OBJECTIVE: Health measures such as the Charlson Comorbidity Index (CCI) and the 11-factor modified frailty index (mFI-11) have been employed to predict general medical and surgical mortality, but their clinical utility is limited by the requirement for a large number of data points, some of which overlap or require data that may be unavailable in large datasets. A more streamlined 5-factor modified frailty index (mFI-5) was recently developed to overcome these barriers, but it has not been widely tested in neuro-oncology patient populations. The authors compared the utility of the mFI-5 to that of the CCI and the mFI-11 in predicting postoperative mortality in brain tumor patients. METHODS: The authors retrospectively reviewed a cohort of adult patients from a single institution who underwent brain tumor surgery during the period from January 2017 to December 2018. Logistic regression models were used to quantify the associations between health measure scores and postoperative mortality after adjusting for patient age, race, ethnicity, sex, marital status, and diagnosis. Results were considered statistically significant at p values ≤ 0.05. Receiver operating characteristic (ROC) curves were used to examine the relationships between CCI, mFI-11, and mFI-5 and mortality, and DeLong’s test was used to test for significant differences between c-statistics. Spearman’s rho was used to quantify correlations between indices. RESULTS: The study cohort included 1692 patients (mean age 55.5 years; mean CCI, mFI-11, and mFI-5 scores 2.49, 1.05, and 0.80, respectively). Each 1-point increase in mFI-11 (OR 4.19, p = 0.0043) and mFI-5 (OR 2.56, p = 0.018) scores independently predicted greater odds of 90-day postoperative mortality. Adjusted CCI, mFI-11, and mFI-5 ROC curves demonstrated c-statistics of 0.86 (CI 0.82–0.90), 0.87 (CI 0.83–0.91), and 0.87 (CI 0.83–0.91), respectively, and there was no significant difference between the c-statistics of the adjusted CCI and the adjusted mFI-5 models (p = 0.089) or between the adjusted mFI-11 and the adjusted mFI-5 models (p = 0.82). The 3 indices were well correlated (p < 0.01). CONCLUSIONS: The adjusted mFI-5 model predicts 90-day postoperative mortality among brain tumor patients as well as our adjusted CCI and adjusted mFI-11 models. The simplified mFI-5 may be easily integrated into clinical workflows to predict brain tumor surgery outcomes in real time.

BACKGROUND: Frailty indices may represent useful decision support tools to optimize modifiable drivers of quality and cost in neurosurgical care. However, classic indices are cumbersome to calculate and frequently require unavailable data. Recently, a more lean 5-factor modified frailty index (mFI-5) was introduced, but it has not yet been rigorously applied to brain tumor patients. OBJECTIVE: To investigate the predictive value of the mFI-5 on length of stay (LOS), complications, and charges in surgical brain tumor patients. METHODS: We retrospectively reviewed data for brain tumor patients who underwent primary surgery from 2017 to 2018. Bivariate (ANOVA) and multivariate (logistic and linear regression) analyses assessed the predictive power of the mFI-5 on postoperative outcomes. RESULTS: Our cohort included 1692 patients with a mean age of 55.5 yr and mFI-5 of 0.80. Mean intensive care unit (ICU) and total LOS were 1.69 and 5.24 d, respectively. Mean pulmonary embolism (PE)/deep vein thrombosis (DVT), physiological/metabolic derangement, respiratory failure, and sepsis rates were 7.2%, 1.1%, 1.6%, and 1.7%, respectively. Mean total charges were $42 331. On multivariate analysis, each additional point on the mFI-5 was associated with a 0.32- and 1.38-d increase in ICU and total LOS, respectively; increased odds of PE/DVT (odds ratio (OR): 1.50), physiological/metabolic derangement (OR: 3.66), respiratory failure (OR: 1.55), and sepsis (OR: 2.12); and an increase in total charges of $5846. CONCLUSION: The mFI-5 is a pragmatic and actionable tool which predicts LOS, complications, and charges in brain tumor patients. It may guide future efforts to risk-stratify patients with subsequent impact on postoperative outcomes.