Development and validation of a prediction model for postoperative intensive care unit admission in patients with non-cardiac surgery.

• Using an open vitalDB database including general, thoracic, urological, and gynecological surgery patients, potential predictors for postoperative ICU stay were selected by the advanced least absolute shrinkage and selection operator (LASSO) algorithm. • A nomogram combining preoperative and intraoperative variables was developed and internal validation showed excellent discrimination and calibration, which incorporates age, ASA classification, surgical department, emergency surgery, preoperative albumin level, preoperative urea nitrogen level, intraoperative crystalloid, intraoperative transfusion, intraoperative catheterization, and surgical time. • The nomogram provides an easy-to-use tool for the clinical team to assess an individual's risk of admission to ICU after non-cardiac surgery. Accurately forecasting patients admitted to the intensive care units (ICUs) after surgery may improve clinical outcomes and guide the allocation of expensive and limited ICU resources. However, studies on predicting postoperative ICU admission in non-cardiac surgery have been limited. To develop and validate a prediction model combining pre- and intraoperative variables to predict ICU admission after non-cardiac surgery. This study is based on data from the Vital Signs DataBase (VitalDB) database. Predictors were selected using the least absolute shrinkage and selection operator regression method and logistic regression to develop a nomogram and an online web calculator. The model was internally verified by 1000-Bootstrap resampling. Performance of model was evaluated using area under the receiver operating characteristic curve (AUC), calibration curve and Brier score. The Youden's index was used to find the optimal nomogram's probability threshold. Clinical utility was assessed by decision curve analysis. This study included 5216 non-cardiac surgery patients; of these, 812 (15.6%) required postoperative ICU admission. Potential predictors included age, ASA classification, surgical department, emergency surgery, preoperative albumin level, preoperative urea nitrogen level, intraoperative crystalloid, intraoperative transfusion, intraoperative catheterization, and surgical time. A nomogram was constructed with an AUC of 0.917 (95% CI: 0.907–0.926) and a Brier score of 0.077. The Bootstrap-adjusted AUC was 0.914; the adjusted Brier score was 0.078. The calibration curve showed good agreement between predicted and actual probabilities; and the decision curve indicated clinical usefulness. Finally, we established an online web calculator for clinical application (https://xuzhikun.shinyapps.io/postopICUadmission1/). We developed and internally validated an easy-to-use nomogram for predicting ICU admission after non-cardiac surgery. [ABSTRACT FROM AUTHOR]