Faculty Collaboration Database

Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm. J Surg Res 2022 Feb;270:39-48

Date

10/11/2021

Pubmed ID

34628162

DOI

10.1016/j.jss.2021.08.021

Scopus ID

2-s2.0-85116716480 (requires institutional sign-in at Scopus site)   4 Citations

Abstract

BACKGROUND: The ability to reliably predict outcomes after trauma in older adults (age ≥ 65 y) is critical for clinical decision making. Using novel machine-learning techniques, we sought to design a nonlinear, competing risks paradigm for prediction of older adult discharge disposition following injury.

MATERIALS AND METHODS: The National Trauma Databank (NTDB) was used to identify patients 65+ y between 2007 and 2014. Training was performed on an enriched cohort of diverse patients. Factors included age, comorbidities, length of stay, and physiologic parameters to predict in-hospital mortality and discharge disposition (home versus skilled nursing/long-term care facility). Length of stay and discharge status were analyzed via competing risks survival analysis with Bayesian additive regression trees and a multinomial mixed model.

RESULTS: The resulting sample size was 47,037 patients. Admission GCS and age were important in predicting mortality and discharge disposition. As GCS decreased, patients were more likely to die (risk ratio increased by average of 1.4 per 2-point drop in GCS, P < 0.001). As GCS decreased, patients were also more likely to be discharged to a skilled nursing or long-term care facility (risk ratio decreased by 0.08 per 2-point decrease in GCS, P< 0.001). The area under curve for prediction of discharge home was improved in the competing risks model 0.73 versus 0.43 in the traditional multinomial mixed model.

CONCLUSIONS: Predicting older adult discharge disposition after trauma is improved using machine learning over traditional regression analysis. We confirmed that a nonlinear, competing risks paradigm enhances prediction on any given hospital day post injury.

Author List

Morris RS, Tignanelli CJ, deRoon-Cassini T, Laud P, Sparapani R

Authors

Purushottam W. Laud PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Rachel S. Morris MD Assistant Professor in the Surgery department at Medical College of Wisconsin
Rodney Sparapani PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Terri A. deRoon Cassini PhD Center Director, Professor in the Surgery department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Aged
Bayes Theorem
Hospital Mortality
Humans
Machine Learning
Patient Discharge
Retrospective Studies