Risk Stratification and Prediction of Postoperative Complications Using Temperature Trajectories

Padman, Rema
Grant, Jennifer
Ravichandran, Urmila
Bashyal, Ravi
Shah, Nirav
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Early identification of patients at highest risk of postoperative complications can facilitate appropriate diagnostic work-ups and earlier interventions. We investigate whether postoperative temperature trajectories can stratify patients and predict this risk via a retrospective study of 5,084 adult patients undergoing elective primary total knee arthroplasty at a major health system. Demographics, surgery duration, temperature readings, length of stay, comorbidities and complications were extracted from the data warehouse. Group-based trajectory modeling was applied to cluster patients into distinct groups following similar progression of maximum temperature over four-hour time intervals until discharge, and group information was included in predicting risk of critical complications. Three non-overlapping, temperature-based trajectories were identified as high- (8% of patients), medium- (49%), and low-risk (43%) groups. Complication rates were significantly higher in the high-risk group (16.7%), than the medium-risk (5.4%), and the low-risk groups (2.70%) (p<0.01). Group information shows promise in improving complication risk prediction for high-risk patients.
Big Data on Healthcare Application, complications prediction, early warning system, group-based trajectory modeling, post-surgical patient care, temperature trajectory
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