Development of a prediction model for infant hospitalization and death using clinical features assessed by community health workers during routine postnatal home visits in Dhaka, Bangladesh
Bibliographic record
Abstract
ABSTRACT Introduction To improve upon the World Health Organization (WHO) 8 danger signs used to identify young infants (<2 months) requiring referral during community health worker (CHW) home visits, aggregative features (e.g., cumulative visits with fever) rather than visit-specific features (e.g., fever at a single visit), and a machine learning random forest model, may enhance predictive performance. Applying these approaches, we aimed to develop a prediction model for infant hospitalization and/or death using CHW-assessed clinical features during home visits in Dhaka, Bangladesh. Methods We analyzed data from generally healthy infants prospectively enrolled at birth and assessed at 11 scheduled CHW visits from 3-60 days of age. To predict first hospitalization or death, we developed two models – time-varying Cox regression and random forest – using the same set of candidate predictors (45 clinical features of which 8 were WHO danger signs, and 12 additional covariates) with aggregative features incorporated. We evaluated discrimination (C-statistic) and calibration (calibration plots). Performance was compared to a time-varying Cox model using only WHO danger signs. Results Among 1906 infants, 176 (9.2%) had an event (173 hospitalizations, 3 deaths). The best-performing Cox model (C-statistic=0.71; 95% CI 0.68-0.75) consisting of three baseline covariates (any perinatal/delivery complication, umbilical cord care, gestational age) and four visit-specific clinical features (nasal congestion, cough, jaundice, skin rash), and a Cox model with these four features plus WHO danger signs (C-statistic=0.70; 95% CI 0.67-0.74), demonstrated higher discrimination than WHO danger signs alone (C-statistic=0.56; 95% CI 0.54-0.60), with similar calibration. A random forest model (42 predictors) was well-calibrated with comparable discrimination (C-statistic=0.69; 95% CI 0.64-0.73). Conclusion Aggregative features and random forest did not outperform a time-varying Cox model using baseline covariates and visit-specific features. Adding four features to WHO danger signs may improve predictive performance by capturing a broader spectrum of infant illnesses requiring hospitalization. What is already known on this topic During community health worker (CHW) home visit assessments of young infants (<2 months), use of World Health Organization (WHO)-recommended danger signs to predict hospitalization and/or death may have limited sensitivity and may miss cases of severe illness requiring referral. Summarizing repeated assessments of clinical features during sequential home visits as aggregative predictors (e.g., cumulative visits with fever) rather than visit-specific predictors (e.g., fever at a single visit), and machine learning models such as random forest, have not been previously evaluated for prediction of infant hospitalization and/or death and may improve predictive performance compared to WHO danger signs. What this study adds Random forest, and use of aggregative predictors in both a random forest model and a time-varying Cox model, did not improve prediction of infant hospitalization and/or death during CHW routine home visits compared to a time-varying Cox model consisting of baseline covariates and visit-specific clinical features. Adding four visit-specific clinical features to the WHO danger signs improved prediction of hospitalization and/or death during CHW routine home visit assessments of young infants born generally healthy in an urban setting. How this study might affect research, practice or policy The findings support future research evaluating whether adding visit-specific clinical features to the WHO danger signs algorithm can improve identification of infants needing referral across diverse settings and with varying baseline risks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".