P-2004. Risk Factors for Death in Children with Multisystem Inflammatory Syndrome in Children — United States, 2020-2022
Bibliographic record
Abstract
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 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".