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  • Title: External validation of a predictive algorithm for in-hospital and 90-day mortality after spinal epidural abscess.
    Author: Shah AA, Karhade AV, Groot OQ, Olson TE, Schoenfeld AJ, Bono CM, Harris MB, Ferrone ML, Nelson SB, Park DY, Schwab JH.
    Journal: Spine J; 2023 May; 23(5):760-765. PubMed ID: 36736740.
    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.
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