Vinícius Viana Abreu Montanaro1
1 SARAH network of Rehabilitation hospitals, Brasilia Brazil
Chagas disease results from infection of the Trypanosoma cruzi parasite, most commonly transmitted through the insect Triatoma infestans.1 Whilst Chagas disease can be treated in the acute stages after infection, untreated Chagas disease can result in cardiomyopathy and gastrointestinal problems.2 Such cardiomyopathies have further been linked to an increased risk of stroke in Chagas disease.1,3 The World Health Organisation estimates that 6 to 7 million people are infected worldwide4, with most cases in South America.2
Dr. Montanaro, of the SARAH network of Rehabilitation hospitals, Brasilia Brazil and invited researcher at Radboud University Nijmegen, Netherlands set out to investigate what factors are associated with ischaemic stroke in Chagas disease with along with colleagues. “Although studies have assessed associations between Chagas and ischaemic stroke, most studies have had small numbers.” said Dr. Montanaro “We wanted to assess ischaemic stroke risk whilst taking into account cardiac involvement.”
Firstly, the group set out to predict factors associated with stroke recurrence and mortality (Table 1).5
Based on the findings from this study, the group then moved on to assess whether stroke topography could determine etiological stroke diagnosis in Chagas.6 “We carried out a study that assessed arterial territories in ischemic stroke and looked at these in Chagasic patients with cardioembolic compared to undetermined etiology” explained Dr. Montanaro “in this study we found that stroke topography was not useful in distinguishing etiologies in Chagas disease.”
From left to right: Paula Versiane, Maira Saul, Maria Cristina Del Negro, Eduardo Uchoa, Denise Freitas, Vinícius Viana Montanaro, Clarissa Menezes, Miguel Merino, Thiago Hora, Patrícia Marinho, Daniele Trizoto and Edna Ferreira
Advancing technologies such as machine learning models provide exciting new tools to understand more about ischaemic stroke within Chagas disease. “For our next project, we wanted to use machine learning to try and predict cardioembolic and non-cardioembolic etiologies of ischaemic stroke.” explained Dr Montanaro. Results showed that group was able to build a model able to distinguish cardioembolic and non-cardioembolic causes of ischaemic stroke with 65% sensitivity and 75% specificity.7
“We were pleased to see that our model was able to distinguish between etiologies” explained Dr Montanaro. “building upon our first study, we hope that being able to distinguish cardioembolic stroke will identify those at risk of stroke recurrence”
The group is keen to continue study of ischaemic stroke within Chagas disease. The next planned step forinthe research team consists in is to comparringe warfarin with new oral anticoagulants in cardioembolic and ESUS Chagas patients with ischemic stroke, whilst and evaluating the stroke recurrence and bleeding risks in these populations.
“Chagas disease and stroke is a field of study with a long road ahead. The methods of approaching problems of etiological classification and secondary prophylaxis are numerous and feasible- the main problem remains funding.” explains Dr. Montanaro “At the same time, the Brazilian scientific community has a responsibility to honor Carlos Chagas’s legacy and lead research studying the disease.”
F.J. Carod-Artal, J. Gascon J, Chagas disease and stroke, Lancet Neurol, (2010) 533–542, 9
Bern C. Chagas' Disease. N Engl J Med. 2015 Jul 30;373(5):456-66.
Lage TAR, Tupinambás JT, Pádua LB, et al. Stroke in Chagas disease: from pathophysiology to clinical practice. Rev Soc Bras Med Trop. 2022;55:e0575.
Montanaro VVA, Hora TF, Da Silva CM, et al. Mortality and stroke recurrence in a rehabilitation cohort of patients with cerebral infarcts and Chagas disease. Eur Neurol 2018;79:177-184.
Montanaro VVA, Hora TF, da Silva CM, de Viana Santos CV, Lima MIR, de Jesus Oliveira EM, de Freitas GR. Cerebral infarct topography of atrial fibrillation and Chagas disease. J Neurol Sci. 2019 May 15;400:10-14. doi: 10.1016/j.jns.2019.03.002. Epub 2019 Mar 12. PMID: 30878634.
Montanaro VVA, Hora TF, Guerra AA, Silva GS, Bezerra RP, Oliveira-Filho J, Santos LSB, de Melo ES, Alves de Andrade LP, Junior WAO, de Meira FCA, Nunes MDCP, Oliveira EMJ, de Freitas GR. Artificial Inteligence-Based Decision for the Prediction of Cardioembolism in Patients with Chagas Disease and Ischemic Stroke. J Stroke Cerebrovasc Dis. 2021 Oct;30(10):106034