Artificial intelligence to predict biomarkers for new-onset atrial fibrillation after coronary artery bypass grafting.

Abstract

Background: This study aims to identify predictors of postoperative atrial fibrillation in coronary artery bypass grafting patients using routinely collected preoperative tests.

Methods: Between January 2020 and December 2023, a total of 50 patients with postoperative atrial fibrillation (POAF group; 39 males, 11 females; mean age: 65.9±8.3 years; range, 38 to 77 years) and 50 without postoperative atrial fibrillation (non-POAF group; 41 males, 9 females; mean age: 61.8±10.0 years; range, 41 to 81 years) were randomly selected from a group of patients undergoing two or three-vessel coronary artery bypass grafting. We analyzed preoperative laboratory, demographic and intraoperative data using machine learning models.

Results: The overall incidence of postoperative atrial fibrillation was 21.69%. The three most effective biomarkers were magnesium, total iron binding capacity, and albumin, respectively. A total of 2.0 mg/dL value of magnesium was identified as a threshold value. Magnesium values below 2.0 mg/dL were considered atrial fibrillation-positive, accounting for 25% of the dataset. Total iron binding capacity values higher than 442 µg/dL were considered atrial fibrillation-positive, accounting for 12% of the dataset. The threshold value for albumin was 29 g/dL, and patients with values under this value were considered atrial fibrillation-positive, accounting for 4% of the dataset.

Conclusion: Machine learning models demonstrate encouraging results in identifying risk factors for many entities. It is of utmost importance to establish a ranking among risk factors and determine threshold values to support clinicians in decision making. This is our first experience with machine learning in this patient group after cardiac surgery. Further studies are warranted to confirm these data.