The performance of frailty in predictive modeling of short-term outcomes in the surgical management of metastatic tumors to the spine

Abstract

Background context: The concept of frailty has become increasingly recognized, and while patients with cancer are at increased risk for frailty, its influence on perioperative outcomes in metastatic spine tumors is uncertain. Furthermore, the impact of frailty can be confounded by comorbidities or metastatic disease burden.

Purpose: The purpose of this study was to evaluate the influence of frailty and comorbidities on adverse outcomes in the surgical management of metastatic spine disease.

Study design/setting: Retrospective analysis of a nationwide database to include patients undergoing spinal fusion for metastatic spine disease.

Patient sample: A total of 1,974 frail patients who received spinal fusion with spinal metastasis, and 1,975 propensity score matched non-frail patients.

Outcome measures: Outcomes analyzed included mortality, complications, length of stay (LOS), nonroutine discharges and costs.

Methods: A validated binary frailty index (Johns Hopkins Adjusted Clinical Groups) was used to identify frail and non-frail groups, and propensity score-matched analysis (including demographics, comorbidities, surgical and tumor characteristics) was performed. Sub-group analysis of levels involved was performed for cervical, thoracic, lumbar and junctional spine. Multivariable-regression techniques were used to develop predictive models for outcomes using frailty and the Elixhauser Comorbidity Index (ECI).

Results: 7,772 patients underwent spinal fusion with spinal metastasis, of which 1,974 (25.4%) patients were identified as frail. Following propensity score matching for frail (n=1,974) and not-frail (n=1,975) groups, frailty demonstrated significantly greater medical complications (OR=1.58; 95% CI 1.33-1.86), surgical complications (OR=1.46; 95% CI 1.15-1.85), LOS (OR=2.65; 95% CI 2.09-3.37), nonroutine discharges (OR=1.79; 95% CI 1.46-2.20) and costs (OR=1.68; 95% CI 1.32-2.14). Differences in mortality were only observed in subgroup analysis and were greater in frail junctional and lumbar spine subgroups. Models using ECI alone (AUC=0.636-0.788) demonstrated greater predictive ability compared to those using frailty alone (AUC=0.633-0.752). However, frailty combined with ECI improved the prediction of increased LOS (AUC=0.811), cost (AUC=0.768), medical complications (AUC=0.723) and nonroutine discharges (AUC=0.718). Predictive modeling of frailty in subgroups demonstrated the greatest performance for mortality (AUC=0.750) in the lumbar spine, otherwise performed similarly for LOS, costs, complications, and discharge across subgroups.

Conclusions: A high prevalence of frailty existed in the current patient cohort. Frailty contributed to worse short-term adverse outcomes and could be more influential in the lumbar and junctional spine due to higher risk of deconditioning in the postoperative period. Predictions for short term outcomes can be improved by adding frailty to comorbidity indices, suggesting a more comprehensive preoperative risk stratification should include frailty.

Keywords: Cancer; Machine learning; Physical function; Prognosis; Scoring system.