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.

Keywords: Artificial intelligence; atrial fibrillation; coronary artery bypass grafting; machine learning; predictors..

Figure 1. Boruta feature selection algorithm. ALP: Alkalen phosphatase; ALT: Alanin animotransferase; APTT: Activated partial thromboplastin time; AST: Aspartat amino transferase; BMI: Body mass index; BSA: Body surface area; BUN: Blood urea nitrogene; Ca: Calcium; Cl: Chlorine; COPD: Chronic obstructive pulmonary disease; CRP: C-reactive protein; Cx: Cross clamp time; D.Bil: Direct bilirubin; DM: Diabetes mellitus; EF: Ejection fraction; Eos: Eosinophyl ; Fe: Iron; Ferr: Ferritin; GGT: Gama glutamil transferaz; Glu: Glucose; Hb: Hemoglobin; HbsAg: HbS antigen; Hct: Haematocrit; HCV: Hepatit C virus; HDL: High density lipoprotein; HIV: Human immun deficiency virus; I.Bil: Indirect bilirubin; INR: International normalized ratio; Kal: Kalium; Krea: Creatinin; LA: Left atrium diameter; LDH: Lactate dehydrogenase; LDL: Low debsity lipoprotein; Leu: Leucocyte count; Lip: Lipase; LVDD: Left ventricle diastolic diameter; Lym: Lymphocyte count; MCH: Mean corpuscular hemoglobin; MCHC: Mean corpuscular hemoglobin concentration; MCV: Mean corpuscular volume; Mg: Magnesium; MinAI: Minimal aortic insufficiency; MinAS: Minimal aortic stenosis; MinMI: Minimal mitral insufficiency; MinMS: Minimal mitral stenosis; MinTI: Minimal tricuspit insufficiency; ModMS: Moderate mitral stenosis; ModAI: Moderate aortic insufficiency; ModAS: Moderate aortic stenosis; ModMI: Moderate mitral insufficiency; ModTI: Moderate tricuspid insufficiency; Mon: Monocyte count; mPAP: Mean pulmonary artery pressure; MPV: Mean platelet volume; Neu: Neutophil count; Pho: Phosphor; Plt: Platelet count; pPAP: Pea pulmonary artery pressure; RBC: Red blood cell count; SevAI. Severe aortic insufficiency; SevAS: Severe aortic stenosis; SevMI: Severe mitral insufficiency; SevMS: Severe mitral stenosis; SevTI: Severe tricuspid insufficiency; ST3: Free T3; ST4: Free T4; T.Bil: Total bilirubin; TIBC: Total iron binding capacity; Trigl: Triglyceride; Trop: Troponin; TT: Thromboplastin time; VLDL: Very low density lipoproetin; WBC: Wight blood cell.

Figure 2. Histogram chart of highly ranked parameters. Glu: Glucose; CPB: Cardiopulmonary bypass time; Mg: Magnesium; Pho: Phosphate; TIBC: Total iron binding capacity; WBC: Wight blood cell count; Hb: Hemoglobin; Hct: Hematocrit; Alb: Albumin; Cx: Cross clamp; EF: Ejection fraction; LA: Left atrium diameter.

Figure 3. Decision tree of most important properties. Mg: Magnesium; TIBC: Total iron binding capacity.

Figure 4. Accuracy of DT model. POAF: Postoperative atrial fibrillation; DT: Decision tree.

Figure 5. ROC analysis of ML models. ROC: Receiver operating characteristic; DT: Decision tree; KNN: K Nearest Neighbour; PDA: Probabilistic Data Association; NB: Naive bayes; RF: Random ferns; ML: Machine learning; AUC: Area under the curve.