{
  "abstract": "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.",
  "authors": [
    {
      "affiliations": [
        "Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada",
        "Centre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada",
        "Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada",
        "Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada"
      ],
      "name": "Alastair Fung"
    },
    {
      "affiliations": [
        "Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada"
      ],
      "name": "Marimuthu Sappani"
    },
    {
      "affiliations": [
        "Centre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada"
      ],
      "name": "Cole Heasley"
    },
    {
      "affiliations": [
        "Centre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada"
      ],
      "name": "Chun-Yuan Chen"
    },
    {
      "affiliations": [
        "Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada",
        "Centre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada",
        "Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada",
        "Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada"
      ],
      "name": "Shaun K Morris"
    },
    {
      "affiliations": [
        "Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada",
        "Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada",
        "Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada"
      ],
      "name": "Peter J Gill"
    },
    {
      "affiliations": [
        "Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada"
      ],
      "name": "Diego G Bassani"
    },
    {
      "affiliations": [
        "Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA",
        "Section of Infectious Diseases, Boston University Chobanian and Avedisian School of Medicine, Boston, Massachusetts, USA"
      ],
      "name": "Davidson H Hamer"
    },
    {
      "affiliations": [
        "Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada",
        "Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada",
        "Department of Pediatrics, Mount Sinai Hospital, Toronto, Ontario, Canada"
      ],
      "name": "Prakesh S Shah"
    },
    {
      "affiliations": [
        "International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh"
      ],
      "name": "S M Abdul Gaffar"
    },
    {
      "affiliations": [
        "International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh"
      ],
      "name": "Sultana Yeasmin"
    },
    {
      "affiliations": [
        "International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh"
      ],
      "name": "Shafiqul A Sarker"
    },
    {
      "affiliations": [
        "International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh"
      ],
      "name": "Shamima Sultana"
    },
    {
      "affiliations": [
        "Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada",
        "Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada"
      ],
      "name": "Joseph Beyene"
    },
    {
      "affiliations": [
        "Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada",
        "Centre for Global Child Health, The Hospital for Sick Children, Toronto, Ontario, Canada",
        "Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada",
        "Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada"
      ],
      "name": "Daniel E Roth"
    }
  ],
  "title": "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",
  "uid": "24e02b85-461a-5b2b-a5a5-8185a0311141"
}