Artificial Inteligence-Based Decision for the Prediction of Cardioembolism in Patients with Chagas Disease and Ischemic Stroke
Vinícius Viana Abreu Montanaro1, Thiago Falcão Hora2, Agostinho Alencar Guerra2
1SARAH Network of Rehabilitation Hospitals, Brasília, Brazil; Programa de Pós Graduação em Medicina (Neurologia) da Universidade Federal Fluminense - UFF, Niterói, RJ, Brazil.
Insights
Chagas disease (CD) and ischemic stroke (IS) share a link, with younger patients exhibiting cardioembolic events and higher risk factors. A new predictive model aids in identifying patients for optimized secondary prophylaxis.
Area of Science:
- Cardiology
- Neurology
- Infectious Diseases
Background:
- The association between Chagas disease (CD) and ischemic stroke (IS) is not fully understood.
- Limited evidence exists for secondary prophylaxis and etiological determination in CD-IS patients, with few identified cardioembolic cases.
Purpose of the Study:
- To describe a multicenter cohort of patients with both CD and IS.
- To develop a predictive model for cardioembolic embolism in patients with CD and IS.
Main Methods:
- Retrospective analysis of electronic and regular medical records from Brazilian hospitals.
- Descriptive analyses comparing cardioembolic and non-cardioembolic CD-IS patients.
- Development and validation of a predictive model for cardioembolism using 70% derivation and 30% validation samples.
Main Results:
- 499 patients were analyzed; cardioembolic patients were younger with higher rates of alcoholism, smoking, and mortality.
- The predictive model correlated with echocardiographic/electrocardiographic abnormalities and vascular risk factors.
Conclusions:
- Vascular risk factors are more prevalent and patients are younger in the cardioembolic CD-IS group.
- The developed predictive model can identify patients at higher risk for recurrence, enabling optimized secondary prophylaxis strategies.
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.
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