Background

Patients are referred to functional coronary artery disease (CAD) testing based on their pre-test probability (PTP) to search for myocardial ischemia. The recommended prediction tools incorporate three variables (symptoms, age, sex) and are easy to use, but have a limited diagnostic accuracy. Hence, a substantial proportion of non-invasive functional tests reveal no myocardial ischemia, leading to unnecessary radiation exposure and costs. Therefore, preselection of patients before ischemia testing needs to be improved using a more predictive and personalised approach.

Aims

Using multiple variables (symptoms, vitals, ECG, biomarkers), artificial intelligence–based tools can provide a detailed and individualised profile of each patient. This could improve PTP assessment and provide a more personalised diagnostic approach in the framework of predictive, preventive and personalised medicine (PPPM).

Methods

Consecutive patients (n = 2417) referred for Rubidium-82 positron emission tomography were evaluated. PTP was calculated using the ESC 2013/2019 and ACC 2012/2021 guidelines, and a memetic pattern–based algorithm (MPA) was applied incorporating symptoms, vitals, ECG and biomarkers. Five PTP categories from very low to very high PTP were defined (i.e., < 5%, 5–15%, 15–50%, 50–85%, > 85%). Ischemia was defined as summed difference score (SDS) ≥ 2.

Results

Ischemia was present in 37.1%. The MPA model was most accurate to predict ischemia (AUC: 0.758, p < 0.001 compared to ESC 2013, 0.661; ESC 2019, 0.673; ACC 2012, 0.585; ACC 2021, 0.667). Using the < 5% threshold, the MPA’s sensitivity and negative predictive value to rule out ischemia were 99.1% and 96.4%, respectively. The model allocated patients more evenly across PTP categories, reduced the proportion of patients in the intermediate (15–85%) range by 29% (ACC 2012)–51% (ESC 2019), and was the only tool to correctly predict ischemia prevalence in the very low PTP category.

Conclusion

The MPA model enhanced ischemia testing according to the PPPM framework:

1. 1)

   The MPA model improved individual prediction of ischemia significantly and could safely exclude ischemia based on readily available variables without advanced testing (“predictive”).

2. 2)

   It reduced the proportion of patients in the intermediate PTP range. Therefore, it could be used as a gatekeeper to prevent patients from further unnecessary downstream testing, radiation exposure and costs (“preventive”).

3. 3)

   Consequently, the MPA model could transform ischemia testing towards a more personalised diagnostic algorithm (“personalised”).

中文翻译：

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人工智能改善铷正电子发射断层扫描中的缺血预测——一项验证研究

背景

根据患者的预测试概率 (PTP)，将患者转介至功能性冠状动脉疾病 (CAD) 测试，以寻找心肌缺血。推荐的预测工具包含三个变量（症状、年龄、性别），易于使用，但诊断准确性有限。因此，相当一部分非侵入性功能测试未发现心肌缺血，从而导致不必要的辐射暴露和费用。因此，需要使用更具预测性和个性化的方法来改进缺血测试前的患者预选。

目标

使用多个变量（症状、生命体征、心电图、生物标志物），基于人工智能的工具可以提供每位患者的详细且个性化的概况。这可以改善 PTP 评估，并在预测、预防和个性化医学 (PPPM) 框架内提供更加个性化的诊断方法。

方法

 对转诊接受铷-82 正电子发射断层扫描的连续患者 ( n = 2417) 进行了评估。PTP 使用 ESC 2013/2019 和 ACC 2012/2021 指南计算，并应用基于模因模式的算法 (MPA)，结合症状、生命体征、心电图和生物标志物。定义了从极低到极高 PTP 的五个 PTP 类别（即 < 5%、5–15%、15–50%、50–85%、> 85%）。缺血定义为总差评分 (SDS) ≥ 2。

结果

37.1% 存在缺血。MPA 模型预测缺血最准确（AUC：0.758， 与 ESC 2013，0.661 相比，p < 0.001；ESC 2019，0.673；ACC 2012，0.585；ACC 2021，0.667）。使用 < 5% 阈值，MPA 排除缺血的敏感性和阴性预测值分别为 99.1% 和 96.4%。该模型在 PTP 类别中更均匀地分配患者，将中间 (15-85%) 范围内的患者比例减少 29% (ACC 2012)–51% (ESC 2019)，并且是正确预测缺血发生率的唯一工具属于非常低的 PTP 类别。

结论

MPA模型根据PPPM框架增强了缺血测试：

1. 1）

   MPA 模型显着改善了个体对缺血的预测，并且可以根据现成的变量安全地排除缺血，而无需进行高级测试（“预测”）。

2. 2）

   它减少了处于中间 PTP 范围的患者比例。因此，它可以用作看门人，防止患者进行进一步不必要的下游测试、辐射暴露和费用（“预防性”）。

3. 3）

   因此，MPA 模型可以将缺血测试转变为更加个性化的诊断算法（“个性化”）。