Appl Clin Inform 2021; 12(04): 778-787
DOI: 10.1055/s-0041-1733908
Research Article

Evaluation of Electronic Health Record-Based Suicide Risk Prediction Models on Contemporary Data

Rod L. Walker
1  Kaiser Permanente Washington Health Research Institute, Seattle, Washington, United States
,
Susan M. Shortreed
1  Kaiser Permanente Washington Health Research Institute, Seattle, Washington, United States
,
Rebecca A. Ziebell
1  Kaiser Permanente Washington Health Research Institute, Seattle, Washington, United States
,
Eric Johnson
1  Kaiser Permanente Washington Health Research Institute, Seattle, Washington, United States
,
Jennifer M. Boggs
2  Kaiser Permanente Colorado, Institute for Health Research, Aurora, Colorado, United States
,
Frances L. Lynch
3  Kaiser Permanente Northwest, Center for Health Research, Portland, Oregon, United States
,
Yihe G. Daida
4  Kaiser Permanente Hawaii, Center for Integrated Health Care Research, Honolulu, Hawaii, United States
,
Brian K. Ahmedani
5  Henry Ford Health System, Center for Health Policy & Health Services Research, Detroit, Michigan, United States
,
Rebecca Rossom
6  Department of Research, HealthPartners Institute, Minneapolis, Minnesota, United States
,
Karen J. Coleman
7  Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States
,
Gregory E. Simon
1  Kaiser Permanente Washington Health Research Institute, Seattle, Washington, United States
› Author Affiliations
Funding This work was supported by cooperative agreements U19 MH092201 and U19 MH121738 with the National Institute of Mental Health.

Abstract

Background Suicide risk prediction models have been developed by using information from patients' electronic health records (EHR), but the time elapsed between model development and health system implementation is often substantial. Temporal changes in health systems and EHR coding practices necessitate the evaluation of such models in more contemporary data.

Objectives A set of published suicide risk prediction models developed by using EHR data from 2009 to 2015 across seven health systems reported c-statistics of 0.85 for suicide attempt and 0.83 to 0.86 for suicide death. Our objective was to evaluate these models' performance with contemporary data (2014–2017) from these systems.

Methods We evaluated performance using mental health visits (6,832,439 to mental health specialty providers and 3,987,078 to general medical providers) from 2014 to 2017 made by 1,799,765 patients aged 13+ across the health systems. No visits in our evaluation were used in the previous model development. Outcomes were suicide attempt (health system records) and suicide death (state death certificates) within 90 days following a visit. We assessed calibration and computed c-statistics with 95% confidence intervals (CI) and cut-point specific estimates of sensitivity, specificity, and positive/negative predictive value.

Results Models were well calibrated; 46% of suicide attempts and 35% of suicide deaths in the mental health specialty sample were preceded by a visit (within 90 days) with a risk score in the top 5%. In the general medical sample, 53% of attempts and 35% of deaths were preceded by such a visit. Among these two samples, respectively, c-statistics were 0.862 (95% CI: 0.860–0.864) and 0.864 (95% CI: 0.860–0.869) for suicide attempt, and 0.806 (95% CI: 0.790–0.822) and 0.804 (95% CI: 0.782–0.829) for suicide death.

Conclusion Performance of the risk prediction models in this contemporary sample was similar to historical estimates for suicide attempt but modestly lower for suicide death. These published models can inform clinical practice and patient care today.

Protection of Human and Subjects Protections

This research study was performed in compliance with the responsible institutional review boards for each health system that approved use of de-identified records data for the study.


Supplementary Material



Publication History

Received: 16 February 2021

Accepted: 01 July 2021

Publication Date:
18 August 2021 (online)

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