{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:17:09Z","timestamp":1792084629650,"version":"4.4.0"},"reference-count":51,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Pulmonology"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to identify independent risk factors for bronchopulmonary dysplasia (BPD) at multiple postnatal time points in extremely preterm (EP) or very low birth weight (VLBW) infants and to develop machine learning\u2010based dynamic prediction models for early risk stratification and intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study utilized retrospective data from EP or VLBW infants (gestational age (GA)\u2009&lt;\u200932 weeks or birth weight (BW)\u2009&lt;\u20091500\u2009g) admitted to the First Affiliated Hospital of Xinjiang between 2017 and 2022. The dataset was randomly divided into training (70%) and validation (30%) cohorts. Prospective data from six Xinjiang neonatal centers (January\u2013October 2023) were collected for external validation. Infants were classified into three groups: no BPD, mild BPD, and moderate\u2010to\u2010severe BPD. Four machine learning algorithms\u2014logistic regression (LR), random forest, XGBoost (XGB), and gradient boosting decision tree\u2014were trained using clinical data from postnatal days 1, 3, and 7. The most predictive models were selected for external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The retrospective cohort included 554 infants (no BPD: 286; mild: 212; msBPD: 56), and the prospective cohort comprised 387 infants (no BPD: 208; mild: 138; msBPD: 41). Ordinal logistic regression identified significant independent risk factors for BPD severity, including GA, BW, prenatal steroids, umbilical flow interruption, severe Pre\u2010eclampsia, FIO\n                      <jats:sub>2<\/jats:sub>\n                      , C\u2010reactive protein, red blood cell count, systemic inflammatory response index, prognostic nutritional index, platelet mass index, alveolar\u2010arterial oxygen difference, and oxygenation index. The LR and XGB models demonstrated the highest predictive performance for BPD stratification on days 1, 3, and 7 (Area under the curve: day 1\u2009=\u20090.810, day 3\u2009=\u20090.837, day 7\u2009=\u20090.813).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine learning\u2013based dynamic prediction models for BPD were successfully developed and validated using data from postnatal days 1, 3, and 7. These models facilitate early identification of EP\/VLBW infants at high\u2010risk of BPD, supporting timely and targeted interventions to improve neonatal outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ppul.71322","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T19:24:43Z","timestamp":1760556283000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dynamic Risk\u2010Stratification Models for Bronchopulmonary Dysplasia in Extremely Preterm Very Low Birth Weight Infants"],"prefix":"10.1002","volume":"60","author":[{"given":"Ting","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Neonatology The First Affiliated Hospital of Xinjiang Medical University Urumqi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"An","sequence":"additional","affiliation":[{"name":"College of Pediatrics Xinjiang Medical University Urumqi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanping","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Neonatology The First Affiliated Hospital of Xinjiang Medical University Urumqi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwen","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Neonatology The First People\u2032s Hospital of Kashi prefecture Xinjiang Uygur Autonomous Region Kashi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Neonatology Xinjiang Yili Friendship Hospital Yili China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Neonatology Karamay Central Hospital of Xinjiang Karamay 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