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.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Delays in triage processes in the emergency department (ED) can compromise patient safety. The aim of this study was to provide proof-of-concept that a self-check-in kiosk could decrease the time needed to identify ambulatory patients arriving in the ED. We compared the use of a novel automated self-check-in kiosk to identify patients on ED arrival to routine nurse-initiated patient identification.

We performed a prospective trail with random weekly allocation to intervention or control processes during a 10-week study period. During intervention weeks, patients used a self-check-in kiosk to self-identify on arrival. This electronically alerted triage nurses to patient arrival times and primary complaint before triage. During control weeks, kiosks were unavailable and patients were identified using routine nurse-initiated triage. The primary outcome was time-to-first-identification, defined as the interval between ED arrival and identification in the hospital system.

Median (interquartile range) time-to-first-identification was 1.4 minutes (1.0–2.08) for intervention patients and 9 minutes (5–18) for control patients. Regression analysis revealed that the adjusted time-to-first-identification was 13.6 minutes (95% confidence interval 12.8–14.5) faster for the intervention group.

A self-check-in kiosk significantly reduced the time-to-first-identification for ambulatory patients arriving in the ED.

The aims of this study were to identify locations of births in Arizona with critical CHD, as well as to assess the current use of pulse-oximetry screening and capacities of birth centres to manage a positive screen.

Infants (n=487) with a potentially critical CHD were identified from the Arizona Department of Health Services from 2012 and 2013; charts were retrospectively reviewed. Diagnosis was confirmed using echocardiographies. ArcGIS was used to generate maps to visualise the location of treating facility and mother’s residence. Birth centres were surveyed to assess screening practices and capacities to manage critical CHD in 2015.

Of the 272 patients identified with critical CHD, 52% had been diagnosed prenatally. Patients travelled an average distance of 55.1 miles to their treating facility. Mortality was not related to prenatal diagnosis (p=0.30), living at a high elevation (p=0.82), or to distance travelled to the treating facility (p=0.68). Of 50 birth centres, 33 responded to the survey and all centres practiced critical CHD screening. A total of 25 centres could perform paediatric echocardiographies; 64% of these centres could digitally transmit echocardiograms. In all, 24 birth centres maintained access to prostaglandins.

Pulse-oximetry screening in newborns is currently implemented in the majority of Arizona hospitals. Although most centres could perform initial management steps following a positive screen, access to paediatric cardiology services was limited. Patients with critical CHD sometimes travelled a great distance to treating facilities. Digital transmission of echocardiograms or tele-echocardiography would reduce the distance travelled for the management of a positive screen, decrease the financial burden of transportation, and expedite care for critically ill neonates.