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Predicting which patient(s) will respond to Electroconvulsive Therapy (ECT) has very important clinical implications. The aim of this manuscript is to review the current literature on clinical, physiologic and laboratory biomarkers as predictors of ECT response and remission related to the treatment of major depressive disorder (MDD). We will briefly discuss available research on the predictors of cognitive side effects of ECT. Although each clinical factor may have subtle influence on ECT response, taken together clinical predictors can lead to a robust treatment plan tailored for an individual patient, and advise on the likelihood of ECT response. Available literature supports the predictive value of several clinical factors. Older age, psychotic depression, and depression severity positively predict ECT response. Limited data is available for catatonia specific to MDD, but overall data shows positive response of ECT for the treatment of catatonia. Multiple medication trials in the current episode and comorbid psychiatric diagnosis (including borderline personality disorder and substance use disorder) predict lower response. Lack of widespread clinical availability and validation in larger studies limits current clinical utility of laboratory and physiologic biomarkers. Genetic, epigenetic, and proteomic factors have been investigated predominately in animal models, but ongoing research in human studies including neuroimaging is promising. Thus, these biomarkers provide an exciting outlook that may elevate the precision of ECT response and remission.

IntroductionIdentifying design features and implementation strategies to optimise community health worker (CHW) programmes is important in the context of mixed results at scale. We systematically reviewed evidence of the effects of proactive case detection by CHWs in low-income and middle-income countries (LMICs) on mortality, morbidity and access to care for common childhood illnesses.MethodsPublished studies were identified via electronic databases from 1978 to 2017. We included randomised and non-randomised controlled trials, controlled before–after studies and interrupted time series studies, and assessed their quality for risk of bias. We reported measures of effect as study investigators reported them, and synthesised by outcomes of mortality, disease prevalence, hospitalisation and access to treatment. We calculated risk ratios (RRs) as a principal summary measure, with CIs adjusted for cluster design effect.ResultsWe identified 14 studies of 11 interventions from nine LMICs that met inclusion criteria. They showed considerable diversity in intervention design and implementation, comparison, outcomes and study quality, which precluded meta-analysis. Proactive case detection may reduce infant mortality (RR: 0.52–0.94) and increase access to effective treatment (RR: 1.59–4.64) compared with conventional community-based healthcare delivery (low certainty evidence). It is uncertain whether proactive case detection reduces mortality among children under 5 years (RR: 0.04–0.80), prevalence of infectious diseases (RR: 0.06–1.02), hospitalisation (RR: 0.38–1.26) or increases access to prompt treatment (RR: 1.00–2.39) because the certainty of this evidence is very low.ConclusionProactive case detection may provide promising benefits for child health, but evidence is insufficient to draw conclusions. More research is needed on proactive case detection with rigorous study designs that use standardised outcomes and measurement methods, and report more detail on complex intervention design and implementation.PROSPERO registration numberCRD42017074621.