Abstract
INTRODUCTION: Although unplanned readmission is a postoperative outcome metric associated with significant morbidity and financial burden, precise assessment tools for its prediction are yet to be developed. The Revised Risk Analysis Index (RAI) has been recently endorsed as a robust frailty index for predicting surgical outcomes. Hence, the RAI could potentially be utilized to help improve prediction unplanned readmission in patients undergoing intracranial tumor resection (ITR). METHODS: Data of patients undergoing ITR were obtained from the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database from the years 2012-2019 using the ICD-9 and ICD-10 codes, indicating ITR. Baseline characteristics, preoperative clinical status, and outcomes were compared between patients with and without unplanned readmission. Frailty, as a measure of baseline physiological reserve, was calculated using the RAI. Univariate and multivariate logistic regression analyses were performed to identify independent associations between unplanned readmissions and patient characteristics. RESULTS: The unplanned readmission rate of this cohort (N = 31,776) was 10.8% (N = 3,420). Among the readmitted patients, 958 required unplanned reoperation. Multiple characteristics were significantly different between the readmitted and non-readmitted patients, including age, BMI, comorbidities, and RAI groups (p < 0.05). The most common causes of unplanned readmission were infection (9.4%), seizures (6%), and pulmonary embolism (4%). Patient characteristics identified as reliable predictors of unplanned readmission, included age, body mass index (BMI), functional status, diabetes, hypertension, hyponatremia, and their RAI score (p < 0.05). In multivariate analysis, RAI “frail” patients had an unplanned readmission odds ratio (OR) of 1.3(95%CI; 1.2-1.4), and RAI “severely frail” patients had an OR of 1.6 (95%CI; 1.3-2.1) (p < 0.001). CONCLUSIONS: The RAI is a reliable pre-operative frailty index for predicting unplanned readmission following ITR. Utilizing the RAI to identify high risk patients may decrease unplanned readmission through implementation of appropriate management guidelines.
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