To assess cost-effectiveness of late time-window endovascular treatment (EVT) in a clinical trial setting and a “real-world” setting.

Data are from the randomized ESCAPE trial and a prospective cohort study (ESCAPE-LATE). Anterior circulation large vessel occlusion patients presenting > 6 hours from last-known-well were included, whereby collateral status was an inclusion criterion for ESCAPE but not ESCAPE-LATE. A Markov state transition model was built to estimate lifetime costs and quality-adjusted life-years (QALYs) for EVT in addition to best medical care vs. best medical care only in a clinical trial setting (comparing ESCAPE-EVT to ESCAPE control arm patients) and a “real-world” setting (comparing ESCAPE-LATE to ESCAPE control arm patients). We performed an unadjusted analysis, using 90-day modified Rankin Scale(mRS) scores as model input and analysis adjusted for baseline factors. Acceptability of EVT was calculated using upper/lower willingness-to-pay thresholds of 100,000 USD/50,000 USD/QALY.

Two-hundred and forty-nine patients were included (ESCAPE-LATE:n = 200, ESCAPE EVT-arm:n = 29, ESCAPE control-arm:n = 20). Late EVT in addition to best medical care was cost effective in the unadjusted analysis both in the clinical trial and real-world setting, with acceptability 96.6%–99.0%. After adjusting for differences in baseline variables between the groups, late EVT was marginally cost effective in the clinical trial setting (acceptability:49.9%–61.6%), but not the “real-world” setting (acceptability:32.9%–42.6%).

EVT for LVO-patients presenting beyond 6 hours was cost effective in the clinical trial setting and “real-world” setting, although this was largely related to baseline patient differences favoring the “real-world” EVT group. After adjusting for these, EVT benefit was reduced in the trial setting, and absent in the real-world setting.

Cognitive dysfunction is recognized as a core feature of schizophrenia and is considered an important predictor of functional outcomes. Despite this, current treatment strategies largely fail to ameliorate these cognitive impairments. In order to develop more efficient treatment strategies, a better understanding of the pathogenesis of cognitive dysfunction is needed. Accumulating evidence indicates that genetic risk of schizophrenia contributes to cognitive dysfunction. However, the precise genetic variants jointly influencing schizophrenia and cognitive function remain to be determined.

Here, we aimed to identify gene loci shared between schizophrenia and general cognitive function, a phenotype that captures the shared variation in performance across several cognitive domains.

Using a Bayesian statistical framework, we compared genome-wide association study (GWAS) data on schizophrenia from the Psychiatric Genomics Consortium cohort (n = 79,757) with GWAS data on general cognitive function from the CHARGE Consortium (n = 53,949). By conditioning the false discovery rate (FDR) on shared associations, this statistical approach increases power to detect gene loci.

We observed substantial polygenetic overlap between schizophrenia and general cognitive function, which replicated across independent schizophrenia sub-studies. Using the conditional FDR approach we increased discovery of gene loci and identified 13 loci shared between schizophrenia and general cognitive function. The majority of these loci (11/13) shows opposite directions of allelic effects in the phenotypes, in line with previous genetic studies and the observed cognitive dysfunction in schizophrenia.

Our study extends the current understanding of the genetic etiology influencing schizophrenia and general cognitive function by identifying shared gene loci between the phenotypes.

The authors have not supplied their declaration of competing interest.