Predicting in-hospital mortality in children in low- and middle-income countries: A systematic review and meta-analysis of vital signs and anthropometric measurements
Bibliographic record
Abstract
BACKGROUND: In low- and middle-income countries (LMICs), child mortality rates remain substantially higher compared to high-income countries, with many deaths preventable through early recognition of deterioration. This systematic review and meta-analysis investigated predictive values of vital signs and anthropometric measurements for paediatric in-hospital mortality in LMICs. METHODS: A search of publicly available data in PubMed and OVID Embase was conducted in November 2021 and last updated in March 2025. Studies that reported on oxygen saturation; respiratory rate; heart rate; blood pressure; temperature; mid-upper arm circumference (MUAC); and/or weight-for-height z-score (WHZ), and paediatric in-hospital mortality were included. Neonatal and paediatric intensive care unit (PICU) studies were excluded. Data was extracted by two independent authors. Forest plots presented odds ratios (OR) using random effect models. Newcastle Ottawa Scale assessed risk of bias. FINDINGS: 104 out of 21,494 yielded studies were included in descriptive analysis and 75 in meta-analysis, encompassing 255,546 children. Associations with in-hospital mortality were observed in hypoxaemia (OR 5.53, 95% CI 4.18-7.30), tachypnoea (OR 1.65, 95% CI 1.16-2.34), tachycardia (OR 1.80, 95% CI 1.22-2.66), bradycardia (OR 3.29, 95% CI 1.38-7.83), hypotension (OR 4.42, 95% CI 2.54-7.70), hyperthermia (OR 1.31, 95% CI 1.04-1.66), hypothermia (OR 3.92, 95% CI 2.76-5.58), low MUAC (OR 3.22, 95% CI 2.12-4.91), and low WHZ (OR 3.19, 95% CI 2.47-4.11). INTERPRETATION: Several vital signs and anthropometric measurements are strongly associated with in-hospital mortality in children. Hypoxaemia demonstrated the highest odds of mortality, followed by hypotension, hypothermia, bradycardia and severe malnutrition. These findings highlight the need for early recognition and targeted interventions for children presenting with these high-risk signs, to improve outcomes in resource-limited settings and stress the need to monitor vital signs. FUNDING: None.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".