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.

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.

The effectiveness of mechanical thrombectomy (MT) in elderly stroke patients remains debated. We aimed to describe outcomes and their predictors in a cohort of patients aged ≥ 85 years treated with MT.

Data from consecutive patients aged ≥ 85 years undergoing MT at two stroke centers between January 2016 and November 2019 were reviewed. Admission National Institutes of Health Stroke Scale (NIHSS), pre-stroke, and 3-month modified Rankin scale (mRS) were collected. Successful recanalization was defined as modified thrombolysis in cerebral ischemia score ≥ 2b. Good outcome was defined as mRS 0–3 or equal to pre-stroke mRS at 3 months.

Of 151 included patients, successful recanalization was achieved in 74.2%. At 3 months, 44.7% of patients had a good outcome and 39% had died. Any intracranial hemorrhage (ICH) and symptomatic ICH occurred in 20.3% and 3.6%, respectively. Logistic regression analysis identified lower pre-stroke mRS score (adjusted odds ratio [aOR], 0.52; 95% CI, 0.36–0.76), lower admission NIHSS score (aOR, 0.90; 95% CI, 0.83–0.97), successful recanalization (aOR, 3.65; 95% CI, 1.32–10.09), and absence of ICH on follow-up imaging (aOR, 0.42; 95% CI, 0.08–0.75), to be independent predictors of good outcome. Patients with successful recanalization had a higher proportion of good outcome (45.3% vs 34.3%, p = 0.013) and lower mortality at 3 months (35.8% vs 48.6%, p = 0.006) compared to patients with unsuccessful recanalization.

Among patients aged ≥ 85 years, successful recanalization with MT is relatively common and associated with better 3-month outcome and lower mortality than failed recanalization. Attempting to achieve recanalization in elderly patients using MT appears reasonable.

Simulation plays an integral role in the Canadian healthcare system with applications in quality improvement, systems development, and medical education. High-quality, simulation-based research will ensure its effective use. This study sought to summarize simulation-based research activity and its facilitators and barriers, as well as establish priorities for simulation-based research in Canadian emergency medicine (EM).

Simulation-leads from Canadian departments or divisions of EM associated with a general FRCP-EM training program surveyed and documented active EM simulation-based research at their institutions and identified the perceived facilitators and barriers. Priorities for simulation-based research were generated by simulation-leads via a second survey; these were grouped into themes and finally endorsed by consensus during an in-person meeting of simulation leads. Priority themes were also reviewed by senior simulation educators.

Twenty simulation-leads representing all 14 invited institutions participated in the study between February and May, 2018. Sixty-two active, simulation-based research projects were identified (median per institution = 4.5, IQR 4), as well as six common facilitators and five barriers. Forty-nine priorities for simulation-based research were reported and summarized into eight themes: simulation in competency-based medical education, simulation for inter-professional learning, simulation for summative assessment, simulation for continuing professional development, national curricular development, best practices in simulation-based education, simulation-based education outcomes, and simulation as an investigative methodology.

This study summarized simulation-based research activity in EM in Canada, identified its perceived facilitators and barriers, and built national consensus on priority research themes. This represents the first step in the development of a simulation-based research agenda specific to Canadian EM.