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Malnutrition and Risk of Procedural Complications in Patients With Atrial Fibrillation Undergoing Catheter Ablation

ABSTRACT: Background: Little is known about the prognostic value of nutritional status among patients undergoing atrial fibrillation (AF) catheter ablation (AFCA). We compared the risk of procedure-related complications and long-term rhythm outcomes of AFCA according to nutritional status. Methods: We included 3,239 patients undergoing de novo AFCA in 2009-2020. Nutritional status was assessed using the controlling nutritional status (CONUT) score. The association between malnutrition and the risk of AFCA complications or long-term rhythm outcomes was evaluated. We validated the effects of malnutrition using an external cohort of 360 patients undergoing AFCA in 2013-2016. Results: In the study population (26.8% women, median age: 58 years), 1,005 (31.0%) had malnutrition (CONUT scores ≥ 2); 991 (30.6%) had mild (CONUT 2–4) and 14 (0.4%) had moderate-to-severe (CONUT ≥ 5) malnutrition. The overall complication rates after AFCA were 3.3% for normal nutrition, 4.2% for mild malnutrition, and 21.4% for moderate-to-severe malnutrition. Moderate-to-severe malnutrition [odds ratio (OR) 6.456, 95% confidence interval (CI) 1.637-25.463, compared with normal nutrition], older age (OR 1.020 per 1-year increase, 95% CI 1.001-1.039), female sex (OR 1.915, 95% CI 1.302-2.817), and higher systolic blood pressure (OR 1.013 per 1-mmHg increase, 95% CI 1.000-1.026) were independent predictors for the occurrence of complications. In the validation cohort, malnutrition (CONUT ≥ 2) was associated with a 2.87-fold higher risk of AFCA complications (95% CI 1.174-7.033). The association between malnutrition and a higher risk of AFCA complications was consistently observed regardless of body mass index and sex. Malnutrition did not affect rhythm outcomes during the median follow-up of 40 months (clinical recurrence: 37.0% in normal nutrition vs. 36.5% in malnutrition). Conclusion: Malnutrition, which is common in patients undergoing AFCA, was associated with a substantially higher risk for complications after AFCA.

SUBMITTER: Kim D

PROVIDER: S-EPMC8572960 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRadiofrequency ablation (RFA) for atrial fibrillation (AF) is associated with a risk of complications. This study aimed to develop and validate risk models for predicting complications after radiofrequency ablation of atrial fibrillation patients.MethodsThis retrospective cohort study included 3365 procedures on 3187 patients with atrial fibrillation at a single medical center from 2018 to 2021. The outcome was the occurrence of postoperative procedural complications during hospitalization. Logistic regression, decision tree, random forest, gradient boosting machine, and extreme gradient boosting were used to develop risk models for any postoperative complications, cardiac effusion/tamponade, and hemorrhage, respectively. Patients' demographic characteristics, medical history, signs, symptoms at presentation, electrocardiographic features, procedural characteristics, laboratory values, and postoperative complications were collected from the medical record. The prediction results were evaluated by performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F score, and Brier score) with repeated fivefold cross-validation.ResultsOf the 3365 RFA procedures, there were 62 procedural complications with a rate of 1.84% in the entire cohort. The most common complications were cardiac effusion/tamponade (28 cases, 0.83%), and hemorrhage (21 cases, 0.80%). There was no procedure-related mortality. The machine learning algorithms of random forest (RF) outperformed other models for any complication (AUC 0.721 vs 0.627 to 0.707), and hemorrhage (AUC 0.839 vs 0.649 to 0.794). The extreme gradient boosting (XGBoost) model outperformed other models for cardiac effusion/tamponade (AUC 0.696 vs 0.606 to 0.662).ConclusionsThe developed risk models using machine learning algorithms showed good performance in predicting complications after RFA of AF patients. These models help identify patients at high risk of complications and guiding clinical decision-making.

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Malnutrition and Risk of Procedural Complications in Patients With Atrial Fibrillation Undergoing Catheter Ablation

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