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Evaluating the predictive value of comorbidity indices in pituitary surgery: a mixed-effects modeling study using the Nationwide Readmissions Database

Shahrestani, Shane and Brown, Nolan J. and Nasrollahi, Tasha S. and Strickland, Ben A. and Bakhsheshian, Joshua and Ruzevick, Jacob J. and Bove, Ilaria and Lee, Ariel and Emeh, Ugochi A. and Carmichael, John D. and Zada, Gabriel (2022) Evaluating the predictive value of comorbidity indices in pituitary surgery: a mixed-effects modeling study using the Nationwide Readmissions Database. Journal of Neurosurgery . ISSN 0022-3085. doi:10.3171/2022.1.jns22197. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20220413-928415300

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Abstract

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3171/2022.1.jns22197DOIArticle
ORCID:
AuthorORCID
Shahrestani, Shane0000-0001-7561-4590
Brown, Nolan J.0000-0002-6025-346X
Bakhsheshian, Joshua0000-0003-2983-6082
Zada, Gabriel0000-0001-5821-902X
Additional Information:© 2022 American Association of Neurological Surgeons. Online Publication Date: 18 Mar 2022.
DOI:10.3171/2022.1.jns22197
Record Number:CaltechAUTHORS:20220413-928415300
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220413-928415300
Official Citation:Shahrestani, S., Brown, N. J., Nasrollahi, T. S., Strickland, B. A., Bakhsheshian, J., Ruzevick, J. J., Bove, I., Lee, A., Emeh, U. A., Carmichael, J. D., & Zada, G. (2022). Evaluating the predictive value of comorbidity indices in pituitary surgery: a mixed-effects modeling study using the Nationwide Readmissions Database, Journal of Neurosurgery (published online ahead of print 2022); DOI: 10.3171/2022.1.jns22197
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114285
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:13 Apr 2022 21:37
Last Modified:13 Apr 2022 21:37

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