RESEARCH PAPER
Risk factors and prediction model construction of post-operative atrial fibrillation after thoracic surgery in Chinese patients
Yunling Lin 1  
,   Jianmin Sun 2  
,   Xun Yuan 1  
,   Hui Liu 3  
 
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1
Department of Cardiology, Fujian Medical University Union Hospital, Fujian Institute of Coronary Artery Disease, Fujian Heart Medical Center, Fuzhou, Fujian, China
2
Fujian Medical University, FuZhou Fujian, China
3
Fujian Medical University Union Hospital, Fuzhou, Fujian, China
CORRESPONDING AUTHOR
Yunling Lin   

Department of Cardiology, Fujian Medical University Union Hospital, Fujian Institute of Coronary Artery Disease, Fujian Heart Medical Center, Fuzhou, Fujian, China, 350001, China
Submission date: 2020-05-19
Final revision date: 2020-11-20
Acceptance date: 2020-12-01
Online publication date: 2021-02-09
 
 
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ABSTRACT
Introduction:
The purpose of this study was to analyze the risk factors of post-operative atrial fibrillation (POAF) after thoracic surgery, and to build a predictive model for accurate preoperative identification of high-risk patients.

Material and methods:
In this study, data of 2072 patients with pulmonary masses and esophageal cancer who attended our hospital in the period from January 1, 2017 to December 31, 2018 were analyzed retrospectively. According to whether AF occurred after the operation, the patients were divided into atrial fibrillation (AF) and non-AF (NAF) groups. The general information (age, sex, height, etc.), previous medical history (chronic lung disease, hypertension, etc.), medication history, preoperative ultrasound and cardiogram results, and preoperative and postoperative electrocardiogram (ECG) were collected. The operation mode, resection scope, histopathology and hospitalization were recorded. Univariate and multivariate logistic regression were used to screen out the risk factors of AF and establish a prediction model.

Results:
The incidence of POAF was 5.98%. Univariate analysis showed that sex, age, body mass index, left atrial diameter and operation organ were the risk factors of POAF. The above factors were included in the multivariate logistic regression analysis, and the results showed that male sex, age, anteroposterior diameter of left atrium and surgical organs were related to POAF. On this basis, a POAF prediction model was constructed, which had good discrimination and calibration. The area under the curve (AUC) is 0.784 with 95% CI: 0.746–0.822.

Conclusions:
The prediction model of POAF based on the risk factors selected in this study can accurately predict the occurrence of AF after thoracic surgery.

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ISSN:1734-1922