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Citation
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Fisher LB, Curtiss JE, Klyce DW, Perrin PB, Juengst SB, Gary KW, Niemeier JP, Hammond FMC, Bergquist TF, Wagner AK, Rabinowitz AR, Giacino JT, Zafonte RD. Am. J. Phys. Med. Rehabil. 2022; ePub(ePub): ePub. |
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Abstract
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OBJECTIVE: To predict suicidal ideation one year after moderate to severe traumatic brain injury (TBI).
DESIGN: Cross-sectional design with data collected through the prospective, longitudinal TBI Model Systems (TBIMS) network at hospitalization and one year after injury. Participants who completed the Patient Health Questionnaire-9 (PHQ-9) suicide item at year one follow-up (N = 4,328) were included.
RESULTS: A gradient boosting machine (GBM) algorithm demonstrated the best performance in predicting suicidal ideation one year after TBI. Predictors were PHQ-9 items (except suicidality), Generalized Anxiety Disorder-7 (GAD-7) items, and a measure of heavy drinking.
RESULTS of the 10-fold cross-validation GBM analysis indicated excellent classification performance with an AUC of 0.882. Sensitivity was 0.85, and specificity was 0.77. Accuracy was 0.78 (95% CI: 0.77 - 0.79). Feature importance analyses revealed that depressed mood and guilt were the most important predictors of suicidal ideation, followed by anhedonia, concentration difficulties, and psychomotor disturbance.
CONCLUSIONS: Overall, depression symptoms were most predictive of suicidal ideation. Despite the limited clinical impact of the present findings, machine learning has potential to improve prediction of suicidal behavior, leveraging electronic health record data, to identify individuals at greatest risk, thereby facilitating intervention and optimization of long-term outcomes following TBI.
Language: en |