Kallenberger Stefan M, Schmid Christian, Wiedmann Felix, Mereles Derliz, Katus Hugo A, Thomas Dierk, Schmidt Constanze
Department for Bioinformatics and Functional Genomics, Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany.
PLoS One. 2016 Sep 28;11(9):e0163621. doi: 10.1371/journal.pone.0163621. eCollection 2016.
Paroxysmal atrial fibrillation (pAF) is a major risk factor for stroke but remains often unobserved. To predict the presence of pAF, we developed model scores based on echocardiographic and other clinical parameters from routine cardiac assessment. The scores can be easily implemented to clinical practice and might improve the early detection of pAF. In total, 47 echocardiographic and other clinical parameters were collected from 1000 patients with sinus rhythm (SR; n = 728), pAF (n = 161) and cAF (n = 111). We developed logistic models for classifying between pAF and SR that were reduced to the most predictive parameters. To facilitate clinical implementation, linear scores were derived. To study the pathophysiological progression to cAF, we analogously developed models for cAF prediction. For classification between pAF and SR, amongst 12 selected model parameters, the most predictive variables were tissue Doppler imaging velocity during atrial contraction (TDI, A'), left atrial diameter, age and aortic root diameter. Models for classifying between pAF and SR or between cAF and SR showed areas under the ROC curves of 0.80 or 0.93, which resembles classifiers with high discriminative power. The novel risk scores were suitable to predict the presence of pAF based on variables readily available from routine cardiac assessment. Modelling helped to quantitatively characterize the pathophysiologic transition from SR via pAF to cAF. Applying the scores may improve the early detection of pAF and might be used as decision aid for initiating preventive interventions to reduce AF-associated complications.
阵发性心房颤动（pAF）是中风的主要危险因素，但常常未被观察到。为了预测pAF的存在，我们基于常规心脏评估中的超声心动图和其他临床参数开发了模型评分。这些评分可以很容易地应用于临床实践，可能会改善pAF的早期检测。总共从1000例窦性心律（SR；n = 728）、pAF（n = 161）和慢性房颤（cAF；n = 111）患者中收集了47项超声心动图和其他临床参数。我们开发了用于区分pAF和SR的逻辑模型，并将其简化为最具预测性的参数。为便于临床应用，得出了线性评分。为了研究向cAF的病理生理进展，我们类似地开发了cAF预测模型。对于pAF和SR之间的分类，在12个选定的模型参数中，最具预测性的变量是心房收缩期组织多普勒成像速度（TDI，A'）、左心房直径、年龄和主动脉根部直径。区分pAF和SR或区分cAF和SR的模型显示ROC曲线下面积分别为0.80或0.93，这类似于具有高鉴别力的分类器。基于常规心脏评估中容易获得的变量，新的风险评分适合预测pAF的存在。建模有助于定量表征从SR经pAF到cAF的病理生理转变。应用这些评分可能会改善pAF的早期检测，并可作为启动预防性干预措施以减少房颤相关并发症的决策辅助工具。
