Development and validation of a multivariable preoperative prediction model for postoperative length of stay in a broad inpatient surgical population

• Published in: Surgery 2023-07, Vol.174 (1), p.66-74
• Main Authors: Mason, Emily M., Henderson, William G., Bronsert, Michael R., Colborn, Kathryn L., Dyas, Adam R., Lambert-Kerzner, Anne, Meguid, Robert A.
• Format: Article
• Language: English
• Subjects: Adult; Humans; Inpatients; Length of Stay; Postoperative Complications - epidemiology; Retrospective Studies; Risk Assessment - methods; Risk Factors
• ISSN: 0039-6060; 1532-7361; 1532-7361
• DOI: 10.1016/j.surg.2023.02.024

Postoperative length of stay is a meaningful patient-centered outcome and an important determinant of healthcare costs. The Surgical Risk Preoperative Assessment System preoperatively predicts 12 postoperative adverse events using 8 preoperative variables, but its ability to predict postoperative length of stay has not been assessed. We aimed to determine whether the Surgical Risk Preoperative Assessment System variables could accurately predict postoperative length of stay up to 30 days in a broad inpatient surgical population. This was a retrospective analysis of the American College of Surgeons' National Surgical Quality Improvement Program adult database from 2012 to 2018. A model using the Surgical Risk Preoperative Assessment System variables and a 28-variable “full” model, incorporating all available American College of Surgeons' National Surgical Quality Improvement Program preoperative nonlaboratory variables, were fit to the analytical cohort (2012–2018) using multiple linear regression and compared using model performance metrics. Internal chronological validation of the Surgical Risk Preoperative Assessment System model was conducted using training (2012–2017) and test (2018) datasets. We analyzed 3,295,028 procedures. The adjusted R2 for the Surgical Risk Preoperative Assessment System model fit to this cohort was 93.3% of that for the full model (0.347 vs 0.372). In the internal chronological validation of the Surgical Risk Preoperative Assessment System model, the adjusted R2 for the test dataset was 97.1% of that for the training dataset (0.3389 vs 0.3489). The parsimonious Surgical Risk Preoperative Assessment System model can preoperatively predict postoperative length of stay up to 30 days for inpatient surgical procedures almost as accurately as a model using all 28 American College of Surgeons’ National Surgical Quality Improvement Program preoperative nonlaboratory variables and has shown acceptable internal chronological validation.