Chagas disease and SARS-CoV-2 coinfection does not lead to worse in-hospital outcomes

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Abstract

Chagas disease (CD) continues to be a major public health burden in Latina America. Information on the interplay between COVID-19 and CD is lacking. Our aim was to assess clinical characteristics and in-hospital outcomes of patients with CD and COVID-19, and to compare it to non-CD patients. Consecutive patients with confirmed COVID-19 were included from March to September 2020. Genetic matching for sex, age, hypertension, diabetes mellitus and hospital was performed in a 4:1 ratio. Of the 7018 patients who had confirmed COVID-19, 31 patients with CD and 124 matched controls were included (median age 72 (64–80) years-old, 44.5% were male). At baseline, heart failure (25.8% vs. 9.7%) and atrial fibrillation (29.0% vs. 5.6%) were more frequent in CD patients than in the controls (p < 0.05). C-reactive protein levels were lower in CD patients compared with the controls (55.5 [35.7, 85.0] vs. 94.3 [50.7, 167.5] mg/dL). In-hospital management, outcomes and complications were similar between the groups. In this large Brazilian COVID-19 Registry, CD patients had a higher prevalence of atrial fibrillation and chronic heart failure compared with non-CD controls, with no differences in-hospital outcomes. The lower C-reactive protein levels in CD patients require further investigation.

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  1. SciScore for 10.1101/2021.03.22.21254078: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethics: The study was approved by the National Commission for Research Ethics (CAAE 30350820.5.1001.0008).
    Consent: Individual informed consent was waived owing to the pandemic situation and the use of deidentified data, based on medical chart review only.
    Randomizationnot detected.
    Blindingnot detected.
    Power AnalysisSample size of 132 controls was calculated considering and expected risk ratio for mortality 2.5 in CD-group, power of 80%, alfa-error probability of 5% for a 4:1 CD/control.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data collection: Study data were collected by trained hospital staff or interns using Research Electronic Data Capture (REDCap) tools. [16] Medical records were reviewed to collect data on patients’ demographic and clinical characteristics, including age, sex, pre-existing medical conditions and home medications; COVID-19 symptoms at hospital presentation; clinical assessment upon hospital admission, third and fifth admission days; laboratory, imaging, electrocardiographic data; inpatient medications, treatment and outcomes.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    In a previous multivariate analysis, we demonstrated that despite being admitted to public hospitals patients do not have worse prognosis than patients admitted to private ones.[5] This study has limitations. In addition to the retrospective design, subject to the drawbacks of a patient records review, the number of CD was low. However, it is the largest series published to date. Due to the pragmatic study design, laboratory and imaging tests were performed at the discretion of the treating physician. Despite the limited representativity of radiologic, tomographic and electrocardiographic analysis, no patient performed echocardiogram during hospital admission.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.