Multi-Institutional Model to Predict Intensive Care Unit Length of Stay after Cardiac Surgery.

Abstract

Objective: Intensive care unit length of stay (ICU LOS) accounts for a large percent of inpatient cost following cardiac surgery. The Society of Thoracic Surgeons (STS) 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 STS 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 utilized to develop a prediction model for the prolonged ICU LOS with pre-specified 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<0.05). MELD, preoperative intra-aortic balloon pump use, and procedure types were the most significant predictors of prolonged ICU LOS (all p<0.0001). 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 following cardiac surgery can be predicted with good predictive accuracy utilizing 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.