Risk Analysis Index Frailty Score as a Predictor of Otolaryngology Surgical Outcomes

Author:

Evans Lauran K.1ORCID,Moffatt Clare1ORCID,Niknejad Keon2,Yang Hong‐Ho1,Kodaverdian Laura1,Soliman Shady1,Reyes Orozco Francis1,Chhetri Dinesh K.1

Affiliation:

1. Department of Head and Neck Surgery David Geffen School of Medicine at UCLA Los Angeles California USA

2. The Johns Hopkins University School of Medicine Baltimore Maryland USA

Abstract

AbstractObjectiveThe Risk Analysis Index (RAI) score is a screening tool to assess patient frailty. It has been shown to be predictive of postoperative outcomes and mortality in orthopedic, urologic, and neurosurgical patient populations. We sought to evaluate the predictive ability of RAI score for surgical outcomes in an otolaryngology patient population.Study DesignRetrospective study.SettingAcademic tertiary medical center.MethodsA retrospective study was conducted of adult patients undergoing otolaryngology surgery at a tertiary medical care center over 21 months. Patients were sent electronic RAI survey questionnaires via direct messaging, which was completed prior to surgery. Endpoint data were analyzed, including demographics, RAI score, and patient outcome data. Univariate analysis, ROC curves, and predictive modeling were utilized.ResultsA total of 517 patients responded to the RAI questionnaire, resulting in a 59.6% response rate. Mean RAI score was 21.38 ± 11.83. Higher RAI scores were associated with increased 30‐day readmissions (P < .0015), postoperative complications (P < .001), hospital length of stay (P < .001), and discharge with home health (P < .001). Predictive models for RAI score and postoperative outcomes were created, and a cutoff score of RAI = 30 was established to identify frail patients.ConclusionWe evaluated if RAI scoring predicted postoperative complications in an otolaryngology patient population. Increased RAI score is significantly associated with poorer surgical outcomes, including increased hospital length of stay, 30‐day readmissions, and postoperative complications. We propose a predictive model with suggested RAI cutoff scoring for use in the otolaryngology surgical population.

Publisher

Wiley

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