Inclusion of Frailty Improves Predictive Modeling for Postoperative Outcomes in Surgical Management of Primary and Secondary Lumbar Spine Tumors.

Background: Malignant 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 with age in patients with malignant neoplasms of the lumbar spine.

Methods: We 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. Propensity score matching for age, sex, Charlson Comorbidity Index, surgical approach, and number of levels fused was implemented between frail and nonfrail patients, identifying 533 frail patients and 538 nonfrail patients. The area under the curve (AUC) of each ROC served as a proxy for model performance.

Results: Frail patients reported significantly higher inpatient lengths of stay, 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 with nonfrail 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.

Conclusions: Frailty demonstrated a significant relationship with increased postoperative patient complications, length of stay, 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 with patient age.