A Predictive Model and Risk Score for Unplanned Cardiac Surgery Intensive Care Unit Readmissions

Area of Science:

• Cardiology
• Intensive Care Medicine
• Health Services Research

Background:

• Readmission to the intensive care unit (ICU) after cardiac surgery increases morbidity and mortality.
• Identifying factors predicting ICU readmissions is crucial for improving patient outcomes.

Purpose of the Study:

• To identify clinical and system-based factors associated with ICU readmissions after cardiac surgery.
• To develop a predictive score, the Bounce Back After Transfer (BATS) score, for ICU readmissions.

Main Methods:

• Prospective data collection of clinical and financial records for patients undergoing coronary artery bypass grafting (CABG) or surgical aortic valve replacement (AVR).
• Multivariable logistic regression analysis to identify independent predictors of ICU readmissions.
• Development of the BATS prediction score based on identified predictors.

Main Results:

• Out of 532 patients, 35 (6.6%) experienced ICU readmissions.
• Predictors for ICU bounce backs included female sex, NYHA class III/IV, urgent/emergent operative status, and postoperative renal failure.
• Bounce back rates were 3.0% for low-risk, 10.4% for moderate-risk, and 42% for high-risk BATS score cohorts.
• ICU bounce backs increased total hospital charges by an average of $68,030.

Conclusions:

• A predictive model (BATS) can effectively determine the risk of ICU readmission after transfer to the general floor.
• Implementing the BATS score upon ICU transfer can enable targeted floor care.
• Targeted care based on BATS scores may decrease ICU readmission rates, postoperative morbidity, mortality, and healthcare costs.