Predictors of severe illness in children with multisystem inflammatory syndrome after SARS-CoV-2 infection: a multicentre cohort study
Bibliographic record
Abstract
BACKGROUND: SARS-CoV-2 infection can lead to multisystem inflammatory syndrome in children (MIS-C). We sought to investigate risk factors for admission to the intensive care unit (ICU) and explored changes in disease severity over time. METHODS: We obtained data from chart reviews of children younger than 18 years with confirmed or probable MIS-C who were admitted to 15 hospitals in Canada, Iran and Costa Rica between Mar. 1, 2020, and Mar. 7, 2021. Using multivariable analyses, we evaluated whether admission date and other characteristics were associated with ICU admission or cardiac involvement. RESULTS: Of 232 children with MIS-C (median age 5.8 yr), 130 (56.0%) were male and 50 (21.6%) had comorbidities. Seventy-three (31.5%) patients were admitted to the ICU but none died. We observed an increased risk of ICU admission among children aged 13-17 years (adjusted risk difference 27.7%, 95% confidence interval [CI] 8.3% to 47.2%), those aged 6-12 years (adjusted risk difference 25.2%, 95% CI 13.6% to 36.9%) or those with initial ferritin levels greater than 500 μg/L (adjusted risk difference 18.4%, 95% CI 5.6% to 31.3%). Children admitted to hospital after Oct. 31, 2020, had numerically higher rates of ICU admission (adjusted risk difference 12.3%, 95% CI -0.3% to 25.0%) and significantly higher rates of cardiac involvement (adjusted risk difference 30.9%, 95% CI 17.3% to 44.4%). At Canadian sites, the risk of ICU admission was significantly higher for children admitted to hospital between December 2020 and March 2021 than those admitted between March and May 2020 (adjusted risk difference 25.3%, 95% CI 6.5% to 44.0%). INTERPRETATION: We observed that age and higher ferritin levels were associated with more severe MIS-C. We observed greater severity of MIS-C later in the study period. Whether emerging SARS-CoV-2 variants pose different risks of severe MIS-C needs to be determined.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".