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Prediction of Postcoronary Artery Bypass Grafting Atrial Fibrillation: POAFRiskScore Tool
Ahmet Kadir Arslan1, Nevzat Erdil2, Emek Guldogan1
1Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Turkey.
Insights
This study identified risk factors for postoperative atrial fibrillation (POAF) after heart bypass surgery and developed a tool to predict POAF risk before surgery. The POAFRiskScore v.1.0 aids clinicians in preoperative risk assessment.
Area of Science:
- Cardiology
- Medical Informatics
- Health Services Research
Background:
- Atrial fibrillation (AF) is a common complication after coronary artery bypass grafting (CABG), with varying reported incidence and risk factors.
- Accurate prediction of postoperative AF (POAF) is crucial for patient management and risk stratification.
Purpose of the Study:
- To analyze risk factors associated with POAF following CABG.
- To develop and present a software tool for predicting POAF risk preoperatively.
Main Methods:
- Retrospective analysis of 1,667 patients undergoing CABG.
- Multivariable logistic regression to identify independent risk factors for POAF.
- Development of a web-based prediction tool using R Shiny.
Main Results:
- The incidence of POAF was 12.2%.
- Independent risk factors for POAF included preoperative carotid artery stenosis (PCAS), chronic obstructive pulmonary disease (COPD), body mass index, age, and platelet count.
- Significant univariate associations were found with age, blood urea nitrogen, platelets, triglycerides, COPD, and PCAS.
Conclusions:
- The developed tool, POAFRiskScore v.1.0, can assist clinicians in predicting preoperative POAF risk.
- Future updates with larger datasets are expected to enhance prediction robustness.
Background:
Atrial fibrillation (AF), a condition that might occur after a heart bypass procedure, has caused differing estimates of its occurrence and risk. The current study analyses the possible risk factors of post-coronary artery bypass grafting (post-CABG) AF (postoperative AF [POAF]) and presents a software for preoperative POAF risk prediction.
Methods:
This retrospective research was performed on 1,667 patients who underwent CABG surgery using the hospital database. The associations between the variables of the patients and AF risk factors after CABG were examined using multivariable logistic regression (LR) after preprocessing the relevant data. The tool was designed to predict POAF risk using Shiny, an R package, to develop a web-based software.
Results:
The overall proportion of post-CABG AF was 12.2%. According to the results of univariate tests, in terms of age (p < 0.001), blood urea nitrogen (p = 0.005), platelet (p < 0.001), triglyceride (p = 0.0026), presence of chronic obstructive pulmonary disease (COPD; p = 0.01), and presence of preoperative carotid artery stenosis (PCAS; p < 0.001), there were statistically significant differences between the POAF and non-POAF groups. Multivariable LR analysis disclosed the independent risk factors associated with POAF: PCAS (odds ratio [OR] = 2.360; p = 0.028), COPD (OR = 2.243; p = 0.015), body mass index (OR = 1.090; p = 0.006), age (OR = 1.054, p < 0.001), and platelet (OR = 0.994, p < 0.001).
Conclusion:
The experimental findings from the current research demonstrate that the suggested tool (POAFRiskScore v.1.0) can help clinicians predict POAF risk development in the preoperative period after validated on large sample(s) that can represent the related population(s). Simultaneously, since the updated versions of the proposed tool will be released periodically based on the increases in data dimensions with continuously added new samples and related factors, more robust predictions may be obtained in the subsequent stages of the current study in statistical and clinical terms.
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