Causes and Predictors of unplanned readmission in patients undergoing Intracranial Tumor Resection: A Multi-Center Analysis of 31,776 Patients.

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 Risk Analysis Index (RAI) could potentially be utilized to help improve prediction unplanned readmission in patients undergoing intracranial tumor resection (ITR). Here we evaluate the predictive accuracy of frailty on 30-day unplanned readmission following ITR using the RAI.

METHODS: Data were obtained from the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database. Baseline characteristics, preoperative clinical status, and outcomes were compared between patients with and without unplanned readmission. Frailty 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). Common causes of unplanned readmission included 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). Frail status, hyponatremia, leukocytosis, hypertension, and thrombocytosis were significant predictors of unplanned readmission.

CONCLUSION: The RAI is a reliable pre-operative frailty index for predicting unplanned readmission following ITR. Utilizing the RAI may decrease unplanned readmission thorough identifying high risk patients, and enabling future implementation of appropriate management guidelines.