Discriminatory Value of the Revised Risk Analysis Index Compared With the Charlson Comorbidity Index in Predicting...

Frailty scoring systems are increasingly used to assess patient vulnerability and predict surgical outcomes. Our objective was to compare the predictive performance of the revised Risk Analysis Index (RAI-rev) and the Charlson Comorbidity Index (CCI) in patients undergoing treatment for chronic subdural hematoma (cSDH).

We conducted a retrospective multicenter study of patients treated with surgical and/or endovascular intervention for cSDH between January 2019 and July 2024. Baseline demographics, comorbidities, frailty scores, and radiographic characteristics were collected. The primary outcome measures were good clinical outcome at the last follow-up (modified Rankin Scale score ≤2) and 30-day mortality. Multivariable regression models were used to assess predictive associations, and receiver operating characteristic analysis was used to evaluate model discrimination. Optimal cutoff scores were determined using Youden's index.

RAI-rev was independently associated with 30-day mortality (odds ratio = 1.05, P = .017) and good clinical outcomes at last follow-up (odds ratio = 0.95, P < .001). In addition, RAI-rev demonstrated superior discrimination compared with CCI for predicting both 30-day mortality (area under the curve 0.857 vs 0.839, respectively) and good clinical outcome (area under the curve 0.767 vs 0.739, respectively). A RAI-rev cutoff score ≥28 identified patients at higher risk, with 30-day mortality of 7.8% vs 2.0% (P = .007) and favorable outcomes in 64.8% vs 86.9% (P < .001).

RAI-rev outperforms CCI in predicting outcomes for patients undergoing surgical and/or endovascular treatment for cSDH. Including RAI-rev during preoperative evaluation may enhance risk stratification and guide treatment planning, including consideration of less invasive interventions in frail patients. Furthermore, integrating RAI-rev into future clinical trials studying patients with cSDH may allow for better understanding of frailty metrics and capture of clinical variables that can affect patient outcomes.