Evaluating the performance of the SORG machine learning algorithm for predicting discharge disposition in lumbar surgery patients

Abstract

PurposeProtracted admissions following lumbar surgeries are rising, often stemming from inefficient identification of patients requiring nonhome discharge for rehabilitation. The SORG Orthopaedic Research Group at Harvard Medical School have developed a machine learning algorithm for predicting discharge following lumbar surgery. This study assessed its predictive performance on an independent tertiary centre patient cohort. MethodsMedical records were retrospectively reviewed for all elective adult lumbar disc degeneration or herniation surgeries performed between July 2017–2021 at a tertiary neurosurgical centre in the United Kingdom. Preoperative variables were collated and discharge destinations noted. Algorithm predictions were analysed using the concordance (c) statistic, Brier score and calibration plot. Positive and negative predictive values (PPV, NPV) were calculated, and a decision curve analysis (DCA) plotted. Results251 subjects were included (48.2 % female, mean age 55.3 years). 2.8 % underwent nonhome discharge. Most had surgery at 1/2 spinal levels (98.4 %) and were functionally independent (84.5 %). Algorithm predictions yielded a 0.88 c-statistic and 0.029 Brier score. The algorithm was miscalibrated to the data (calibration plot slope 1.31 and intercept -1.12). At a 0.25 threshold for nonroutine discharge risk, the PPV was 0.19 and NPV 0.98. DCA revealed limited clinical utility. ConclusionsAlgorithm predictive performance was mixed for this cohort, displaying strong discrimination but poor calibration and overestimation of nonroutine discharges. Differences in patient management practices and the low nonhome discharge rate may explain this. Larger validation studies across different healthcare systems, alongside geographically specific algorithm development, will improve predictive accuracy prior to clinical application.