The risk analysis index is an independent predictor of outcomes after lung cancer resection

Abstract

Background: The Risk Analysis Index (RAI) is a frailty assessment tool based on an accumulation of deficits model. We mapped RAI to data from the Society of Thoracic Surgeons (STS) Database to determine whether RAI correlates with postoperative outcomes following lung cancer resection.

Methodology/principal findings: This was a national database retrospective observational study based on data from the STS Database. Study patients underwent surgery 2018 to 2020. RAI was divided into four increasing risk categories. The associations between RAI and each of postoperative complications and administrative outcomes were examined using logistic regression models. We also compared the performance of RAI to established risk indices (American Society of Anesthesiology (ASA) and Charlson Comorbidity Index (CCI)) using areas under the Receiver Operating Characteristic (ROC) curves (AUC). Results: Of 29,420 candidate patients identified in the STS Database, RAI could be calculated for 22,848 (78%). Almost all outcome categories exhibited a progressive increase in marginal probability as RAI increased. On multivariable analyses, RAI was significantly associated with an incremental pattern with almost all outcomes. ROC analyses for RAI demonstrated "good" AUC values for mortality (0.785; 0.748) and discharge location (0.791), but only "fair" values for all other outcome categories (0.618 to 0.690). RAI performed similarly to ASA and CCI in terms of AUC score categories.

Conclusions/significance: RAI is associated with clinical and administrative outcomes following lung cancer resection. However, its overall accuracy as a surgical risk predictor is only moderate and similar to ASA and CCI. We do not recommend routine use of RAI for assessment of individual patient risk for major lung resection.