Prenatal and perinatal risk factors for bipolar disorder: A systematic review and meta-analysis.

BACKGROUND: Perinatal and prenatal risk factors may be implicated in the development of bipolar disorder, but literature lacks a comprehensive account of possible associations. METHODS: We performed a systematic review and meta-analyses of observational studies detailing the association between prenatal and perinatal risk factors and bipolar disorder in adulthood by searching PubMed, Embase, Web of Science and Psycinfo for articles published in any language between January 1st, 1960 and September 20th, 2021. Meta-analyses were performed when risk factors were available in at least two studies. FINDINGS: Twenty seven studies were included with 18 prenatal or perinatal factors reported across the literature. Peripartum asphyxia (k = 5, OR = 1.46 [1.02; 2.11]), maternal stress during pregnancy (k = 2, OR = 12.00 [3.30; 43.59]), obstetric complications (k = 6, OR = 1.41 [1.18; 1.69]), and birth weight less than 2500 g (k = 5, OR = 1.28 [1.04; 1.56]) were associated with an increased risk for bipolar disorder. INTERPRETATION: Perinatal and prenatal risk factors are implicated in the pathogenesis of bipolar disorder, supporting a role of prenatal care in preventing the condition.

Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis.

Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022. We included 15 studies that predicted treatment responses among patients with major depressive disorder using machine-learning techniques. Within a random-effects model with a restricted maximum likelihood estimator comprising 758 patients, the pooled accuracy across studies was 83.93% (95% CI: 78.90-89.29), with an Area-Under-the-Curve (AUC) of 0.850 (95% CI: 0.747-0.890), and partial AUC of 0.779. The average sensitivity and specificity across models were 77.96% (95% CI: 60.05-88.70), and 84.60% (95% CI: 67.89-92.39), respectively. In a subgroup analysis, greater performance was observed in predicting response to rTMS (Pooled accuracy: 85.70% (95% CI: 77.45-94.83), Area-Under-the-Curve (AUC): 0.928, partial AUC: 0.844), relative to antidepressants (Pooled accuracy: 81.41% (95% CI: 77.45-94.83, AUC: 0.895, pAUC: 0.821). Furthermore, across all meta-analyses, the specificity (true negatives) of EEG models was greater than the sensitivity (true positives), suggesting that EEG models thus far better identify non-responders than responders to treatment in MDD. Studies varied widely in important features across models, although relevant features included absolute and relative power in frontal and temporal electrodes, measures of connectivity, and asymmetry across hemispheres. Predictive models of treatment response using EEG hold promise in major depressive disorder, although there is a need for prospective model validation in independent datasets, and a greater emphasis on replicating physiological markers. Crucially, standardization in cut-off values and clinical scales for defining clinical response and non-response will aid in the reproducibility of findings and the clinical utility of predictive models. Furthermore, several models thus far have used data from open-label trials with small sample sizes and evaluated performance in the absence of training and testing sets, which increases the risk of statistical overfitting. Large consortium studies are required to establish predictive signatures of treatment response using EEG, and better elucidate the replicability of specific markers. Additionally, it is speculated that greater performance was observed in rTMS models, since EEG is assessing neural networks more likely to be directly targeted by rTMS, comprising electrical activity primarily near the surface of the cortex. Prospectively, there is a need for models that examine the comparative effectiveness of multiple treatments across the same patients. However, this will require a thoughtful consideration towards cumulative treatment effects, and whether washout periods between treatments should be utilised. Regardless, longitudinal cross-over trials comparing multiple treatments across the same group of patients will be an important prerequisite step to both facilitate precision psychiatry and identify generalizable physiological predictors of response between and across treatment options.