{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T14:03:32Z","timestamp":1791813812089,"version":"4.3.4"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e4tsklinikum Hamburg-Eppendorf (UKE)"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Arch Gynecol Obstet"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Preeclampsia is a major obstetric disorder. Machine learning (ML)\u00a0models incorporating angiogenic biomarkers show promise in predicting related adverse outcomes, but refinement is needed for clinical use. This study aimed to reduce features to a clinically meaningful set and to develop and validate predictive endpoints for preeclampsia-associated outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A model with a reduced feature set was derived from a training cohort of 1,634 patients (2,<\/jats:p>\n                    <jats:p>412 visits) and then tested on a validation cohort of 402 patients (540 visits). Three machine learning models were developed to predict (1) adverse outcomes overall, (2) delivery within 14\u00a0days before 34\u00a0weeks of gestation, and (3) delivery within 7\u00a0days after 34\u00a0weeks, using 13 features versus 114 originally.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Reduced-feature models demonstrated comparable accuracy to original models across all endpoints. Model 1 (any adverse outcome) achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.92 (95% CI: 0.88\u20130.96) in training and 0.89 (95% CI: 0.84\u20130.93,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.31) in the\u00a0validation cohort, respectively. For delivery within 14\u00a0days, the\u00a0AUROC was 0.92 (95% CI: 0.87\u20130.96) in training and 0.85 (95% CI: 0.78\u20130.92) in validation (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.13). Delivery within 7\u00a0days showed AUROCs of 0.79 (95% CI: 0.70\u20130.87) and 0.80 (95% CI: 0.75\u20130.85) (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.78).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A machine learning model with a significantly reduced number of features can accurately predict clinically relevant preeclampsia outcomes. The identified endpoints (timing of delivery and adverse events) could support clinical decision-making and help reduce maternal and neonatal morbidity and mortality.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00404-025-08261-7","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:10:05Z","timestamp":1770394205000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Validation of a machine-learning-based algorithm to predict preeclampsia-related adverse outcomes on a real-world dataset"],"prefix":"10.1007","volume":"313","author":[{"given":"Ameli","family":"Hoyler","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Rieger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Max","family":"Hackel\u00f6er","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Neznansky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang","family":"Henrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa","family":"Lorenz-Meyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Verlohren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"issue":"Suppl 1","key":"8261_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/ijgo.12802","volume":"145","author":"LC Poon","year":"2019","unstructured":"Poon LC et al (2019) The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: a pragmatic guide for first-trimester screening and prevention. 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Stefan\u00a0Verlohren holds stock options on Neopredics. All other authors declare to not have a conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This retrospective chart review study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The Ethics Committee of Charit\u00e9 \u2013Universit\u00e4tsmedizin Berlin approved this study, approval number EA2\/064\/20.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This was a retrospective real-world study on patient data from patients hospitalized at Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin. 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