The inclusion of frailty improves predictive modeling for postoperative outcomes in the surgical management of primary and secondary lumbar spine tumors.

Abstract

INTRODUCTION\nMalignant spinal tumors are common, continually increasing in incidence as a function of improved survival times for patients with cancer. Using predictive analytics and propensity score matching, we evaluated the influence of frailty on postoperative complications compared to age in patients with malignant neoplasms of the lumbar spine.\n\n\nMETHODS\nWe used the Nationwide Readmissions Database from 2016 and 2017 to identify patients with malignant neoplasms of the lumbar spine who received a fusion procedure. Patient frailty was queried using the Johns Hopkins Adjusted Clinical Groups (JHACG). Propensity score matching for age, sex, CCI, surgical approach, and number of levels fused was implemented between frail and non-frail patients, identifying 533 frail patients and 538 non-frail patients. The area under the curve (AUC) of each ROC served as a proxy for model performance.\n\n\nRESULTS\nFrail patients reported significantly higher inpatient lengths of stay (LOS), costs, infection, posthemorrhagic anemia, and urinary tract infections (p<0.05). In addition, frail patients were more often discharged to skilled nursing facilities and short-term hospitals compared to non-frail patients (p<0.0001). Regression models for mortality (AUC=0.644), nonroutine discharge (AUC=0.600), and acute infection (AUC=0.666) were improved when using frailty as the primary predictor. These models were also improved using frailty when predicting 30-day readmission and 90-day hardware failure.\n\n\nCONCLUSIONS\nFrailty demonstrated a significant relationship with increased postoperative patient complications, LOS, costs, and acute complications in patients receiving fusion following resection of a malignant neoplasm of the lumbar spine region. Frailty demonstrated better predictive validity of outcomes compared to patient age.