Kozieł-Siołkowska Monika, Siołkowski Sebastian, Mihajlovic Miroslav, Lip Gregory Y H, Potpara Tatjana S
Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.
1st Department of Cardiology and Angiology, Silesian Centre for Heart Diseases, Zabrze, Poland.
TH Open. 2022 Sep 23;6(3):e283-e290. doi: 10.1055/s-0042-1755617. eCollection 2022 Jul.
Compared with usual care, guideline-adherent stroke prevention strategy, based on the ABC (Atrial fibrillation Better Care) pathway, is associated with better outcomes. Given that stroke prevention is central to atrial fibrillation (AF) management, improved efforts to determining predictors of adherence with 'A' (avoid stroke) component of the ABC pathway are needed. We tested the hypothesis that more sophisticated methodology using machine learning (ML) algorithms could do this. In this post-hoc analysis of the BALKAN-AF dataset, ML algorithms and logistic regression were tested. The feature selection process identified a subset of variables that were most relevant for creating the model. Adherence with the 'A' criterion of the ABC pathway was defined as the use of oral anticoagulants (OAC) in patients with AF with a CHA DS -VASc score of 0 (male) or 1 (female). Among 2,712 enrolled patients, complete data on 'A'-adherent management were available in 2,671 individuals (mean age 66.0 ± 12.8; 44.5% female). Based on ML algorithms, independent predictors of 'A-criterion adherent management' were paroxysmal AF, center in capital city, and first-diagnosed AF. Hypertrophic cardiomyopathy, chronic kidney disease with chronic dialysis, and sleep apnea were independently associated with a lower likelihood of 'A'-criterion adherent management. ML evaluated predictors of adherence with the 'A' criterion of the ABC pathway derived an area under the receiver-operator curve of 0.710 (95%CI 0.67-0.75) for random forest with fine tuning. Machine learning identified paroxysmal AF, treatment center in the capital city, and first-diagnosed AF as predictors of adherence to the A pathway; and hypertrophic cardiomyopathy, chronic kidney disease with chronic dialysis, and sleep apnea as predictors of non adherence.
与常规护理相比，基于ABC（房颤更佳护理）路径的遵循指南的卒中预防策略与更好的结局相关。鉴于卒中预防是房颤（AF）管理的核心，需要加大力度确定ABC路径中“A”（避免卒中）部分的依从性预测因素。我们检验了这样一个假设，即使用机器学习（ML）算法的更复杂方法可以做到这一点。在对巴尔干房颤数据集的这项事后分析中，对ML算法和逻辑回归进行了测试。特征选择过程确定了一组与创建模型最相关的变量。ABC路径“A”标准的依从性定义为CHA DS -VASc评分为0（男性）或1（女性）的房颤患者使用口服抗凝剂（OAC）。在2712名登记患者中，2671名个体（平均年龄66.0±12.8岁；44.5%为女性）有关于“A”依从性管理的完整数据。基于ML算法，“A标准依从性管理”的独立预测因素为阵发性房颤、位于首都的中心以及首次诊断的房颤。肥厚型心肌病、慢性透析的慢性肾病和睡眠呼吸暂停与“A”标准依从性管理的可能性较低独立相关。对ABC路径“A”标准依从性的预测因素进行ML评估，随机森林经微调后的受试者工作特征曲线下面积为0.710（95%CI 0.67 - 0.75）。机器学习确定阵发性房颤、位于首都的治疗中心和首次诊断的房颤是A路径依从性的预测因素；肥厚型心肌病、慢性透析的慢性肾病和睡眠呼吸暂停是非依从性的预测因素。
