Exploratory Modeling of Postoperative Atrial Fibrillation After Cardiac Surgery with Cardiopulmonary Bypass Using Inflammatory Biomarkers and Clinical-Surgical Factors

Background/Objectives: Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery with cardiopulmonary bypass (CPB), increasing morbidity and prolonging hospitalization. This study aimed to develop and validate an exploratory prediction model that integrates perioperative inflammatory biomarkers with clinical and surgical variables to identify patients at risk of early POAF. Methods: A prospective exploratory cohort of 89 patients undergoing coronary artery bypass grafting (CABG; n = 36), valve surgery (n = 40), or CABG–valve surgery (n = 13) was evaluated. Clinical, surgical, and proinflammatory serum biomarkers (IL-6, IL-8, IL-10, and CRP) were recorded preoperatively (T1) and at 24 h (T2) and 48 h (T3) postoperatively. Multiple-comparison adjustments were made using the Benjamini–Hochberg false discovery rate. Predictor selection was based on bootstrap-derived stability using LASSO-penalized logistic regression, and the final model was estimated using Firth’s bias-reduced logistic regression. Results: POAF incidence was 8.3% in CABG, in contrast to 22.5% and 30.8% in valve and CABG-valve surgeries, respectively. After multiple-comparison corrections, only IL-6 at T2 postoperatively was significantly higher in patients who subsequently developed POAF. Bootstrap-based stability selection retained T2 postoperative IL-10 and magnesium concentrations in the final model, which achieved an apparent AUC of 0.776 and a bootstrap optimism-corrected AUC of 0.728, with acceptable calibration (Brier score = 0.103), negligible multicollinearity (VIF = 1.04), and a negative predictive value of 95.5% at the optimal Youden threshold. Conclusions: Our findings support an exploratory prediction model with moderate discrimination for POAF after cardiac surgery with CPB, providing a methodological foundation for future multicenter validation studies.

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Journal
Medical Sciences
Published
2026-08-25
DOI
https://doi.org/10.3390/medsci14050513
Primary Topic
Adipokines, Inflammation, and Metabolic Diseases
Type
article
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0.00

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article

Exploratory Modeling of Postoperative Atrial Fibrillation After Cardiac Surgery with Cardiopulmonary Bypass Using Inflammatory Biomarkers and Clinical-Surgical Factors

Eliseo Portilla‐de Buen, Ramón Arreola-Torres, Karla Mayela Bravo-Villagra, Marina María de Jesús Romero‐Prado et al.
Medical Sciences
Adipokines, Inflammation, and Metabolic Diseases
article

Exploratory Modeling of Postoperative Atrial Fibrillation After Cardiac Surgery with Cardiopulmonary Bypass Using Inflammatory Biomarkers and Clinical-Surgical Factors

Eliseo Portilla‐de Buen, Ramón Arreola-Torres, Karla Mayela Bravo-Villagra, Marina María de Jesús Romero‐Prado, Rosa Michel Martínez-Contreras, Aneth Karine Sánchez-Soto, Ana Rebeca Jaloma-Cruz, Jorge Straffon-Castañeda, Guillermo Alejandro Muñoz-Benavides, Joel Regalado-Silva, José Marco Medina-Carrillo
article en

Abstract

Background/Objectives: Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery with cardiopulmonary bypass (CPB), increasing morbidity and prolonging hospitalization. This study aimed to develop and validate an exploratory prediction model that integrates perioperative inflammatory biomarkers with clinical and surgical variables to identify patients at risk of early POAF. Methods: A prospective exploratory cohort of 89 patients undergoing coronary artery bypass grafting (CABG; n = 36), valve surgery (n = 40), or CABG–valve surgery (n = 13) was evaluated. Clinical, surgical, and proinflammatory serum biomarkers (IL-6, IL-8, IL-10, and CRP) were recorded preoperatively (T1) and at 24 h (T2) and 48 h (T3) postoperatively. Multiple-comparison adjustments were made using the Benjamini–Hochberg false discovery rate. Predictor selection was based on bootstrap-derived stability using LASSO-penalized logistic regression, and the final model was estimated using Firth’s bias-reduced logistic regression. Results: POAF incidence was 8.3% in CABG, in contrast to 22.5% and 30.8% in valve and CABG-valve surgeries, respectively. After multiple-comparison corrections, only IL-6 at T2 postoperatively was significantly higher in patients who subsequently developed POAF. Bootstrap-based stability selection retained T2 postoperative IL-10 and magnesium concentrations in the final model, which achieved an apparent AUC of 0.776 and a bootstrap optimism-corrected AUC of 0.728, with acceptable calibration (Brier score = 0.103), negligible multicollinearity (VIF = 1.04), and a negative predictive value of 95.5% at the optimal Youden threshold. Conclusions: Our findings support an exploratory prediction model with moderate discrimination for POAF after cardiac surgery with CPB, providing a methodological foundation for future multicenter validation studies.

Medical SciencesVol. 14(5)
Universidad Autónoma de Guadalajara (MX), Mexican Social Security Institute (MX), Universidad de Guadalajara (MX), Hospital General de México (MX), Secretaria de Salud (MX), Hospital de Especialidades (MX)
Universidad de Guadalajara
Peace, Justice and strong institutions
Openalex Percentile: Top 10%
Adipokines, Inflammation, and Metabolic Diseases
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