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Record W4406948363 · doi:10.1093/ofid/ofae631.2161

P-2004. Risk Factors for Death in Children with Multisystem Inflammatory Syndrome in Children — United States, 2020-2022

2025· article· en· W4406948363 on OpenAlexaff
Anna R Yousaf, Regina M. Simeone, Katherine Lindsey, Ami B. Shah, Michael Wu, Rebecca J. Free, Laura D. Zambrano, Angela P. Campbell

Bibliographic record

VenueOpen Forum Infectious Diseases · 2025
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsGeneral Dynamics (Canada)
Fundersnot available
KeywordsMedicineSystemic inflammatory response syndromePediatricsIntensive care medicineInternal medicineSepsis

Abstract

fetched live from OpenAlex

Abstract Background Although multisystem inflammatory syndrome in children (MIS-C) incidence has decreased, cases continue to occur and can have notable morbidity and mortality. This investigation evaluates clinical and demographic risk factors for death from MIS-C.Table 1.Demographic and clinical risk factors for death in children with Multisystem Inflammatory Syndrome in Children, United States, 2020-2022 Methods We evaluated children reported to CDC national MIS-C surveillance with illness onset February 2020–December 2022 and known illness outcome. We performed multivariable logistic regression to estimate odds of death compared with survival for patient characteristics and MIS-C organ involvement. Model covariates were selected a priori including age at MIS-C onset, date of MIS-C onset, race and ethnicity, and presence of any underlying medical condition. To compare risk of death across the pandemic we evaluated patients with date of MIS-C onset in five pandemic waves corresponding to peaks of MIS-C activity.Table 2.Organ system involvement risk factors for death in children with Multisystem Inflammatory Syndrome in Children, United States, 2020-2022 Results Of 8,767 MIS-C cases reported, there were 76 (9%) deaths (Table 1). Compared with children aged 5–11 years, children < 1 year and 16–20 years had increased odds of death (aOR 4.8 [1.7–13.6], p< 0.01, and 8.2 [4.6–14.8], p< 0.001, respectively). American Indian/alaska Native (AIAN) and Native Hawaiian/Pacific Islander (NHPI) children had increased odds of death compared with White non-Hispanic children (aOR 5.4 [1.4–20.9, p=0.01 and 5.9 [1.5–23.1, p=0.01, respectively). Children with MIS-C illness onset after wave 1 had decreased odds of death compared with those during wave 1. Presence of >1 underlying medical condition was associated with higher odds of death (aOR 2.3 [1.5–3.7, p< 0.001); diabetes, congenital heart disease, neurologic, immunosuppressive/autoimmune, and noncardiac congenital disorders were all independently associated with death. Cardiovascular, respiratory, neurologic, and renal MIS-C organ involvement increased odds of death while children with mucocutaneous involvement had decreased odds (Table 2). Conclusion Younger (< 1 year) and older (16-20 years) age, AIAN and NHPI race and ethnicity, MIS-C early in the pandemic, and underlying medical conditions are risk factors for death from MIS-C. Cardiovascular, respiratory, neurologic, and renal involvement increased risk of death. Identifying risk factors associated with death may help clinicians with management and prognosis. Disclosures Regina Simeone, PhD, Pfizer: Stocks/Bonds (Private Company)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.011
GPT teacher head0.323
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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