ArticleBMJ paediatrics open2026
Development of a prediction model for infant hospitalisation and death using clinical features assessed by community health workers during routine postnatal home visits in Dhaka, Bangladesh.
Article in BMJ paediatrics open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo improve upon the WHO 8 danger signs used to identify young infants (<2 months) requiring referral during community health worker (CHW) home visits, aggregative features (eg, cumulative visits with fever) rather than visit-specific features (eg, 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 hospitalisation and/or death using CHW-assessed clinical features during home visits in Dhaka, Bangladesh.
methodsWe analysed data from generally healthy infants prospectively enrolled at birth and assessed at 11 scheduled CHW visits from 3 to 60 days of age. To predict first hospitalisation or death, we developed two models-time-varying Cox regression and random forest-using the same candidate predictors (45 clinical features of which eight were WHO danger signs and 12 additional covariates) with aggregative features incorporated. We evaluated discrimination (C-statistic) and calibration (calibration plots). Performance was compared with a time-varying Cox model using only WHO danger signs.
resultsAmong 1906 infants, 176 (9.2%) had an event (173 hospitalisations, three deaths). The best-performing Cox model (C-statistic=0.71; 95% CI 0.68 to 0.75) consisting of three baseline covariates (any perinatal/delivery complication, umbilical cord care and gestational age) and four visit-specific clinical features (nasal congestion, cough, jaundice and skin rash) and a Cox model with these four features plus WHO danger signs (C-statistic=0.70; 95% CI 0.67 to 0.74) demonstrated higher discrimination than WHO danger signs alone (C-statistic=0.56; 95% CI 0.54 to 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 to 0.73).
conclusionsAggregative features and random forest did not outperform a time-varying Cox model using baseline covariates and visit-specific features. Among lower-risk infants, adding four features to WHO danger signs may improve predictive performance by capturing a broader spectrum of illnesses requiring hospitalisation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.