Development and validation of a prediction model for conversion of outpatient to inpatient surgery

Abstract

Background: Unplanned hospital admission after intended outpatient surgery is an undesirable outcome. We aimed to develop a prediction model that estimates a patient's risk of conversion from outpatient surgery to inpatient hospitalization.

Methods: This was a retrospective analysis using the American College of Surgeons National Surgical Quality Improvement Program database, 2005 to 2018. Conversion from outpatient to inpatient surgery was defined as having outpatient surgery and >1 day hospital stay. The Surgical Risk Preoperative Assessment System was developed using multiple logistic regression on a training dataset (2005-2016) and compared to a model using the 26 relevant variables in the American College of Surgeons National Surgical Quality Improvement Program. The Surgical Risk Preoperative Assessment System was validated using a testing dataset (2017-2018). Performance statistics and Hosmer-Lemeshow plots were compared. Two high-risk definitions were compared: (1) the maximum Youden index, and (2) the cohort above the tenth decile of risk on the Hosmer-Lemeshow plot. The sensitivities, specificities, positive predictive values, negative predictive values, and accuracies were compared.

Results: In all, 2,822,379 patients were included; 3.6% of patients unexpectedly converted to inpatient. The 6-variable Surgical Risk Preoperative Assessment System model performed comparably to the 26-variable American College of Surgeons National Surgical Quality Improvement Program model (c-indices = 0.818 vs. 0.823; Brier scores = 0.0308 vs 0.0306, respectively). The Surgical Risk Preoperative Assessment System performed well on internal validation (c-index = 0.818, Brier score = 0.0341). The tenth decile of risk definition had higher specificity, positive predictive values, and accuracy than the maximum Youden index definition, while having lower sensitivity.

Conclusion: The Surgical Risk Preoperative Assessment System accurately predicted a patient's risk of unplanned outpatient-to-inpatient conversion. Patients at higher risk should be considered for inpatient surgery, while lower risk patients could safely undergo operations at ambulatory surgery centers.