Trajectory Analysis of Healthcare Utilization Before and After Major Surgery

Abstract

Objective: To characterize patterns of healthcare utilization before and after surgery and determine any association with pre-operative frailty. Summary Background Data: Frail patients experience worse post-operative outcomes and increased costs during the surgical encounter. Evidence is comparatively lacking for longer-term effects of frailty on post-operative healthcare utilization. Methods: Retrospective, longitudinal cohort analysis of adult patients undergoing any elective surgical procedure following pre-operative frailty assessment with the Risk Analysis Index (RAI) from 02/2016-12/2020 at a large integrated healthcare delivery and financing system. Group-based trajectory modeling of claims data estimated distinct clusters of patients with discrete utilization trajectories. Multivariable regression predicted membership in trajectories of interest using preoperative characteristics, including frailty. Results: Among 29,067 surgical encounters, four distinct utilization trajectories emerged in longitudinal data from the 12 months before and after surgery. All cases exhibited a surge in utilization during the surgical month, after which most patients returned to “low” [25,473 (87.6%)], “medium” [1,403 (4.8%)], or “high” [528 (1.8%)] baseline utilization states established before surgery. The fourth trajectory identified 1,663 (5.7%) cases where surgery occasioned a transition from “low” utilization before surgery to “high” utilization afterward. RAI score alone did not effectively predict membership in this transition group, but a multivariable model with other preoperative variables was effective (c=0.859, max re-scaled R-squared 0.264). Conclusions and Relevance: Surgery occasions the transition from low to high healthcare utilization for a substantial subgroup of surgical patients. Multivariable modeling may effectively discriminate this utilization trajectory, suggesting an opportunity to tailor care processes for these patients.