A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery.

Background: Postoperative admission to the ICU for surgical patients is a significant burden in nursing care, and there is currently a lack of corresponding assessment tools.

Methods: Clinical information of patients was extracted from the VitalDB database. LASSO regression and random forest algorithms were used to screen clinical variables related to postoperative ICU admission. Subsequently, the effectiveness of logistic regression, random forest, support vector machine, and multi-layer perceptron algorithms was compared using ROC curves. After selecting the best algorithm, postoperative ICU admission probability prediction nomogram was constructed.

Results: This study identified 18 clinical factors that influence postoperative ICU admission. The factors influencing patient outcomes include three physiological characteristics: age, weight, and gender; five preoperative laboratory tests:platelet count, prothrombin time(%),activated partial thromboplastin time, albumin, and blood urea nitrogen; and seven intraoperative anesthesia details: anesthesia duration, propofol dosing during surgery, midazolam dosing during surgery, phenylephrine dosing during surgery, calcium chloride dosing during surgery, American Society of Anesthesiologists (ASA) classification, and anesthesia method. Additionally, three other factors are considered: whether the surgery is classified as an emergency, the department category, and the type of surgery. The logistic regression model developed using these 18 variables was identified as the most effective predictive model for postoperative ICU admission, achieving an ROC AUC of 0.925.

Conclusion: The postoperative admission warning model constructed in this study can effectively predict the probability of patients being admitted to the ICU after surgery, providing a corresponding management tool for postoperative care in surgical patients.