Impact of Preoperative Nutritional Status on Surgical Outcomes in Degenerative Cervical Myelopathy: A Cohort Analysis Based on Geriatric Nutritional Risk Index

Andre A. Payman, Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA.
Sina Zoghi, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
John Ochieng, Michigan State University College of Human Medicine, Grand Rapids, Michigan, USA.
Arman Sourani, Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Sandy, Utah, USA.
Niels Pacheco-Barrios, Carrera de Medicina Humana, Universidad Científica del Sur, Lima, Peru.
Shubhang Bhalla, Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA.
Nabil Yazdi, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA.
Meic Schmidt, Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Sandy, Utah, USA.
Marc Moisi, Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Sandy, Utah, USA.
Christian A. Bowers, Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Sandy, Utah, USA.

Abstract

BACKGROUND AND OBJECTIVES: The aim of this study was to determine whether malnutrition, as defined by the Geriatric Nutritional Risk Index (GNRI), an index of malnutrition in hospitalized geriatric patients, is independently associated with postoperative adverse outcomes in degenerative cervical myelopathy (DCM) spine surgery patients using data from a large national cohort drawn from the National Surgical Quality Improvement Program (NSQIP). METHODS: Data from the American College of Surgeons NSQIP database, spanning 2015 to 2020, were analyzed for adult patients diagnosed with DCM who underwent cervical decompression and/or fusion surgery. Demographic and baseline characteristics, including Risk Analysis Index and modified 5-item Frailty Index, were assessed and stratified based on GNRI. Univariate and multivariate logistic regression analyses evaluated GNRI's impact on various outcomes. Receiver operating characteristic curve analysis assessed GNRI's predictive performance. Statistical analyses were performed using Excel, SPSS, and STATA, with a significance threshold of P < .05. RESULTS: A total of 7142 patients met the inclusion criteria for elective surgical treatment of DCM. Univariate analysis indicated a direct correlation between increasing nutritional risk and poor postoperative outcomes. Multivariate regression analysis yielded similar results. For postoperative mortality, the GNRI IV group had an odds ratio (OR) of 14.033 (P < .001), for nonroutine discharge an OR of 11.658 (P < .001), and for extended length of stay an OR of 9.635 (P < .001). Receiver operating characteristic/area under the curve analysis demonstrated GNRI's strong predictive ability for 30-day postoperative outcomes: mortality (P < .001), nonroutine discharge (P < .001), readmission (P < .001), and reoperation (P < .001). CONCLUSION: GNRI was identified as an independent predictor of postoperative adverse outcomes for cervical decompression and/or fusion in DCM patients, including mortality, readmission, reoperation, and complications in a large multicenter high-quality surgical database with reliable and accurate data. This study demonstrates that malnutrition assessment is valuable for identifying cervical myelopathy patients at higher risk of poor postoperative outcomes.