A novel nomogram for predicting 3-year mortality in critically ill patients after coronary artery bypass grafting

Area of Science:

• Cardiovascular Surgery
• Critical Care Medicine
• Medical Informatics

Background:

• Long-term outcomes after coronary artery bypass grafting (CABG) are a growing concern.
• Existing models primarily focus on in-hospital mortality, with limited research on long-term prediction.
• There is a need for reliable tools to predict mortality beyond the immediate postoperative period.

Purpose of the Study:

• To develop and validate a novel nomogram for predicting 3-year mortality in critically ill patients following CABG.
• To create a tool that offers improved prognostic accuracy compared to existing methods.

Main Methods:

• Utilized data from the Medical Information Mart for Intensive Care III (MIMIC-III) database.
• Enrolled 2929 critically ill patients who underwent CABG during their first admission.
• Employed multivariable logistic regression to identify independent prognostic factors.

Main Results:

• A nomogram was constructed using seven independent prognostic factors: age, congestive heart failure, white blood cell count, creatinine, SpO2, anion gap, and continuous renal replacement treatment.
• The nomogram demonstrated accurate discrimination in both primary (AUC: 0.81) and validation cohorts (AUC: 0.802).
• Calibration curves and decision curve analysis confirmed its superior performance and clinical utility over traditional severity scores.

Conclusions:

• The developed nomogram exhibits strong performance in predicting 3-year mortality for critically ill patients post-CABG.
• This predictive model offers valuable insights for treatment strategies and post-discharge management.
• The tool has the potential to enhance long-term prognosis for this patient population.