Prediction analysis for delayed discharge following gastric bypass surgery

Growing rates of obesity and an increased demand for bariatric surgery have led to shorter postoperative hospital stays. Although single-day discharge is becoming increasingly common, limited data exist to identify patients requiring longer stays. This study aimed to develop a prediction model using preoperative factors to predict successful discharge within 24 h (single-day discharge) after Roux-en-Y gastric bypass.

A retrospective chart review was performed. Patient age, sex, preoperative weight, body mass index (BMI), medication use, comorbidities, and discharge data were recorded. Cross-validation (CV) was applied to assess the generalizability of the model, and CV error was used to identify the best predictor set.

A total of 525 patients were included. The final model predicted prolonged hospital stay with a prediction error of 0.1545. The best predictive variables for delayed discharge included asthma, preoperative weight, gastroesophageal reflux disease, gabapentin use, preoperative BMI, preoperative anticoagulant use, chronic pain, obstructive sleep apnea, dyslipidemia, chronic kidney disease, and type 2 diabetes mellitus.

A prediction model including preoperative BMI, obesity-related comorbidities, and medication use was generated. This model has the potential to support hospital resource management and triage patients suitable for a single-day discharge and increase the efficiency of resource allocation for gastric bypass.