• Jad Zreik, Mohammed Ali Alvi, Yagiz U Yolcu, Arjun S Sebastian, Brett A Freedman, and Mohamad Bydon.
• Central Michigan University College of Medicine, Mount Pleasant, Michigan, USA; Mayo Clinic Neuro-Informatics Laboratory, Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota, USA.
• World Neurosurg. 2021 Feb 1; 146: e670-e677.

BackgroundFrailty is an increasingly studied tool for preoperative risk stratification, but its prognostic value for anterior cervical discectomy and fusion (ACDF) patients is unclear. We sought to evaluate the association of the 5-item modified Frailty Index (5i-mFI) with 30-day adverse outcomes following ACDF and its predictive performance compared with other common metrics.MethodsThe National Surgical Quality Improvement Program was queried from 2016-2018 for patients undergoing elective ACDF for degenerative diseases. Outcomes of interest included 30-day complications, extended length of stay (LOS), non-home discharge, and unplanned readmissions. Unadjusted/adjusted odds ratios were calculated. The discriminatory performance of the 5i-mFI compared with age, American Society of Anesthesiologists (ASA) classification, and body mass index was computed using the area under the receiver operating characteristic curve (AUC).ResultsA total of 23,754 patients were identified. On adjusted analysis, an index of 1 was significantly associated with extended LOS, non-home discharge, and unplanned readmissions (P < 0.001, P = 0.023, P = 0.003, respectively), but not complications (all P > 0.05). An index ≥2 was significantly associated with each outcome (all P < 0.001). The 5i-mFI was found to have a significantly higher AUC than body mass index for each outcome, a similar AUC compared with ASA classification and age for complications and unplanned readmissions, and a significantly lower AUC than ASA classification and age for extended LOS and non-home discharge.ConclusionsThe 5i-mFI was found to be significantly associated with 30-day adverse outcomes following ACDF but had similar or lesser predictive performance compared with more universally available and easily implemented metrics, such as ASA classification and age.Copyright © 2020 Elsevier Inc. All rights reserved.

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