Introduction: Operative management of spinal metastatic disease is largely for symptom palliation rather than curative and revolves around the expectation that postoperative survival will exceed recovery time. While several scoring systems and models to predict survival exist, few studies have unified diverse predictors into integrated models to predict short-term postoperative outcomes as indicators of recovery.
Methods: The IBM MarketScan Claims Database and Medicare Supplement were queried for adult patients receiving surgery for extradural spinal metastatic disease between 2006 and 2023. Primary outcomes of interest were nonhome discharge and unplanned 90-day post-discharge readmission. Inpatient length of stay (LOS) was assessed as a secondary outcome. Five models (extreme gradient boosting, support vector machine, neural network, random forest and penalized logistic regression) were trained on a 70% training sample and validated on the withheld 30%.
Results: A total of 1,926 patients were included. Thoracic spine localization (vs cervical, odds ratio [OR]:2.83, 95% confidence interval [CI]:[1.74,4.58]) was associated with higher odds, while post-resection arthrodesis (vs no arthrodesis, OR=1.24, [0.59,0.97]) and intraoperative neuromonitoring (vs not, OR=0.45, [0.31,0.66]) were associated with lower odds of non-home discharge. Utilizing a combined anterior and posterior approach (vs anterior, OR=0.50, [0.33,0.75]) and arthrodesis (OR=0.96, [0.75,1.23]) were associated were lower odds of 90-day readmission. post-resection arthrodesis (B=-0.17 [-2.66,-0.76]) and using intraoperative neuromonitoring (B=-1.84, [-2.72,-0.97]) or operating microscope (vs not, B=-1.71, [-2.66,-0.76]) were associated with shorter LOS. The neural network algorithm demonstrated the best overall predictive performance in the withheld validation cohort when assessing non-home discharge (AUC=0.68, calibration slope=0.82), while random forest was best when evaluating unplanned 90-day readmission (AUC=0.67, calibration slope=0.87). The final algorithms were incorporated into an open access web application to provide predictions and patient-specific explanations of the results generated by the models.
Conclusion : We developed and validated parsimonious predictive models incorporating diverse features to estimate risk of non-home discharge and 90-day readmission after surgery for extradural spinal metastatic disease. After integration into physician- and patient-facing interfaces, these models may serve as clinically useful decision tools to enhance prognostication and management.
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