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.

A contemporary analysis of surgical ligation versus endovascular embolization in patients with intracranial dural arteriovenous fistulas: a propensity score–matched and mixed-effects model study

“…Hospital identifiers were included as random effects. [26][27][28][29] Model performance was assessed using the area under the curve (AUC) of each ROC curve, acting as a proxy. An AUC of 0.50 indicates a random guess, whereas values exceeding 0.70 are considered acceptable.…”

Developing Mixed-effects Models to Optimize Prediction of Postoperative Outcomes in a Modern Sample of Over 450,000 Patients Undergoing Elective Cervical Spine Fusion Surgery

“…Our study is the first of its kind to assess the combined predictive utility of multiple comorbidity indices for cervical spine fusion and extends the evidence relevant to the role of comorbidity indices in predicting surgical outcomes. Comorbidity indices have previously been used to inform surgical resection of cranial tumors such as pituitary adenomas and those associated with neurofibromatosis type I 35,36 . With respect to spinal procedures, a 2021 study by Hersh et al 37 reported that frailty measured by the 5-item frailty index or the metastatic spinal tumor frailty index is more predictive of adverse outcomes in patients with spinal metastases than CCI in a cohort of 322 patients.…”

“…Comorbidity indices have previously been used to inform surgical resection of cranial tumors such as pituitary adenomas and those associated with neurofibromatosis type I. 35,36 With respect to spinal procedures, a 2021 study by Hersh et al 37 reported that frailty measured by the 5-item frailty index or the metastatic spinal tumor frailty index is more predictive of adverse outcomes in patients with spinal metastases than CCI in a cohort of FIGURE 2. ROC plot for prediction of mortality.…”

Developing Mixed-Effects Models to Compare the Predictive Ability of Various Comorbidity Indices in a Contemporary Cohort of Patients Undergoing Lumbar Fusion

“…37,38 Similar studies have also been performed to optimize the use of comorbidity indices for neurosurgical resection of cranial tumors, including pituitary tumors and those associated with Neurofibromatosis type 1. 39,40 For neurosurgery of the spine, studies evaluating comorbidity indices are predominantly focused on adult spinal deformity (ASD) correction and surgical interventions for spinal tumors. For example, a 2021 study by Hersh et al 41 found that frailty measured with either 5-item modified frailty index (mFI-5) or metastatic spinal tumor frailty index scores was more predictive of adverse postoperative outcomes in patients with spinal metastases than the more widely used CCI.…”

“…37,38 Similar studies have also been performed to optimize the use of comorbidity indices for neurosurgical resection of cranial tumors, including pituitary tumors and those associated with Neurofibromatosis type 1. 39,40…”