Longitudinal EHR data, commonly available in clinical settings, can be useful for predicting future risk of suicidal behavior. This modeling approach could serve as an early warning system to help clinicians identify high-risk patients for further screening. By analyzing the full phenotypic breadth of the EHR, computerized risk screening approaches may enhance prediction beyond what is feasible for individual clinicians.

Background Studies have shown decreased response to COVID-19 vaccinations in some populations. In addition, it is possible that vaccine-triggered immune activation could trigger immune-dysregulation and thus exacerbate inflammatory bowel diseases (IBD). In this population-based study we used the epi-Israeli IBD Research Nucleus (IIRN) validated cohort to explore the effectiveness of COVID-19 vaccination in IBD and to assess its effect on disease outcomes. Methods We included all IBD patients insured in two of the four Israeli health maintenance organizations (HMOs), covering 35% of the population. Patients receiving two Pfizer-BioNTech BNT162b2 vaccine doses between December 2020 to June 2021 were individually matched to non-IBD controls. To assess IBD outcomes, we matched vaccinated to unvaccinated IBD patients and response was analyzed per medical treatment. Results In total, 12,109 IBD patients received two vaccine doses, of whom 4,946 were matched to non-IBD controls (mean age 51±16 years and median follow-up 22 weeks (interquartile range [IQR], 4-24)). Fifteen patients in each group (0.3%) developed COVID-19 post vaccination (OR=1 [95%CI 0.49-2.05], P =1.0). Patients on tumor necrosis factor (TNF) inhibitors and/or corticosteroids did not have a higher incidence of infection. To explore IBD outcomes, 707 vaccinated IBD patients were compared to unvaccinated IBD patients by stringent matching (median follow-up, 14 weeks [IQR 2.3-20.4]). The risk of exacerbation was 29% in the vaccinated patients compared with 26% in unvaccinated patients ( P =.3). Conclusions COVID-19 vaccine effectiveness in IBD patients is comparable to that in non-IBD controls and is not influenced by treatment with TNF inhibitors or corticosteroids. The IBD exacerbation rate did not differ between vaccinated and unvaccinated patients.

IMPORTANCE Suicide is a leading cause of mortality, with suicide-related deaths increasing in recent years. Automated methods for individualized risk prediction have great potential to address this growing public health threat. To facilitate their adoption, they must first be validated across diverse health care settings.OBJECTIVE To evaluate the generalizability and cross-site performance of a risk prediction method using readily available structured data from electronic health records in predicting incident suicide attempts across multiple, independent, US health care systems. DESIGN, SETTING, AND PARTICIPANTSFor this prognostic study, data were extracted from longitudinal electronic health record data comprising International Classification of Diseases, Ninth Revision diagnoses, laboratory test results, procedures codes, and medications for more than 3.7 million patients from 5 independent health care systems participating in the Accessible Research Commons for Health network. Across sites, 6 to 17 years' worth of data were available, up to 2018. Outcomes were defined by International Classification of Diseases, Ninth Revision codes reflecting incident suicide attempts (with positive predictive value >0.70 according to expert clinician medical record review). Models were trained using naive Bayes classifiers in each of the 5 systems. Models were cross-validated in independent data sets at each site, and performance metrics were calculated. Data analysis was performed from November 2017 to August 2019. MAIN OUTCOMES AND MEASURES The primary outcome was suicide attempt as defined by a previously validated case definition using International Classification of Diseases, Ninth Revision codes. The accuracy and timeliness of the prediction were measured at each site. RESULTS Across the 5 health care systems, of the 3 714 105 patients (2 130 454 female [57.2%])included in the analysis, 39 162 cases (1.1%) were identified. Predictive features varied by site but, as expected, the most common predictors reflected mental health conditions (eg, borderline personality disorder, with odds ratios of 8.1-12.9, and bipolar disorder, with odds ratios of 0.9-9.1) and substance use disorders (eg, drug withdrawal syndrome, with odds ratios of 7.0-12.9). Despite variation in geographical location, demographic characteristics, and population health characteristics, model performance was similar across sites, with areas under the curve ranging from 0.71 (95% CI, 0.70-0.72) to 0.76 (95% CI, 0.75-0.77). Across sites, at a specificity of 90%, the models detected a mean of 38% of cases a mean of 2.1 years in advance. CONCLUSIONS AND RELEVANCEAcross 5 diverse health care systems, a computationally efficient approach leveraging the full spectrum of structured electronic health record data was able to detect (continued) Key Points Question Can a process for training machine-learning algorithms based on electronic health records identify individuals at increased risk of suicide attempts across independent health care systems? Abstract (contin...

Data collected by EDs in electronic medical records can be used within a progressive modelling framework to predict patient flow and improve clinical operations. This approach relies on commonly available data and can be applied across different healthcare settings.

This approach can accurately predict patient hospitalization early in the ED encounter by using data commonly available in most electronic medical records. Such early identification can be used to advance patient placement processes and shorten ED boarding times.

Key Points Question What is the best method to predict which patients presenting to the emergency department will make a suicide attempt within 1 and 6 months after the visit? Findings This prognostic study of 1818 patients found that prediction of suicide attempts in the 1 month and 6 months after a patient visited an emergency department was significantly improved using machine learning models applied to data from a brief patient self-report scale, especially when supplemented with data from patients’ electronic health records and/or clinicians’ assessments. Meaning This study suggests that clinicians can improve their ability to identify patients at high risk of suicide by using data from a brief patient self-report scale and electronic health records.

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