. It includes a general seasonality score (GSS), composed of 6 items, from which cutoffs have been established to screen for seasonal affective disorder (SAD). However, it has never been validated in French and associations with circadian rhythm and symptoms of depression and bipolarity remain unclear. In this study, including 165 subjects (95 controls and 70 patients with depression or bipolar disorder), we confirmed the validity of the French version of the SPAQ, with a two-factor structure (a psychological factor: energy, mood, social activity and sleep length; and a food factor: weight and appetite) and a good fit was observed by all indicators. Mood and social activity dimensions were significantly affected by seasons in the depressed/bipolar group and a stronger global seasonality score (GSS) was associated with more severe phenotypes of depression and mania. Subjects meeting SAD and subsyndromal-SAD criteria also showed a delayed circadian rhythm compared to controls. Simple tools, such as the SPAQ, can aid the identification of significant seasonal changes and have direct implications on therapeutics including the use of bright light therapy in order to enhance personalized treatments, but also to prevent adverse seasonal effects.

Perspectives in affective disorders: Clocks and sleep

Mood disorders are often characterised by alterations in circadian rhythms, sleep disturbances and seasonal exacerbation. Conversely, chronobiological treatments utilise zeitgebers for circadian rhythms such as light to improve mood and stabilise sleep, and manipulations of sleep timing and duration as rapid antidepressant modalities. Although sleep deprivation ("wake therapy") can act within hours, and its mood-elevating effects be maintained by regular morning light administration/medication/earlier sleep, it has not entered the regular guidelines for treating affective disorders as a first-line treatment. The hindrances to using chronotherapeutics may lie in their lack of patentability, few sponsors to carry out large multi-centre trials, non-reimbursement by medical insurance and their perceived difficulty or exotic "alternative" nature. Future use can be promoted by new technology (single-sample phase measurements, phone apps, movement and sleep trackers) that provides ambulatory documentation over long periods and feedback to therapist and patient. Light combinations with cognitive behavioural therapy and sleep hygiene practice may speed up and also maintain response. The urgent need for new antidepressants should hopefully lead to reconsideration and implementation of these non-pharmacological methods, as well as further clinical trials. We review the putative neurochemical mechanisms underlying the antidepressant effect of sleep deprivation and light therapy, and current knowledge linking clocks and sleep with affective disorders: neurotransmitter switching, stress and cortico-limbic reactivity, clock genes, cortical neuroplasticity, connectomics and neuroinflammation. Despite the complexity of multi-system mechanisms, more insight will lead to fine tuning and better application of circadian and sleep-related treatments of depression.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

BACKGROUNDS

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.

METHOD

We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.

RESULTS

Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.

CONCLUSIONS

Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.

Effectiveness of bright light therapy on depressive symptoms in older adults with non-seasonal depression: a systematic review protocol

REVIEW QUESTION

What is the effectiveness of bright light therapy (BLT) on depressive symptoms in older adults with non-seasonal depression?

REVIEW OBJECTIVE

The review objective is to determine the current evidence related to the effectiveness of BLT on depressive symptoms in older adults with non-seasonal depression.

Seasonal and meteorological associations with depressive symptoms in older adults: A geo-epidemiological study

BACKGROUND

Given increased social and physiological vulnerabilities, older adults may be particularly susceptible to environmental influences on mood. Whereas the impact of season on mood is well described for adults, studies rarely extend to elders or include objective weather data. We investigated the impact of seasonality and meteorological factors on risk of current depressive symptoms in older adults.

METHODS

We used data on 8027 participants from the first wave of The Irish Longitudinal Study of Ageing, a population-representative cohort of adults aged 50+. Depressive symptoms were recorded using the Centre for Epidemiological Studies Depression Scale. Season was defined according to the World Meteorological Organisation. Data on climate over the preceding thirty years, and temperature and rain over the preceding month, were provided by the Irish Meteorological Service and linked using Geographic Information Systems techniques to participant's geo-coded locations at a resolution of one kilometre.

RESULTS

The highest levels of depressive symptoms were reported in winter and the lowest in spring (mean 6.56 [CI95% 6.09, 7.04] vs. 5.81 [CI95%: 5.40, 6.22]). In fully adjusted linear regression models, participants living in areas with higher levels of rainfall in the preceding and/or current calendar month had greater depressive symptoms (0.04 SE 0.02; p=0.039 per 10mm additional rainfall per month) while those living in areas with sunnier climates had fewer depressive symptoms (-2.67 SE 0.88; p=0.003 for every additional hour of average annual daily sunshine).

LIMITATIONS

This was a cross-sectional analysis thus causality cannot be inferred; monthly rain and temperature averages were available only on a calendar month basis while monthly local levels of sunshine data were not available.

CONCLUSIONS

Environmental cues may influence mood in older adults and thus have relevance for the recognition and treatment of depression in this age group.

Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders

OBJECTIVES

To provide guidance for the management of mood disorders, based on scientific evidence supplemented by expert clinical consensus and formulate recommendations to maximise clinical salience and utility.

METHODS

Articles and information sourced from search engines including PubMed and EMBASE, MEDLINE, PsycINFO and Google Scholar were supplemented by literature known to the mood disorders committee (MDC) (e.g., books, book chapters and government reports) and from published depression and bipolar disorder guidelines. Information was reviewed and discussed by members of the MDC and findings were then formulated into consensus-based recommendations and clinical guidance. The guidelines were subjected to rigorous successive consultation and external review involving: expert and clinical advisors, the public, key stakeholders, professional bodies and specialist groups with interest in mood disorders.

RESULTS

The Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders (Mood Disorders CPG) provide up-to-date guidance and advice regarding the management of mood disorders that is informed by evidence and clinical experience. The Mood Disorders CPG is intended for clinical use by psychiatrists, psychologists, physicians and others with an interest in mental health care.

CONCLUSIONS

The Mood Disorder CPG is the first Clinical Practice Guideline to address both depressive and bipolar disorders. It provides up-to-date recommendations and guidance within an evidence-based framework, supplemented by expert clinical consensus.

MOOD DISORDERS COMMITTEE

Professor Gin Malhi (Chair), Professor Darryl Bassett, Professor Philip Boyce, Professor Richard Bryant, Professor Paul Fitzgerald, Dr Kristina Fritz, Professor Malcolm Hopwood, Dr Bill Lyndon, Professor Roger Mulder, Professor Greg Murray, Professor Richard Porter and Associate Professor Ajeet Singh.

INTERNATIONAL EXPERT ADVISORS

Professor Carlo Altamura, Dr Francesco Colom, Professor Mark George, Professor Guy Goodwin, Professor Roger McIntyre, Dr Roger Ng, Professor John O'Brien, Professor Harold Sackeim, Professor Jan Scott, Dr Nobuhiro Sugiyama, Professor Eduard Vieta, Professor Lakshmi Yatham.

AUSTRALIAN AND NEW ZEALAND EXPERT ADVISORS

Professor Marie-Paule Austin, Professor Michael Berk, Dr Yulisha Byrow, Professor Helen Christensen, Dr Nick De Felice, A/Professor Seetal Dodd, A/Professor Megan Galbally, Dr Josh Geffen, Professor Philip Hazell, A/Professor David Horgan, A/Professor Felice Jacka, Professor Gordon Johnson, Professor Anthony Jorm, Dr Jon-Paul Khoo, Professor Jayashri Kulkarni, Dr Cameron Lacey, Dr Noeline Latt, Professor Florence Levy, A/Professor Andrew Lewis, Professor Colleen Loo, Dr Thomas Mayze, Dr Linton Meagher, Professor Philip Mitchell, Professor Daniel O'Connor, Dr Nick O'Connor, Dr Tim Outhred, Dr Mark Rowe, Dr Narelle Shadbolt, Dr Martien Snellen, Professor John Tiller, Dr Bill Watkins, Dr Raymond Wu.

Bright white light therapy in depression: A critical review of the evidence

BACKGROUND

Light therapy is an accepted treatment option, at least for seasonal affective disorder (SAD). Our aim was to critically evaluate treatment effects of bright white light (BWL) on the depressive symptoms in both SAD and non-seasonal depression.

METHODS

The systematic review was performed according to the PRISMA guidelines. PubMed, Embase, and PsycINFO were searched (December 1974 through June 2014) for randomized controlled trials published in peer-reviewed journals. Study quality was assessed with a checklist developed by the Swedish Council on Technology Assessment in Health Care. Only studies with high or medium quality were used in the meta-analyses.

RESULTS

Eight studies of SAD and two studies of non-seasonal depression met inclusion and quality criteria. Effects on SAD were estimated in two meta-analyses. In the first, week by week, BWL reached statistical significance only at two and three weeks of treatment (Standardized Mean Difference, SMD: -0.50 (-CI 0.94, -0.05); -0.31 (-0.59, -0.03) respectively). The second meta-analysis, of endpoint data only, showed a SMD of -0.54 (CI: -0.95, -0.13), which indicates an advantage for BWL. No meta-analysis was performed for non-seasonal depression due to heterogeneity between studies.

LIMITATIONS

This analysis is restricted to short-term effects of BWL measured as mean changes in scores derived from SIGH-SAD, SIGH-SAD self-report, or HDRS rating scales.

CONCLUSIONS

Most studies of BWL have considerable methodological problems, and the results of published meta-analyses are highly dependent on the study selection. Even though quality criteria are introduced in the selection procedures of studies, when the results are carefully scrutinized, the evidence is not unequivocal.