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

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 shortterm 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. (c) 2023 Elsevier Inc. All rights reserved.

Automated summary

This study aimed to predict the short-term mortality rate in patients with spinal epidural abscess (SEA) and validate a machine learning algorithm for predicting in-hospital and 90-day postdischarge mortality in SEA patients.