{
  "abstract": "Background: Early identification of extremely preterm infants at risk of moderate-to-severe bronchopulmonary dysplasia (BPD) or death remains challenging. We assessed whether minute-by-minute respiratory data from the first 24 h improves early prediction of this outcome using explainable machine learning. Methods: Retrospective cohort of 192 infants <30 weeks' gestation (2019-2024). Logistic regression, random forest, and XGBoost models were trained on 17 predictors and summarized 24-h respiratory data. Evaluation used AUROC, AUPRC, Brier score, calibration, and decision curves, with SHAP for interpretability. Results: Forty-five infants (23.4%) reached the composite endpoint. All models achieved comparable discrimination (AUROC 0.831-0.847). Random forest was selected as the headline model based on superior calibration (slope 1.016; ECE 0.053), lowest Brier score (0.131), and highest net benefit at decision thresholds ≥20%. Chest X-ray density, gestational age, and maximum FiO₂ were the top SHAP predictors in the headline model; gestational age achieved the highest cross-model rank. SHAP identified a gestational age threshold at 28 weeks, an FiO₂ risk step at 30-35%, and a U-shaped birthweight z-score effect. Conclusion: Combining early radiographic and high-frequency respiratory data with explainable machine learning enables well-calibrated BPD risk stratification on day 0-1. SHAP decomposition supports personalized care by distinguishing modifiable from non-modifiable risks. Impact: A machine learning model integrating respiratory monitoring, radiography, and perinatal variables from the first 24 h of life predicts moderate-to-severe BPD or death in very preterm infants with an AUROC of 0.831 and well-calibrated risk estimates (calibration slope 1.016). Unlike existing tools relying on data from day 7 or later, this day-1 approach significantly improves early prognosis beyond gestational age and birthweight alone. Providing net clinical benefit across a 10-30% threshold, it enables individualized, actionable risk stratification at the bedside, while clinical trajectories can still be modified.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Théolyne Louifi"
    },
    {
      "name": "Boris Delange"
    },
    {
      "name": "Mona Leroux"
    },
    {
      "name": "Patrick Pladys"
    },
    {
      "name": "Alain Beuchée"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5vd7eyas4fmzb.bib",
  "cited_by": 0,
  "doi": "10.1038/s41390-026-05503-5",
  "fulltext_url": null,
  "id": "5vd7eyas4fmzb",
  "issns": [
    "0031-3998",
    "1530-0447"
  ],
  "landing_url": "https://hal.science/hal-05785922v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "1.sdv.ib"
    ],
    [
      "openalex",
      "2740|Neonatal Respiratory Health Research"
    ]
  ],
  "openalex": "W7214683235",
  "pdf_url": "https://hal.science/hal-05785922/document",
  "pmcid": null,
  "pmid": "42806140",
  "published": "2026-10-08",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/5vd7eyas4fmzb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:50Z",
      "native_id": "hal-05785922",
      "source": "hal",
      "url": "https://hal.science/hal-05785922v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.sdv.ib",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Early prediction of moderate-to-severe BPD or death in preterm infants using minute-level respiratory monitoring and explainable machine learning",
  "title_text": "Early prediction of moderate-to-severe BPD or death in preterm infants using minute-level respiratory monitoring and explainable machine learning",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:50Z",
  "url": "https://research.draconitic.com/paper/5vd7eyas4fmzb",
  "venue": "Pediatric Research",
  "versions": []
}