Objective: Although pituitary adenomas (PAs) are common intracranial tumors, literature evaluating the utility of comorbidity indices for predicting postoperative complications in patients undergoing pituitary surgery remains limited, thereby hindering the development of complex models that aim to identify high-risk patient populations. We utilized comparative modeling strategies to evaluate the predictive validity of various comorbidity indices and combinations thereof in predicting key pituitary surgery outcomes.
Methods: The Nationwide Readmissions Database was used to identify patients who underwent pituitary tumor operations (n = 19,653) in 2016-2017. Patient frailty was assessed using the Johns Hopkins Adjusted Clinical Groups (ACG) System. The Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) were calculated for each patient. Five sets of generalized linear mixed-effects models were developed, using as the primary predictors 1) frailty, 2) CCI, 3) ECI, 4) frailty + CCI, or 5) frailty + ECI. Complications of interest investigated included inpatient mortality, nonroutine discharge (e.g., to locations other than home), length of stay (LOS) within the top quartile (Q1), cost within Q1, and 1-year readmission rates.
Results: Postoperative mortality occurred in 73 patients (0.4%), 1-year readmission was reported in 2994 patients (15.2%), and nonroutine discharge occurred in 2176 patients (11.1%). The mean adjusted all-payer cost for the procedure was USD $25,553.85 ± $26,518.91 (Q1 $28,261.20), and the mean LOS was 4.8 ± 7.4 days (Q1 5.0 days). The model using frailty + ECI as the primary predictor consistently outperformed other models, with statistically significant p values as determined by comparing areas under the curve (AUCs) for most complications. For prediction of mortality, however, the frailty + ECI model (AUC 0.831) was not better than the ECI model alone (AUC 0.831; p = 0.95). For prediction of readmission, the frailty + ECI model (AUC 0.617) was not better than the frailty model alone (AUC 0.606; p = 0.10) or the frailty + CCI model (AUC 0.610; p = 0.29).
Conclusions: This investigation is to the authors' knowledge the first to implement mixed-effects modeling to study the utility of common comorbidity indices in a large, nationwide cohort of patients undergoing pituitary surgery. Knowledge gained from these models may help neurosurgeons identify high-risk patients who require additional clinical attention or resource utilization prior to surgical planning.
Keywords: Charlson Comorbidity Index; Elixhauser Comorbidity Index; frailty; machine learning; neurosurgery; pituitary surgery; skull base.