Background

Chagas disease (CD) and ischemic stroke (IS) have a close, but poorly understood, association. There is paucity of evidence on the ideal secondary prophylaxis and etiological determination, with few cardioembolic patients being identified.

Aims

This study aimed to describe a multicenter cohort of patients with concomitant CD and IS admitted in tertiary centers and to create a predictive model for cardioembolic embolism in CD and IS.

Materials and methods

We retrospectively studied data obtained from electronic medical and regular medical records of patients with CD and IS in several academic, hospital-based, and university hospitals across Brazil. Descriptive analyses of cardioembolic and non-cardioembolic patients were performed. A prediction model for cardioembolism was proposed with 70% of the sample as the derivation sample, and the model was validated in 30% of the sample.

Results

A total of 499 patients were analyzed. The median age was similar in both groups; however, patients with cardioembolic embolism were younger and tended to have higher alcoholism, smoking, and death rates. The predictive model for the etiological classification showed close relation with the number of abnormalities detected on echocardiography and electrocardiography as well as with vascular risk factors.

Conclusions

Our results replicate in part those previously published, with a higher prevalence of vascular risk factors and lower median age in patients with cardioembolic etiology. Our new model for predicting cardioembolic etiology can help identify patients with higher recurrence rate and therefore allow an optimized strategy for secondary prophylaxis.

中文翻译：

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基于人工智能的决策用于预测恰加斯病和缺血性中风患者的心脏栓塞

背景

恰加斯病 (CD) 和缺血性中风 (IS) 之间有着密切但知之甚少的关联。缺乏关于理想的二级预防和病因学确定的证据，很少发现心源性栓塞患者。

宗旨

本研究旨在描述在三级中心合并的 CD 和 IS 患者的多中心队列，并创建 CD 和 IS 中心源性栓塞的预测模型。

材料和方法

我们回顾性研究了从巴西几家学术医院、医院和大学医院的 CD 和 IS 患者的电子医疗记录和常规医疗记录中获得的数据。对心源性和非心源性患者进行了描述性分析。以70%的样本作为推导样本，提出了心源性栓塞预测模型，并在30%的样本中进行了验证。

结果

共分析了 499 名患者。两组的中位年龄相似；然而，心源性栓塞患者更年轻，酗酒、吸烟和死亡率往往更高。病因分类的预测模型显示与超声心动图和心电图检测到的异常数量以及血管危险因素密切相关。

结论

我们的结果部分复制了先前发表的结果，心血管危险因素的患病率较高，心源性栓塞患者的中位年龄较低。我们用于预测心源性栓塞病因的新模型可以帮助识别复发率较高的患者，从而优化二级预防策略。