Department of Cardiac Arrhythmia Research and Innovation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
JMIR Med Inform. 2024 Nov 22;12:e63795. doi: 10.2196/63795.
Atrial fibrillation (AF) is a progressive disease, and its clinical type is classified according to the AF duration: paroxysmal AF, persistent AF (PeAF; AF duration of less than 1 year), and long-standing persistent AF (AF duration of more than 1 year). When considering the indication for catheter ablation, having a long AF duration is considered a risk factor for recurrence, and therefore, the duration of AF is an important factor in determining the treatment strategy for PeAF.
This study aims to improve the accuracy of the cardiologists' diagnosis of the AF duration, and the steps to achieve this goal are to develop a predictive model of the AF duration and validate the support performance of the prediction model.
The study included 272 patients with PeAF (aged 20-90 years), with data obtained between January 1, 2015, and December 31, 2023. Of those, 189 (69.5%) were included in the study, excluding 83 (30.5%) who met the exclusion criteria. Of the 189 patients included, 145 (76.7%) were used as training data to build the machine learning (ML) model and 44 (23.3%) were used as test data for predictive ability of the ML model. Using a questionnaire, 10 cardiologists (group A) evaluated whether the test data (44 patients) included AF of more than a 1-year duration (phase 1). Next, the same questionnaire was performed again after providing the ML model's answer (phase 2). Subsequently, another 10 cardiologists (group B) were shown the test results of group A, were made aware of the limitations of their own diagnostic abilities, and were then administered the same 2-stage test as group A.
The prediction results with the ML model using the test data provided 81.8% accuracy (72% sensitivity and 89% specificity). The mean percentage of correct answers in group A was 63.9% (SD 9.6%) for phase 1 and improved to 71.6% (SD 9.3%) for phase 2 (P=.01). The mean percentage of correct answers in group B was 59.8% (SD 5.3%) for phase 1 and improved to 68.2% (SD 5.9%) for phase 2 (P=.007). The mean percentage of answers that differed from the ML model's prediction for phase 2 (percentage of answers where cardiologists did not trust the ML model and believed their own determination) was 17.3% (SD 10.3%) in group A and 20.9% (SD 5%) in group B and was not significantly different (P=.85).
ML models predicting AF duration improved the diagnostic ability of cardiologists. However, cardiologists did not entirely rely on the ML model's prediction, even if they were aware of their diagnostic capability limitations.
心房颤动（AF）是一种进行性疾病，其临床类型根据 AF 持续时间进行分类：阵发性 AF、持续性 AF（PeAF；AF 持续时间小于 1 年）和持久性持续性 AF（AF 持续时间超过 1 年）。在考虑导管消融的适应证时，较长的 AF 持续时间被认为是复发的危险因素，因此，AF 的持续时间是确定 PeAF 治疗策略的重要因素。
本研究旨在提高心脏病专家对 AF 持续时间诊断的准确性，实现这一目标的步骤是开发 AF 持续时间的预测模型，并验证预测模型的支持性能。
该研究纳入了 272 例 PeAF 患者（年龄 20-90 岁），数据采集时间为 2015 年 1 月 1 日至 2023 年 12 月 31 日。其中 189 例（69.5%）符合纳入标准，排除 83 例（30.5%）不符合纳入标准的患者。在纳入的 189 例患者中，145 例（76.7%）用于构建机器学习（ML）模型的训练数据，44 例（23.3%）用于测试 ML 模型的预测能力。10 名心脏病专家（A 组）使用问卷评估了测试数据（44 例患者）是否存在持续时间超过 1 年的 AF（第 1 阶段）。接下来，在提供 ML 模型的答案后，再次进行相同的问卷（第 2 阶段）。随后，另外 10 名心脏病专家（B 组）展示了 A 组的测试结果，让他们意识到自己诊断能力的局限性，然后对他们进行与 A 组相同的 2 阶段测试。
使用测试数据的 ML 模型的预测结果准确率为 81.8%（72%的敏感性和 89%的特异性）。A 组第 1 阶段的平均正确答案百分比为 63.9%（SD 9.6%），第 2 阶段提高至 71.6%（SD 9.3%）（P=.01）。B 组第 1 阶段的平均正确答案百分比为 59.8%（SD 5.3%），第 2 阶段提高至 68.2%（SD 5.9%）（P=.007）。第 2 阶段与 ML 模型预测结果不同的答案百分比（心脏病专家不相信 ML 模型并相信自己的判断的答案百分比）在 A 组中为 17.3%（SD 10.3%），在 B 组中为 20.9%（SD 5%），差异无统计学意义（P=.85）。
预测 AF 持续时间的 ML 模型提高了心脏病专家的诊断能力。然而，即使心脏病专家意识到自己的诊断能力有限，他们也不完全依赖 ML 模型的预测。
