Validation of an Electronic Health Record-Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems.

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 Participants: For 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 Relevance: Across 5 diverse health care systems, a computationally efficient approach leveraging the full spectrum of structured electronic health record data was able to detect the risk of suicidal behavior in unselected patients. This approach could facilitate the development of clinical decision support tools that inform risk reduction interventions.

Lactate clearance as a predictor of mortality in trauma patients.

BACKGROUND: Initial serum lactate has been associated with mortality in trauma patients. It is not known if lactate clearance is predictive of death in a broad cohort of trauma patients. METHODS: We enrolled 4,742 trauma patients who had an initial lactate measured during a 10-year period. Patients were identified via the trauma registry. Lactate clearance was calculated at 6 hours. Multivariable logistic regression was used to identify the independent contribution of both initial lactate and lactate clearance with mortality, after adjustment for severity of injury. RESULTS: Initial lactate level was strongly correlated with mortality: when lactate was less than 2.5 mg/dL, 5.4% (95% confidence interval [CI], 4.5-6.2%) of patients died; with lactate 2.5 mg/dL to 4.0 mg/dL, mortality was 6.4% (95% CI, 5.1-7.8%); with lactate 4.0 mg/dL or greater, mortality was 18.8% (95% CI, 15.7-21.9%). After adjustment for age, Injury Severity Score (ISS), Glasgow Coma Scale (GCS) score, heart rate, and blood pressure, initial lactate remained independently associated with increased mortality, with adjusted odds ratios of 1.0, 1.5 (95% CI, 1.1-2.0) and 3.8 (95% CI, 2.8-5.3), for lactate less than 2.5 mg/dL, 2.5 mg/dL to 4.0 mg/dL, and 4.0 mg/dL or greater, respectively. Among patients with an initially elevated lactate (≥4.0 mg/dL), lower lactate clearance at 6 hours strongly and independently predicted an increased risk of death. For lactate clearances of 60% or greater, 30% to 59%, and less than 30%, the adjusted odds ratio for death were 1.0, 3.5 (95% CI 1.2-10.4), and 4.3 (95% CI, 1.5-12.6), respectively. CONCLUSION: Both initial lactate and lactate clearance at 6 hours independently predict death in trauma patients. LEVEL OF EVIDENCE: Prognostic study, level III.