Shah AA, Karhade AV, Groot OQ, Olson TE, Schoenfeld AJ, Bono CM, Harris MB, Ferrone ML, Nelson SB, Park DY, Schwab JH. External validation of a predictive algorithm for in-hospital and ninety-day mortality after spinal epidural abscess.
Spine J 2023;
23:760-765. [PMID:
36736740 DOI:
10.1016/j.spinee.2023.01.013]
[Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 01/05/2023] [Accepted: 01/21/2023] [Indexed: 02/05/2023]
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.
Collapse