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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

Ahmet Kadir Arslan
1   Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Turkey
,
Nevzat Erdil
2   Department of Cardiovascular Surgery, Faculty of Medicine, Inonu University, Malatya, Turkey
,
Emek Guldogan
1   Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Turkey
,
Cemil Colak 
1   Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Turkey
,
Baris Akca 
2   Department of Cardiovascular Surgery, Faculty of Medicine, Inonu University, Malatya, Turkey
,
M. Cengiz Colak
2   Department of Cardiovascular Surgery, Faculty of Medicine, Inonu University, Malatya, Turkey
› Author Affiliations Funding This study is a part of the research project numbered TCD-2017-761 (ID: 761) supported by the Inonu University Scientific Research Projects Coordination Unit.
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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


Publication History

Received: 04 March 2021

Accepted: 27 July 2021

Article published online:
11 December 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
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