Development and Validation of a Prediction Score for Low-Cardiac-Output Syndrome After Adult Cardiac Surgery

Area of Science:

• Cardiology
• Cardiac Surgery
• Clinical Prediction Models

Background:

• Low-cardiac-output syndrome (LCOS) is a significant complication following adult cardiac surgery.
• Early identification of patients at high risk for LCOS is crucial for optimizing perioperative management and improving patient outcomes.

Purpose of the Study:

• To develop and validate a simple clinical prediction score for identifying patients at high risk of developing low-cardiac-output syndrome after adult cardiac surgery.

Main Methods:

• A retrospective, observational study was conducted on 2,806 adult patients undergoing on-pump cardiac surgery.
• A multivariate logistic regression model was used to identify independent risk factors for LCOS.
• A clinical prediction score was derived from the regression coefficients and validated using bootstrap resampling.

Main Results:

• The final prediction model included 8 risk factors: preoperative left ventricular ejection fraction, impaired renal function, combined surgery, nonelective surgery, mitral valve regurgitation surgery, extracardiac arteriopathy, low preoperative hemoglobin, and advanced New York Heart Association functional class.
• The model demonstrated good discriminative ability (AUC = 0.80).
• The derived score showed a sensitivity of 68% and specificity of 79% for predicting LCOS.

Conclusions:

• A simple clinical prediction score was successfully developed to identify patients at high risk of low-cardiac-output syndrome post-adult cardiac surgery.
• This score can aid clinicians in tailoring patient management strategies for early intervention and potentially reducing LCOS incidence.