Prediction of Postcoronary Artery Bypass Grafting Atrial Fibrillation: POAFRiskScore Tool

Ahmet Kadir Arslan; Nevzat Erdil; Emek Guldogan; Cemil Colak; Baris Akca; M. Cengiz Colak

doi:10.1055/s-0041-1736245

The Thoracic and Cardiovascular Surgeon, Table of Contents

Thorac Cardiovasc Surg 2023; 71(04): 282-290
DOI: 10.1055/s-0041-1736245

Original Cardiovascular

Prediction of Postcoronary Artery Bypass Grafting Atrial Fibrillation: POAFRiskScore Tool

Authors

Ahmet Kadir Arslan

¹Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Turkey
Nevzat Erdil

²Department of Cardiovascular Surgery, Faculty of Medicine, Inonu University, Malatya, Turkey
Emek Guldogan

¹Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Turkey
Cemil Colak

¹Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Turkey
Baris Akca

²Department of Cardiovascular Surgery, Faculty of Medicine, Inonu University, Malatya, Turkey
M. Cengiz Colak

²Department of Cardiovascular Surgery, Faculty of Medicine, Inonu University, Malatya, Turkey

Abstract

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Abstract

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.

Keywords

postoperative atrial fibrillation - risk prediction - risk factors - prediction software

Full Text

References

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