Abstract
Numerous prognostic models of suicide risk have been published, but few have been implemented outside of integrated managed care systems. To evaluate performance of a suicide attempt risk prediction model implemented in a vendor-supplied electronic health record to predict subsequent (1) suicidal ideation and (2) suicide attempt. This observational cohort study evaluated implementation of a suicide attempt prediction model in live clinical systems without alerting. The cohort comprised patients seen for any reason in adult inpatient, emergency department, and ambulatory surgery settings at an academic medical center in the mid-South from June 2019 to April 2020. Primary measures assessed external, prospective, and concurrent validity. Manual medical record validation of coded suicide attempts confirmed incident behaviors with intent to die. Subgroup analyses were performed based on demographic characteristics, relevant clinical context/setting, and presence or absence of universal screening. Performance was evaluated using discrimination (number needed to screen, C statistics, positive/negative predictive values) and calibration (Spiegelhalter z statistic). Recalibration was performed with logistic calibration. The system generated 115 905 predictions for 77 973 patients (42 490 [54%] men, 35 404 [45%] women, 60 586 [78%] White, 12 620 [16%] Black). Numbers needed to screen in highest risk quantiles were 23 and 271 for suicidal ideation and attempt, respectively. Performance was maintained across demographic subgroups. Numbers needed to screen for suicide attempt by sex were 256 for men and 323 for women; and by race: 373, 176, and 407 for White, Black, and non-White/non-Black patients, respectively. Model C statistics were, across the health system: 0.836 (95% CI, 0.836-0.837); adult hospital: 0.77 (95% CI, 0.77-0.772); emergency department: 0.778 (95% CI, 0.777-0.778); psychiatry inpatient settings: 0.634 (95% CI, 0.633-0.636). Predictions were initially miscalibrated (Spiegelhalter z = -3.1; P = .001) with improvement after recalibration (Spiegelhalter z = 1.1; P = .26). In this study, this real-time predictive model of suicide attempt risk showed reasonable numbers needed to screen in nonpsychiatric specialty settings in a large clinical system. Assuming that research-valid models will translate without performing this type of analysis risks inaccuracy in clinical practice, misclassification of risk, wasted effort, and missed opportunity to correct and prevent such problems. The next step is careful pairing with low-cost, low-harm preventive strategies in a pragmatic trial of effectiveness in preventing future suicidality.
Highlights
Suicide prevention begins with risk identification and prognostication
Predictions were initially miscalibrated (Spiegelhalter z = −3.1; P = .001) with improvement after recalibration (Spiegelhalter z = 1.1; P = .26). In this study, this real-time predictive model of suicide attempt risk showed reasonable numbers needed to screen in nonpsychiatric specialty settings in a large clinical system
Assuming that research-valid models will translate without performing this type of analysis risks inaccuracy in clinical practice, misclassification of risk, wasted effort, and missed opportunity to correct and prevent such problems
Summary
Suicide prevention begins with risk identification and prognostication. The standard of care remains face-to-face screening and routine clinical interaction. Rates of suicidal ideation, attempts, and deaths continue to rise internationally despite increased monitoring and intervention efforts.[1] The coronavirus disease 2019 (COVID-19) pandemic exacerbated contributing factors for suicide and will continue to do so in the post–COVID-19 era.[2,3,4] Numerous prognostic models of suicide risk have been published.[5] Few have been implemented into real-world clinical systems outside of integrated managed care settings.[5,6,7] In some settings, universal screening might reduce risk of downstream suicidality.[8] But in-person screening takes time and attention and can be conducted with variable quality.[9] Concealed distress subverts risk identification in face-to-face screening.[10] those at risk might not be identified despite health care encounters as recently as the day they die from suicide.[11,12,13]
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