关键词: 心电图指标, logistic模型, 房颤, 心力衰竭, 射频消融, 复发

Abstract:

Objective To develop and validate a nomogram model for predicting recurrence after radiofrequency catheter ablation （RFCA） in patients with atrial fibrillation and heart failure using body surface electrocardiogram indicators and clinical indicators. Methods We retrospectively analyzed 305 patients with atrial fibrillation complicated with heart failure who underwent RFCA from January 2019 to January 2024. Patients were randomized into training set （213 cases） and validation set （92 cases） at a ratio of 7∶3 and followed up for at least 1 year. Based on the recurrence status， the patients were divided into recurrence group and non-recurrence group， with body surface electrocardiogram indicators and clinical indicators collected. Multivariate logistic regression analysis identified for risk factors for post RFCA recurrence， which were used to construct a nomogram. Model performance was assessed using the area under the receiver operating characteristic curve （AUC）， Hosmer-Lemeshow test， calibration curves， and decision curve analysis （DCA）. Results Among the 305 patients， 84 （27.54%） experienced recurrence after treatment. In the training set， 61 patients had recurrence and 152 did not. No statistical differences were observed between the training set and the validation set （all P > 0.05）. In the training set， the recurrence group exhibited a higher proportion of persistent atrial fibrillation and significantly higher CHA2DS2-VASc scores， larger left atrial diameter， longer PR interval， and higher levels of NLR and NT-proBNP compared to the non-recurrence group （all P < 0.05）. Multivariate stepwise regression analysis revealed that high CHA2DS2-VASc score， long left atrial diameter， prolonged PR interval， and high NLR were independent risk factors of recurrence after RFCA （P < 0.05） A four-factor prediction model was established as： Ln （P/1-P） = -12.87 + 0.84*CHA2DS2-VASc score + 0.11* left atrial diameter + 0.03*PR interval + 0.31*NLR. The training and validation models showed AUCs of 0.85（95%CI： 0.80 ~ 0.91） and 0.85 （95%CI： 0.76 ~ 0.94）， respectively， suggesting that the model had good predictive efficiency. Hosmer-Lemeshow test results （χ2 = 2.43， P = 0.965 for the training set； χ2 = 5.30， P = 0.725 for the validation set） confirmed model fit， indicating that the fitted probability value was consistent with the actual probability value. Calibration curves after 1 000 times of Bootstrap repeated sampling showed the bias calibration curves of the training set and the validation set had good consistency with the actual curves， both close to the ideal curve. DCA revealed clinical utility across a wide threshold probability range （0.02 ~ 1.0 for the training set； 0.04 ~ 1.0 for the validation set）. Conclusion This nomogram， based on body surface electrocardiogram indicators and clinical indicators， effectively predicts post-RFCA recurrence in atrial fibrillation and heart failure patients， offering a useful tool for early assessment of recurrence risk.

Key words: electrocardiogram indicators, logistic model, atrial fibrillation, heart failure, radiofrequency catheter ablation, recurrence