Pertussis infection in critically ill infants: meta-analysis and validation of a mortality score
Bibliographic record
Abstract
BACKGROUND: Despite widespread vaccination programs, pertussis continues circulating within populations and remains a life-threatening infection in infants. While several mortality risk factors have been described, a comprehensive synthesis is lacking. We conducted a meta-analysis of studies investigating mortality risk factors in Pertussis infections and validated those factors in a large cohort. METHODS: Observational studies published in English were systematically searched in PubMed, EMBASE, and LiSSa databases from 01/2000 to 06/2024. The search yielded 816 unique citations. The primary outcome was mortality before discharge from the Pediatric Intensive Care Unit (PICU). Two independent reviewers assessed the risk of bias and extracted data. A REML-random effect model was used to calculate pooled prevalence and conduct the analysis. The identified risk factors were subsequently evaluated in a monocentric cohort of patients admitted to a tertiary hospital's PICU for severe pertussis between January 1996 and December 2020. Data analysis was conducted between June and August 2024. RESULTS: = 96). Identified mortality risk factors included elevated heart rate, presence of pulmonary hypertension, presence of seizures, and elevated white blood cell (WBC) count. Validation in an 83-patient cohort (median age: 45 days, IQR: 30-55) revealed a mortality rate of 12%. Risk factors identified in the meta-analysis were significantly associated with non-survival in the cohort. A mortality prediction score was developed incorporating age < 30 days, heart rate > 200/min, and WBC > 30 G/l, achieving an area under the curve of 0.92 (95% CI: 0.86-0.99). CONCLUSION: This meta-analysis identified a simple yet effective score to assess the severity of pertussis infection in infants admitted to PICU. Accurate risk stratification may enable timely treatment of critically ill patients, potentially improving outcomes. TRIAL REGISTRATION: The study protocol was registered on PROSPERO: CRD42024582057.
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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 | 0.044 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.067 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".