Key result
Four-factor clinical model predicts POAF after thoracic surgery with an AUC of ~0.78.
Why the study?
This study was conducted to analyze risk factors for postoperative atrial fibrillation after thoracic surgery and to construct a predictive model for preoperative identification of high-risk patients.
Cohort (n=2,072)
No
Effect estimate: AUC 0.784 (95% CI 0.746-0.822)
p-value: p=<0.001
A prediction model incorporating male sex, age, left atrial anteroposterior diameter, and surgical organ accurately predicts the risk of post-operative atrial fibrillation after thoracic surgery in Chinese patients.
May aid preoperative risk stratification in thoracic surgery; leaves open external validation before clinical adoption.
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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Lin et al. (2021) conducted a cohort in Pulmonary masses and esophageal cancer undergoing thoracic surgery (n=2,072). Prediction model (male sex, age, anteroposterior diameter of left atrium, surgical organs) was evaluated on Post-operative atrial fibrillation (POAF) prediction model discrimination (AUC) (AUC 0.784, 95% CI 0.746-0.822, p=<0.001). A prediction model incorporating male sex, age, left atrial anteroposterior diameter, and surgical organ accurately predicted post-operative atrial fibrillation after thoracic surgery with an AUC of 0.784.
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