Decomposing the hazard function into interpretable readmission risk components

doi:10.1016/j.dss.2024.114264

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Decomposing the hazard function into interpretable readmission risk components

2024-08-01

James K. Todd *

Steven Stern

DOI：10.1016/j.dss.2024.114264

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Abstract

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Hospital decision-makers use predictive models to proactively manage risk of readmission for discharged patients. While predictions from classification models are easily integrated into decision-making processes, it is unclear how to best integrate predictions of the evolution of risk from time-to-event models. We propose a method for summarising predictions of risk over time that produces interpretable components for use in a variety of decision-making processes. The proposed method summarises predictions of risk over time (hazard functions) by approximating them with a parametric smoother. The components of the smoothed approximation can then serve as the basis for decision-making. To demonstrate the proposed summarisation method, we apply it in the specific case of a previously published model for patients discharged from a large teaching hospital on the Gold Coast, Australia. In this context, we describe how the summaries produced by the method could be used to estimate time until a patient reaches a stable, persistent risk level or to stratify patients according to risks of readmission in excess of patient-specific baselines. Our method is anticipated to be valuable in and outside of healthcare for settings where the evolution of risk is important, with specific examples including posttransplantation risk and reinjury risks.

Keywords:

Survival analysis

Hazard rate

Exponential sums

Hospital readmissions

Rehospitalization

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