Several hypotheses may explain the association between substance use, posttraumatic stress disorder (PTSD), and depression. However, few studies have utilized a large multisite dataset to understand this complex relationship. Our study assessed the relationship between alcohol and cannabis use trajectories and PTSD and depression symptoms across 3 months in recently trauma-exposed civilians.

In total, 1618 (1037 female) participants provided self-report data on past 30-day alcohol and cannabis use and PTSD and depression symptoms during their emergency department (baseline) visit. We reassessed participant's substance use and clinical symptoms 2, 8, and 12 weeks posttrauma. Latent class mixture modeling determined alcohol and cannabis use trajectories in the sample. Changes in PTSD and depression symptoms were assessed across alcohol and cannabis use trajectories via a mixed-model repeated-measures analysis of variance.

Three trajectory classes (low, high, increasing use) provided the best model fit for alcohol and cannabis use. The low alcohol use class exhibited lower PTSD symptoms at baseline than the high use class; the low cannabis use class exhibited lower PTSD and depression symptoms at baseline than the high and increasing use classes; these symptoms greatly increased at week 8 and declined at week 12. Participants who already use alcohol and cannabis exhibited greater PTSD and depression symptoms at baseline that increased at week 8 with a decrease in symptoms at week 12.

Our findings suggest that alcohol and cannabis use trajectories are associated with the intensity of posttrauma psychopathology. These findings could potentially inform the timing of therapeutic strategies.

Delayed cerebral ischemia (DCI) is a complication of aneurysmal subarachnoid hemorrhage (aSAH) and is associated with significant morbidity and mortality. There is little high-quality evidence available to guide the management of DCI. The Canadian Neurosurgery Research Collaborative (CNRC) is comprised of resident physicians who are positioned to capture national, multi-site data. The objective of this study was to evaluate practice patterns of Canadian physicians regarding the management of aSAH and DCI.

We performed a cross-sectional survey of Canadian neurosurgeons, intensivists, and neurologists who manage aSAH. A 19-question electronic survey (Survey Monkey) was developed and validated by the CNRC following a DCI-related literature review (PubMed, Embase). The survey was distributed to members of the Canadian Neurosurgical Society and to Canadian members of the Neurocritical Care Society. Responses were analyzed using quantitative and qualitative methods.

The response rate was 129/340 (38%). Agreement among respondents was limited to the need for intensive care unit admission, use of clinical and radiographic monitoring, and prophylaxis for the prevention of DCI. Several inconsistencies were identified. Indications for starting hyperdynamic therapy varied. There was discrepancy in the proportion of patients who felt to require IV milrinone, IA vasodilators, or physical angioplasty for treatment of DCI. Most respondents reported their facility does not utilize a standardized definition for DCI.

DCI is an important clinical entity for which no homogeneity and standardization exists in management among Canadian practitioners. The CNRC calls for the development of national standards in the definition, identification, and treatment of DCI.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

To assess the mental health of pregnant women, with reference to anxiety, depression and obsessive-compulsive (OC) symptoms, during the COVID-19 pandemic.

A cross-sectional survey was conducted in Ireland during the third wave of the pandemic between February and March 2021. Psychiatric, social and obstetric information was collected from pregnant women in a Dublin maternity hospital, alongside self-reported measures of mental health status.

Of 392 women responding, 23.7% had anxiety, scoring >9 for GAD-7 (7-item generalised anxiety disorder), 20.4% had depression, scoring >9 for PHQ-9 (9-item depression screening tool: Patient health questionnaire) and 10.3% had obsessive-compulsive disorder (OCD), scoring >13 for Yale–Brown obsessive-compulsive scale symptom checklist (Y-BOCS). Amongst self-reported OCD symptoms, there was a preponderance for obsessions rather than compulsions. Of 392 women, 36.2% described their mental health as worse during the pandemic, most frequently describing symptoms of anxiety and sleep disturbance. When analysed against test scores, self-reported worsening of mental health was significantly associated with higher scores on the GAD-7, PHQ-9 and Y-BOCS scales. The three scores were positively interrelated. Poor mental health scores were associated with self-reported strain in relationship with the baby’s father, and current or previous history of mental illness.

This study found high levels of depression, anxiety and OC symptoms amongst pregnant women during COVID-19. This highlights the vulnerability of this group to mental illness and the importance of enhanced screening and support during pandemics.

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.

An accurate estimate of the average number of hand hygiene opportunities per patient hour (HHO rate) is required to implement group electronic hand hygiene monitoring systems (GEHHMSs). We sought to identify predictors of HHOs to validate and implement a GEHHMS across a network of critical care units.

Multicenter, observational study (10 hospitals) followed by quality improvement intervention involving 24 critical care units across 12 hospitals in Ontario, Canada.

Critical care patient beds were randomized to receive 1 hour of continuous direct observation to determine the HHO rate. A Poisson regression model determined unit-level predictors of HHOs. Estimates of average HHO rates across different types of critical care units were derived and used to implement and evaluate use of GEHHMS.

During 2,812 hours of observation, we identified 25,417 HHOs. There was significant variability in HHO rate across critical care units. Time of day, day of the week, unit acuity, patient acuity, patient population and use of transmission-based precautions were significantly associated with HHO rate. Using unit-specific estimates of average HHO rate, aggregate HH adherence was 30.0% (1,084,329 of 3,614,908) at baseline with GEHHMS and improved to 38.5% (740,660 of 1,921,656) within 2 months of continuous feedback to units (P < .0001).

Unit-specific estimates based on known predictors of HHO rate enabled broad implementation of GEHHMS. Further longitudinal quality improvement efforts using this system are required to assess the impact of GEHHMS on both HH adherence and clinical outcomes within critically ill patient populations.