Thrombosis and hemorrhage experienced by hospitalized children with SARS‐CoV‐2 infection or MIS‐C: Results of the PICNIC registry
Bibliographic record
Abstract
INTRODUCTION: Coagulopathy and thrombosis associated with SARS-CoV-2 infection are well defined in hospitalized adults and leads to adverse outcomes. Pediatric studies are limited. METHODS: An international multicentered (n = 15) retrospective registry collected information on the clinical manifestations of SARS-CoV-2 and multisystem inflammatory syndrome (MIS-C) in hospitalized children from February 1, 2020 through May 31, 2021. This sub-study focused on coagulopathy. Study variables included patient demographics, comorbidities, clinical presentation, hospital course, laboratory parameters, management, and outcomes. RESULTS: Nine hundred eighty-five children were enrolled, of which 915 (93%) had clinical information available; 385 (42%) had symptomatic SARS-CoV-2 infection, 288 had MIS-C (31.4%), and 242 (26.4%) had SARS-CoV-2 identified incidentally. Ten children (1%) experienced thrombosis, 16 (1.7%) experienced hemorrhage, and two (0.2%) experienced both thrombosis and hemorrhage. Significantly prevalent prothrombotic comorbidities included congenital heart disease (p-value .007), respiratory support (p-value .006), central venous catheter (CVC) (p = .04) in children with primary SARS-CoV-2 and in those with MIS-C included respiratory support (p-value .03), obesity (p-value .002), and cytokine storm (p = .012). Comorbidities prevalent in children with hemorrhage included age >10 years (p = .04), CVC (p = .03) in children with primary SARS-CoV-2 infection and in those with MIS-C encompassed thrombocytopenia (p = .001) and cytokine storm (p = .02). Eleven patients died (1.2%), with no deaths attributed to thrombosis or hemorrhage. CONCLUSION: Thrombosis and hemorrhage are uncommon events in children with SARS-CoV-2; largely experienced by those with pre-existing comorbidities. Understanding the complete spectrum of coagulopathy in children with SARS-CoV-2 infection requires ongoing research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".