Clinical and laboratory predictors of 30‐day mortality in severe acute malnourished children with severe pneumonia
Bibliographic record
Abstract
Abstract Objective To determine the predictors of mortality within 30 days of hospital admission in a diarrhoeal disease hospital in Bangladesh. Methods Cohort study of hospitalised children aged 0–59 months with severe acute malnutrition (SAM) and severe pneumonia in Dhaka Hospital, icddr,b, Bangladesh from April 2015 to March 2017. Those discharged were followed up, and survival status at 30 days from admission was determined. Children who died were compared with the survivors in terms of clinical and laboratory biomarkers. Multivariable logistic regression analysis was used for calculating adjusted odds ratio for death within 30 days of hospital admission. Results We enrolled 191 children. Mortality within 30 days of admission was 6% (14/191). After adjusting for potential confounders (hypoxia, CRP and haematocrit) in logistic regression analysis, independent factors associated with death were female sex (aOR = 5.80, 95% CI: 1.34–25.19), LAZ <−4 (aOR = 6.51, 95% CI: 1.49–28.44) and Polymorphonuclear Leucocytes (PMNL) (>6.0 × 109/L) (aOR = 1.06, 95% CI: 1.01–1.11). Using sex, Z‐score for length for age (LAZ), and PMNL percentage, we used random forest and linear regression models to achieve a cross‐validated AUC of 0.83 (95% CI: 0.82, 0.84) for prediction of 30‐day mortality. Conclusions The results of our data suggest that female sex, severe malnutrition (<−4 LAZ) and higher PMNL percentage were prone to be associated with 30‐day mortality in children with severe pneumonia. Association of these factors may be used in clinical decision support for prompt identification and appropriate management for prevention of mortality in this population.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".