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.

BACKGROUND: To 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. METHODS: We 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. RESULTS: Among 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). CONCLUSIONS: Aggregative 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.