The 5-factor modified frailty index: an effective predictor of mortality in brain tumor patients

Abstract

OBJECTIVEHealth 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.METHODSThe 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.RESULTSThe 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).CONCLUSIONSThe 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.

Frailty as a Predictor of Postoperative Complications Following Skull Base Surgery

“…Frailty is a state of diminished physiological reserve that limits resolution of homeostasis following a stressor 6 . Surgical oncology literature has shown frailty to be a predictor of morbidity and mortality in sinonasal cancer resections, transsphenoidal pituitary resections, meningioma resections, and intracranial surgery 7–13 . However, a population‐level study of the utility of frailty index in patients undergoing SBS has yet to be completed.…”

“…6 Surgical oncology literature has shown frailty to be a predictor of morbidity and mortality in sinonasal cancer resections, transsphenoidal pituitary resections, meningioma resections, and intracranial surgery. [7][8][9][10][11][12][13] However, a population-level study of the utility of frailty index in patients undergoing SBS has yet to be completed. The 5-factor modified-frailty index (mFI-5) is an easily calculated score based on five comorbidities gleaned from a patient's history, which has been validated using American College of Surgeons (ACS) NSQIP data and utilized by various surgical specialties.…”

The 5-Factor Modified Frailty Index Predicts Complications and Worsening After Meningioma Surgery in Non-elderly Rather Than Elderly Patients: A Nationwide Registry Study

“…The same analyses were performed separately across the three categories of age groups, except for in-hospital mortality where the numbers of participants in each subgroup were too low for statistical analysis. For multivariate logistic regression analyses, independent variables were selected based on the existing literature [4,13,19,20,26,28,31], and no variable selection method was applied; odds ratios (ORs) and 95% con dence intervals (CIs) were calculated.…”

“…To assess frailty, the 11-factor modi ed frailty index (mFI-11) was derived from the Canadian Study of Health and Aging Frailty Index [25] by matching 11 comorbidity and de cit variables from the American College of Surgeons' National Surgery Quality and Improvement Project, which are well-validated health measures that have been applied within general medical and surgical datasets [7,23,30,33]. Recently, the simpli ed 5-factor modi ed frailty index (mFI-5) was introduced and validated within various elds of surgery, and it includes the following ve factors: the prevalence of functional dependence, history of diabetes mellitus, history of chronic obstructive pulmonary disease, congestive heart failure, and hypertension [13,26,28,31]. Frailty is observed more commonly among the elderly than non-elderly patients; therefore, the frailty index is generally considered an important risk factor in elderly patients.…”

Frailty as a Predictor of Neurosurgical Outcomes in Brain Tumor Patients: A Systematic Review and Meta-Analysis

“…Publication location come from the United States [18 -20, 22-30] and Columbia [21] between 2013 and 2021. Frailty was assessed use mFI [18,21,25,[29][30], JHACG [19][20][27][28], HFS [22], mFI-5 [23][24]26]. Prevalence of frailty ranged from 1.48% to 57%.…”