Multi-institutional model to predict intensive care unit length of stay after cardiac surgery

Objective: Intensive care unit length of stay (ICU LOS) accounts for a large percentage of inpatient cost after cardiac surgery. The Society of Thoracic Surgeons risk calculator predicts total LOS but does not discriminate between ICU and non-ICU time. We sought to develop a predictive model of prolonged ICU LOS.

Methods: Adult patients undergoing Society of Thoracic Surgeons index operations within a regional collaborative (2014-2021) were included. Prolonged ICU LOS was defined as ICU care for ≥72 hours postoperatively. A logistic regression model was used to develop a prediction model for the prolonged ICU LOS with prespecified risk factors identified from our previous single-center study. Internal prediction model validation was determined by bootstrapping resampling method. The prediction model performance was assessed by measures of discrimination and calibration.

Results: We identified 37,519 patients that met inclusion criteria with 11,801 (31.5%) patients experiencing prolonged ICU stay. From the logistic regression model, there were significant associations between prolonged ICU LOS and all pre-specified factors except sleep apnea (all P < .05). Model for End-Stage Liver Disease, preoperative intra-aortic balloon pump use, and procedure types were the most significant predictors of prolonged ICU LOS (all P < .0001). Our prediction model had not only a good discrimination power (bootstrapped-corrected C-index = 0.71) but also excellent calibration (bootstrapped-corrected mean absolute error = 0.005).

Conclusions: Prolonged ICU stay after cardiac surgery can be predicted with good predictive accuracy using preoperative data and may aid in patient counseling and resource allocation. Through use of a state-wide database, the application of this model may extend to other practices.