Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Obesity increases the risk of post-operative arrhythmias in adults undergoing cardiac surgery, but little is known regarding the impact of obesity on post-operative arrhythmias after CHD surgery.

Patients undergoing CHD surgery from 2007 to 2019 were prospectively enrolled in the parent study. Telemetry was assessed daily, with documentation of all arrhythmias. Patients aged 2–20 years were categorised by body mass index percentile for age and sex (underweight <5, normal 5–85, overweight 85–95, and obese >95). Patients aged >20 years were categorised using absolute body mass index. We investigated the impact of body mass index category on arrhythmias using univariate and multivariate analysis.

There were 1250 operative cases: 12% underweight, 65% normal weight, 12% overweight, and 11% obese. Post-operative arrhythmias were observed in 38%. Body mass index was significantly higher in those with arrhythmias (18.8 versus 17.8, p = 0.003). There was a linear relationship between body mass index category and incidence of arrhythmias: underweight 33%, normal 38%, overweight 42%, and obese 45% (p = 0.017 for trend). In multivariate analysis, body mass index category was independently associated with post-operative arrhythmias (p = 0.021), with odds ratio 1.64 in obese patients as compared to normal-weight patients (p = 0.036). In addition, aortic cross-clamp time (OR 1.007, p = 0.002) and maximal vasoactive–inotropic score in the first 48 hours (OR 1.03, p = 0.04) were associated with post-operative arrhythmias.

Body mass index is independently associated with incidence of post-operative arrhythmias in children after CHD surgery.

Refugees typically spend years in a state of protracted displacement prior to permanent resettlement. Little is known about how various prior displacement contexts influence long-term mental health in resettled refugees. In this study, we aimed to determine whether having lived in refugee camps v. community settings prior to resettlement impacted the course of refugees' psychological distress over the 4 years following arrival in Australia.

Participants were 1887 refugees who had taken part in the Building a New Life in Australia study, which comprised of five annual face-to-face or telephone surveys from the year of first arrival in Australia.

Latent growth curve modelling revealed that refugees who had lived in camps showed greater initial psychological distress (as indexed by the K6) and faster decreases in psychological distress in the 4 years after resettling in Australia, compared to those who had lived in community settings. Investigation of refugee camp characteristics revealed that poorer access to services in camps was associated with greater initial distress after resettlement, and greater ability to meet one's basic needs in camps was associated with faster decreases in psychological distress over time.

These findings highlight the importance of the displacement context in influencing the course of post-resettlement mental health. Increasing available services and meeting basic needs in the displacement environment may promote better mental health outcomes in resettled refugees.

Retrospective self-report is typically used for diagnosing previous pediatric traumatic brain injury (TBI). A new semi-structured interview instrument (New Mexico Assessment of Pediatric TBI; NewMAP TBI) investigated test–retest reliability for TBI characteristics in both the TBI that qualified for study inclusion and for lifetime history of TBI.

One-hundred and eight-four mTBI (aged 8–18), 156 matched healthy controls (HC), and their parents completed the NewMAP TBI within 11 days (subacute; SA) and 4 months (early chronic; EC) of injury, with a subset returning at 1 year (late chronic; LC).

The test–retest reliability of common TBI characteristics [loss of consciousness (LOC), post-traumatic amnesia (PTA), retrograde amnesia, confusion/disorientation] and post-concussion symptoms (PCS) were examined across study visits. Aside from PTA, binary reporting (present/absent) for all TBI characteristics exhibited acceptable (≥0.60) test–retest reliability for both Qualifying and Remote TBIs across all three visits. In contrast, reliability for continuous data (exact duration) was generally unacceptable, with LOC and PCS meeting acceptable criteria at only half of the assessments. Transforming continuous self-report ratings into discrete categories based on injury severity resulted in acceptable reliability. Reliability was not strongly affected by the parent completing the NewMAP TBI.

Categorical reporting of TBI characteristics in children and adolescents can aid clinicians in retrospectively obtaining reliable estimates of TBI severity up to a year post-injury. However, test–retest reliability is strongly impacted by the initial data distribution, selected statistical methods, and potentially by patient difficulty in distinguishing among conceptually similar medical concepts (i.e., PTA vs. confusion).

This study aimed to investigate general factors associated with prognosis regardless of the type of treatment received, for adults with depression in primary care.

We searched Medline, Embase, PsycINFO and Cochrane Central (inception to 12/01/2020) for RCTs that included the most commonly used comprehensive measure of depressive and anxiety disorder symptoms and diagnoses, in primary care depression RCTs (the Revised Clinical Interview Schedule: CIS-R). Two-stage random-effects meta-analyses were conducted.

Twelve (n = 6024) of thirteen eligible studies (n = 6175) provided individual patient data. There was a 31% (95%CI: 25 to 37) difference in depressive symptoms at 3–4 months per standard deviation increase in baseline depressive symptoms. Four additional factors: the duration of anxiety; duration of depression; comorbid panic disorder; and a history of antidepressant treatment were also independently associated with poorer prognosis. There was evidence that the difference in prognosis when these factors were combined could be of clinical importance. Adding these variables improved the amount of variance explained in 3–4 month depressive symptoms from 16% using depressive symptom severity alone to 27%. Risk of bias (assessed with QUIPS) was low in all studies and quality (assessed with GRADE) was high. Sensitivity analyses did not alter our conclusions.

When adults seek treatment for depression clinicians should routinely assess for the duration of anxiety, duration of depression, comorbid panic disorder, and a history of antidepressant treatment alongside depressive symptom severity. This could provide clinicians and patients with useful and desired information to elucidate prognosis and aid the clinical management of depression.