External validation of a predictive algorithm for in-hospital and 90-day mortality after spinal epidural abscess

Abstract

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 Skeletal Oncology Research Group (SORG) stochastic gradient boosting algorithm for prediction of in-hospital and 90-day postdischarge 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 postdischarge 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 area under the receiver operating characteristic curve (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 postdischarge mortality in SEA.

Keywords: Machine learning; Mortality; Outcomes; Risk calculator; Spinal epidural abscess.