In The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT : Mortality in patients with spinal epidural abscess (SEA) remains high. Accurate prediction of patient-specific prognosis in SEA can improve patient counseling as well as guide management decisions. There are no externally validated studies predicting short-term mortality in patients with SEA.
PURPOSE : The purpose of this study was to externally validate the SORG stochastic gradient boosting algorithm for prediction of in-hospital and 90-day post-discharge mortality in SEA.
STUDY DESIGN/SETTING : Retrospective, case-control study at a tertiary care academic medical center from 2003 to 2021.
PATIENT SAMPLE : Adult patients admitted for radiologically confirmed diagnosis of SEA who did not initiate treatment at an outside institution.
OUTCOME MEASURES : In-hospital and 90-day post-discharge mortality.
METHODS : We tested the SORG stochastic gradient boosting algorithm on an independent validation cohort. We assessed its performance with discrimination, calibration, decision curve analysis, and overall performance.
RESULTS : A total of 212 patients met inclusion criteria, with a short-term mortality rate of 10.4%. The AUROC of the SORG algorithm when tested on the full validation cohort was 0.82, the calibration intercept was -0.08, the calibration slope was 0.96, and the Brier score was 0.09.
CONCLUSIONS : With a contemporaneous and geographically distinct independent cohort, we report successful external validation of a machine learning algorithm for prediction of in-hospital and 90-day post-discharge mortality in SEA.
Shah Akash A, Karhade Aditya V, Groot Olivier Q, Olson Thomas E, Schoenfeld Andrew J, Bono Christopher M, Harris Mitchel B, Ferrone Marco L, Nelson Sandra B, Park Don Y, Schwab Joseph H
2023-Feb-01
outcomes, machine learning, risk calculator, spinal epidural abscess, mortality
