{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T14:12:01Z","timestamp":1790604721190,"version":"4.1.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pediatr Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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\u2009h improves early prediction of this outcome using explainable machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Retrospective cohort of 192 infants &lt;30 weeks\u2019 gestation (2019\u20132024). 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Forty-five infants (23.4%) reached the composite endpoint. All models achieved comparable discrimination (AUROC 0.831\u20130.847). 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 \u226520%. Chest X-ray density, gestational age, and maximum FiO\u2082 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 FiO\u2082 risk step at 30\u201335%, and a U-shaped birthweight\n                      <jats:italic>z<\/jats:italic>\n                      -score effect.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Combining early radiographic and high-frequency respiratory data with explainable machine learning enables well-calibrated BPD risk stratification on day 0\u20131. SHAP decomposition supports personalized care by distinguishing modifiable from non-modifiable risks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Impact<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>A machine learning model integrating respiratory monitoring, radiography, and perinatal variables from the first 24\u2009h 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).<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>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.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Providing net clinical benefit across a 10\u201330% threshold, it enables individualized, actionable risk stratification at the bedside, while clinical trajectories can still be modified.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s41390-026-05503-5","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T13:51:15Z","timestamp":1790603475000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early prediction of moderate-to-severe BPD or death in preterm infants using minute-level respiratory monitoring and explainable machine learning"],"prefix":"10.1038","author":[{"given":"Theolyne","family":"Louifi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boris","family":"Delange","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mona","family":"Leroux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Pladys","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alain","family":"Beuch\u00e9e","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"5503_CR1","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1053\/j.semperi.2006.05.004","volume":"30","author":"JJ Coalson","year":"2006","unstructured":"Coalson, J. 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