Impact of SARS-CoV-2 Infection on the Association Between Laboratory Tests and Severe Outcomes Among Hospitalized Children
Bibliographic record
Abstract
Abstract Background To assist clinicians with identifying children at risk of severe outcomes, we assessed the association between laboratory findings and severe outcomes among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)–infected children and determined if SARS-CoV-2 test result status modified the associations. Methods We conducted a cross-sectional analysis of participants tested for SARS-CoV-2 infection in 41 pediatric emergency departments in 10 countries. Participants were hospitalized, had laboratory testing performed, and completed 14-day follow-up. The primary objective was to assess the associations between laboratory findings and severe outcomes. The secondary objective was to determine if the SARS-CoV-2 test result modified the associations. Results We included 1817 participants; 522 (28.7%) SARS-CoV-2 test-positive and 1295 (71.3%) test-negative. Seventy-five (14.4%) test-positive and 174 (13.4%) test-negative children experienced severe outcomes. In regression analysis, we found that among SARS-CoV-2-positive children, procalcitonin ≥0.5 ng/mL (adjusted odds ratio [aOR], 9.14; 95% CI, 2.90–28.80), ferritin >500 ng/mL (aOR, 7.95; 95% CI, 1.89–33.44), D-dimer ≥1500 ng/mL (aOR, 4.57; 95% CI, 1.12–18.68), serum glucose ≥120 mg/dL (aOR, 2.01; 95% CI, 1.06–3.81), lymphocyte count <1.0 × 109/L (aOR, 3.21; 95% CI, 1.34–7.69), and platelet count <150 × 109/L (aOR, 2.82; 95% CI, 1.31–6.07) were associated with severe outcomes. Evaluation of the interaction term revealed that a positive SARS-CoV-2 result increased the associations with severe outcomes for elevated procalcitonin, C-reactive protein (CRP), D-dimer, and for reduced lymphocyte and platelet counts. Conclusions Specific laboratory parameters are associated with severe outcomes in SARS-CoV-2-infected children, and elevated serum procalcitonin, CRP, and D-dimer and low absolute lymphocyte and platelet counts were more strongly associated with severe outcomes in children testing positive compared with those testing negative.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".