Search Results

You are looking at 1 - 2 of 2 items for

  • Author or Editor: Tasha Nasrollahi x
  • Refine by Access: all x
Clear All Modify Search
Restricted access

Shane Shahrestani, Nolan J. Brown, Tasha S. Nasrollahi, Ben A. Strickland, Joshua Bakhsheshian, Jacob J. Ruzevick, Ilaria Bove, Ariel Lee, Ugochi A. Emeh, John D. Carmichael, and Gabriel Zada

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.

Free access

Shane Shahrestani, Nolan J. Brown, Ben A. Strickland, Joshua Bakhsheshian, Seyed Mohammadreza Ghodsi, Tasha Nasrollahi, Michela Borrelli, Julian Gendreau, Jacob J. Ruzevick, and Gabriel Zada

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.