RESUMO
Intensive care unit (ICU) costs comprise a significant proportion of the total inpatient charges for cardiac surgery. No reliable method for predicting intensive care unit length of stay following cardiac surgery exists, making appropriate staffing and resource allocation challenging. We sought to develop a predictive model to anticipate prolonged ICU length of stay (LOS). All patients undergoing coronary artery bypass grafting (CABG) and/or valve surgery with a Society of Thoracic Surgeons (STS) predicted risk score were evaluated from an institutional STS database. Models were developed using 2014-2017 data; validation used 2018-2019 data. Prolonged ICU LOS was defined as requiring ICU care for at least three days postoperatively. Predictive models were created using lasso regression and relative utility compared. A total of 3283 patients were included with 1669 (50.8%) undergoing isolated CABG. Overall, 32% of patients had prolonged ICU LOS. Patients with comorbid conditions including severe COPD (53% vs 29%, P < 0.001), recent pneumonia (46% vs 31%, P < 0.001), dialysis-dependent renal failure (57% vs 31%, P < 0.001) or reoperative status (41% vs 31%, P < 0.001) were more likely to experience prolonged ICU stays. A prediction model utilizing preoperative and intraoperative variables correctly predicted prolonged ICU stay 76% of the time. A preoperative variable-only model exhibited 74% prediction accuracy. Excellent prediction of prolonged ICU stay can be achieved using STS data. Moreover, there is limited loss of predictive ability when restricting models to preoperative variables. This novel model can be applied to aid patient counseling, resource allocation, and staff utilization.
