Importance of variables from different time frames for predicting self-harm using health system data.

Autor:	Wolock CJ; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania., Williamson BD; Kaiser Permanente Washington Health Research Institute.; Department of Biostatistics, University of Washington., Shortreed SM; Kaiser Permanente Washington Health Research Institute.; Department of Biostatistics, University of Washington., Simon GE; Kaiser Permanente Washington Health Research Institute.; Department of Health Systems Science, Bernard J. Tyson Kaiser Permanente School of Medicine., Coleman KJ; Department of Health Systems Science, Bernard J. Tyson Kaiser Permanente School of Medicine.; Department of Research and Evaluation, Kaiser Permanente Southern California., Yeargans R; Department of Research and Evaluation, Kaiser Permanente Southern California., Ahmedani BK; Center for Health Policy and Health Services Research, Henry Ford Health., Daida Y; Center for Integrated Health Care Research, Kaiser Permanente Hawaii., Lynch FL; Center for Health Research, Kaiser Permanente Northwest., Rossom RC; HealthPartners Institute., Ziebell RA; Kaiser Permanente Washington Health Research Institute., Cruz M; Kaiser Permanente Washington Health Research Institute.; Department of Biostatistics, University of Washington., Wellman RD; Kaiser Permanente Washington Health Research Institute., Coley RY; Kaiser Permanente Washington Health Research Institute.; Department of Biostatistics, University of Washington.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2024 Sep 20. Date of Electronic Publication: 2024 Sep 20.
DOI:	10.1101/2024.04.29.24306260
Abstrakt:	Objective: Self-harm risk prediction models developed using health system data (electronic health records and insurance claims information) often use patient information from up to several years prior to the index visit when the prediction is made. Measurements from some time periods may not be available for all patients. Using the framework of algorithm-agnostic variable importance, we study the predictive potential of variables corresponding to different time horizons prior to the index visit and demonstrate the application of variable importance techniques in the biomedical informatics setting. Materials and Methods: We use variable importance to quantify the potential of recent (up to three months before the index visit) and distant (more than one year before the index visit) patient mental health information for predicting self-harm risk using data from seven health systems. We quantify importance as the decrease in predictiveness when the variable set of interest is excluded from the prediction task. We define predictiveness using discriminative metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value. Results: Mental health predictors corresponding to the three months prior to the index visit show strong signal of importance; in one setting, excluding these variables decreased AUC from 0.85 to 0.77. Predictors corresponding to more distant information were less important. Discussion: Predictors from the months immediately preceding the index visit are highly important. Implementation of self-harm prediction models may be challenging in settings where recent data are not completely available (e.g., due to lags in insurance claims processing) at the time a prediction is made. Conclusion: Clinically derived variables from different time frames exhibit varying levels of importance for predicting self-harm. Variable importance analyses can inform whether and how to implement risk prediction models into clinical practice given real-world data limitations. These analyses be applied more broadly in biomedical informatics research to provide insight into general clinical risk prediction tasks. Competing Interests: CONFLICTS OF INTEREST K.J.C. has worked on grants awarded to Kaiser Permanente Southern California by Janssen Pharmaceuticals. S.M.S. has worked on grants awarded to Kaiser Permanente Washington Health Research Institute (KPWHRI) by Bristol Meyers Squibb and by Pfizer. She was also a co-investigator on grants awarded to KPWHRI from Syneos Health, who represented a consortium of pharmaceutical companies carrying out FDA-mandated studies regarding the safety of extended-release opioids. The other authors report there are no competing interests to declare.
Databáze:	MEDLINE
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