The role of frailty in the clinical management of neurofibromatosis type 1: a mixed-effects modeling study using the Nationwide Readmissions Database

Abstract

Objective: Frailty embodies a state of increased medical vulnerability that is most often secondary to age-associated decline. Recent literature has highlighted the role of frailty and its association with significantly higher rates of morbidity and mortality in patients with CNS neoplasms. There is a paucity of research regarding the effects of frailty as it relates to neurocutaneous disorders, namely, neurofibromatosis type 1 (NF1). In this study, the authors evaluated the role of frailty in patients with NF1 and compared its predictive usefulness against the Elixhauser Comorbidity Index (ECI). Methods: Publicly available 2016–2017 data from the Nationwide Readmissions Database was used to identify patients with a diagnosis of NF1 who underwent neurosurgical resection of an intracranial tumor. Patient frailty was queried using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. ECI scores were collected in patients for quantitative measurement of comorbidities. Propensity score matching was performed for age, sex, ECI, insurance type, and median income by zip code, which yielded 60 frail and 60 nonfrail patients. Receiver operating characteristic (ROC) curves were created for complications, including mortality, nonroutine discharge, financial costs, length of stay (LOS), and readmissions while using comorbidity indices as predictor values. The area under the curve (AUC) of each ROC served as a proxy for model performance. Results: After propensity matching of the groups, frail patients had an increased mean ± SD hospital cost ($85,441.67 ± $59,201.09) compared with nonfrail patients ($49,321.77 ± $50,705.80) (p = 0.010). Similar trends were also found in LOS between frail (23.1 ± 14.2 days) and nonfrail (10.7 ± 10.5 days) patients (p = 0.0020). For each complication of interest, ROC curves revealed that frailty scores, ECI scores, and a combination of frailty+ECI were similarly accurate predictors of variables (p > 0.05). Frailty+ECI (AUC 0.929) outperformed using only ECI for the variable of increased LOS (AUC 0.833) (p = 0.013). When considering 1-year readmission, frailty (AUC 0.642) was outperformed by both models using ECI (AUC 0.725, p = 0.039) and frailty+ECI (AUC 0.734, p = 0.038). Conclusions: These findings suggest that frailty and ECI are useful in predicting key complications, including mortality, nonroutine discharge, readmission, LOS, and higher costs in NF1 patients undergoing intracranial tumor resection. Consideration of a patient's frailty status is pertinent to guide appropriate inpatient management as well as resource allocation and discharge planning.

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