In Reply: High Fatality Rates in Pediatric Multisystem Inflammatory Syndrome: A Multicenter Experience From the Epicenter of Brazil’s Coronavirus Pandemic
Bibliographic record
Abstract
To the Editors: We read, with great interest, a recent article on the case fatality rate (CFR) of pediatric multisystem inflammatory syndrome in children (MIS-C) in Brazil.1 Considering this, we share our recent findings from an observational multicenter study conducted in Rio de Janeiro State, which challenges prevailing national trends. Brazil, with an estimated population of 215.3 million inhabitants and ranked as the sixth most populous country globally, exhibits stark socioeconomic inequalities and disparities in healthcare access, reflected in its Human Development Index of 0.760, which is in contrast to the Human Development Index of 0.926 in the United States.2 The US Centers for Disease Control and Prevention (CDC) reported 9698 MIS-C cases, with 79 deaths (0.81%) as of July 2, 2024. In Brazil, the Ministry of Health’s Epidemiological Bulletin documented 2142 MIS-C cases and 145 deaths (6.76%) by May 5, 2024, one of the highest CFRs for MIS-C globally, as the country is vast and diverse, with different healthcare access.3 The CFR was recently estimated at 3.9% in a multicenter study from São Paulo State. Our study, encompassing 112 MIS-C patients across 7 hospitals in Rio de Janeiro, revealed a CFR of only 0.9%. This contrast underscores the need for a nuanced understanding of MIS-C outcomes in Brazil. Our cohort of patients was diagnosed with MIS-C according to the World Health Organization/CDC guidelines,4,5 with a median age of 4.2 years. Most were White (43%), followed by Afro-descendant individuals (35%). Most patients (58.9%) were admitted to public university hospitals. Underlying medical conditions were present in 12.5% of patients, and the median hospital stay was 7 days (interquartile range: 8; 1–66). A classic Kawasaki disease (KD) phenotype was observed in 39% of patients. The MIS-C/incomplete KD phenotype was associated with a higher incidence of myocarditis (P = 0.009), elevated ferritin levels (P = 0.047), and the use of prophylactic anticoagulation (P = 0.014). No significant differences were observed between the phenotypes regarding severity, respiratory support, renal involvement, or intensive care unit admission need. The single death in our cohort occurred in an adolescent with an incomplete KD phenotype complicated by cytokine storm syndrome, highlighting the importance of early diagnosis and aggressive management of high-risk patients. Several factors may explain the lower CFR observed in this study. The high proportion of patients admitted to university hospitals (58.9%) likely facilitated earlier diagnosis and access to specialized multidisciplinary care. Additionally, heightened awareness and preparedness for MIS-C among healthcare providers during the study period (August 2020–March 2022) may have contributed. Circulation of different SARS-CoV-2 variants may also play a role. Our study period encompassed the circulation of B.1.1.28, P.2, Gamma, Delta, and Omicron variants, whereas the study from São Paulo State included only B.1.1.33, B.1.1.28, P.2, and Gamma variants.3 The lower risk of MIS-C associated with the Omicron variant and the potential impact of vaccination may have further contributed to the lower observed CFR. Our findings diverge from the national average and data from São Paulo, suggesting regional variations in MIS-C outcomes in Brazil. These variations may be attributable to differences in the healthcare infrastructure, diagnostic criteria, treatment protocols, circulating SARS-CoV-2 variants, or host genetic factors. Further research is required to elucidate the factors underlying these discrepancies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".