{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T04:16:00Z","timestamp":1790136960162,"version":"4.0.1"},"reference-count":55,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100005088","name":"Beijing Municipal Health Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005088","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJOG"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To create and validate a machine learning (ML)\u2010based model for predicting the adverse perinatal outcome (APO) in foetal growth restriction (FGR) at diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Design<\/jats:title><jats:p>A retrospective study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Setting<\/jats:title><jats:p>Multi\u2010centre in China.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>Pregnancies affected by FGR.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We enrolled singleton foetuses with a perinatal diagnosis of FGR who were admitted between January 2021 and November 2023. A total of 361 pregnancies from Beijing Obstetrics and Gynecology Hospital were used as the training set and the internal test set. In comparison, data from 50 pregnancies from Haidian Maternal and Child Health Hospital were used as the external test set. Feature screening was performed using the random forest (RF), the Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression (LR). Subsequently, six ML methods, including Stacking, were used to construct models to predict the APO of FGR.<\/jats:p><\/jats:sec><jats:sec><jats:title>Main Outcome Measures<\/jats:title><jats:p>Model's performance was evaluated through indicators such as the area under the receiver operating characteristic curve (AUROC). The Shapley Additive Explanation analysis was used to rank each model feature and explain the final model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Mean\u2009\u00b1\u2009SD gestational age at diagnosis was 32.3\u2009\u00b1\u20094.8\u2009weeks in the absent APO group and 27.3\u2009\u00b1\u20093.7 in the present APO group. Women enrolled in the present APO group had a higher rate of hypertension related to pregnancy (74.8% vs. 18.8%, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). Among 17 candidate predictors (including maternal characteristics, maternal comorbidities, obstetric characteristics and ultrasound parameters), the integration of RF, LASSO and LR methodologies identified maternal body mass index, hypertension, gestational age at diagnosis of FGR, estimated foetal weight (EFW) <jats:italic>z<\/jats:italic> score, EFW growth velocity and abnormal umbilical artery Doppler (defined as a pulsatility index above the 95th percentile or instances of absent\/reversed diastolic flow) as significant predictors. The Stacking model demonstrated a good performance in both the internal test set [AUROC: 0.861, 95% confidence interval (CI), 0.838\u20130.896] and the external test set [AUROC: 0.906, 95% CI, 0.875\u20130.947]. The calibration curve showed high agreement between the predicted and observed risks. The Hosmer\u2013Lemeshow test for the internal and external test sets was <jats:italic>p<\/jats:italic>\u2009=\u20090.387 and <jats:italic>p<\/jats:italic>\u2009=\u20090.825, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The ML algorithm for APO, which integrates maternal clinical factors and ultrasound parameters, demonstrates good predictive value for APO in FGR at diagnosis. This suggested that ML techniques may be a valid approach for the early detection of high\u2010risk APO in FGR pregnancies.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1471-0528.18226","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T06:27:28Z","timestamp":1747981648000},"page":"1514-1523","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Construction of a Prediction Model for Adverse Perinatal Outcomes in Foetal Growth Restriction Based on a Machine Learning Algorithm: A Retrospective Study"],"prefix":"10.1111","volume":"132","author":[{"given":"Xiangli","family":"Meng","sequence":"first","affiliation":[{"name":"Beijing Obstetrics and Gynecology Hospital Capital Medical University  Beijing China"},{"name":"Beijing Maternal and Child Health Care Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Obstetrics and Gynecology Hospital Capital Medical University  Beijing China"},{"name":"Beijing Maternal and Child Health Care Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghui","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Obstetrics and Gynecology Hospital Capital Medical University  Beijing China"},{"name":"Beijing Maternal and Child Health Care Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Obstetrics and Gynecology Hospital Capital Medical University  Beijing China"},{"name":"Beijing Maternal and Child Health Care Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofei","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Obstetrics and Gynecology Hospital Capital Medical University  Beijing China"},{"name":"Beijing Maternal and Child Health Care Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6836-9192","authenticated-orcid":false,"given":"Qingqing","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Obstetrics and Gynecology Hospital Capital Medical University  Beijing China"},{"name":"Beijing Maternal and Child Health Care Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.22134"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(08)60074-4"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471\u20102393\u201014\u201063"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.3109\/14767058.2012.715006"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/00003081-200606000-00009"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.earlhumdev.2018.08.013"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa044160"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.26037"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/pd.5596"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.149"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.965"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.7347"},{"issue":"2","key":"e_1_2_12_14_1","first-page":"139","article-title":"Combined Sonographic Testing Index and Prediction of Adverse Outcome in Preterm Fetal Growth Restriction","volume":"31","author":"Odibo A. O.","year":"2014","journal-title":"American Journal of Perinatology"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1172\/JCI169199"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.22087"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.12.006"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2019.12.267"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2024.102913"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/1471-0528.16700"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.27710"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijgo.14721"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.26044"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.AOG.0000454932.15177.be"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.20272"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.23698"},{"issue":"6","key":"e_1_2_12_27_1","first-page":"361","article-title":"Expert Consensus on Fetal Growth Restriction","volume":"22","author":"Fetal Medicine Subgroup SoPM","year":"2019","journal-title":"Chinese Journal of Perinatal Medicine"},{"key":"e_1_2_12_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2015.08.032"},{"key":"e_1_2_12_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.20157"},{"key":"e_1_2_12_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2018.05.016"},{"key":"e_1_2_12_31_1","article-title":"Diagnosis and Management of Hypertension in Pregnancy: Summary of Updated NICE Guidance","volume":"366","author":"Webster K.","year":"2019","journal-title":"BMJ (Clinical Research Ed.)"},{"key":"e_1_2_12_32_1","doi-asserted-by":"publisher","DOI":"10.2337\/dc18-S002"},{"key":"e_1_2_12_33_1","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2011-2038"},{"key":"e_1_2_12_34_1","volume-title":"Haemoglobin Concentrations for the Diagnosis of Anaemia and Assessment of Severity","author":"World Health Organization","year":"2011"},{"key":"e_1_2_12_35_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1538-7836.2006.01753.x"},{"issue":"23","key":"e_1_2_12_36_1","first-page":"1805","article-title":"Moving Beyond Regression Techniques in Cardiovascular Risk Prediction: Applying Machine Learning to Address Analytic Challenges","volume":"38","author":"Goldstein B. A.","year":"2017","journal-title":"European Heart Journal"},{"key":"e_1_2_12_37_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_12_38_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2011.00771.x"},{"key":"e_1_2_12_39_1","doi-asserted-by":"publisher","DOI":"10.1198\/tech.2006.s357"},{"key":"e_1_2_12_40_1","doi-asserted-by":"publisher","DOI":"10.4258\/hir.2019.25.4.283"},{"key":"e_1_2_12_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10654-018-0390-z"},{"key":"e_1_2_12_42_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.CCM.0000275267.64078.B0"},{"key":"e_1_2_12_43_1","doi-asserted-by":"publisher","DOI":"10.2307\/2531595"},{"key":"e_1_2_12_44_1","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0b013e3181c30fb2"},{"key":"e_1_2_12_45_1","unstructured":"S. M.LundbergandS.\u2010I.Lee \u201cA Unified Approach to Interpreting Model Predictions \u201dinProceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017) Long Beach CA USA(2017) 4768\u201347777."},{"key":"e_1_2_12_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.03.022"},{"key":"e_1_2_12_47_1","doi-asserted-by":"publisher","DOI":"10.2196\/43815"},{"key":"e_1_2_12_48_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1403392"},{"key":"e_1_2_12_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107340"},{"key":"e_1_2_12_50_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-024-01994-6"},{"key":"e_1_2_12_51_1","doi-asserted-by":"publisher","DOI":"10.1159\/000357592"},{"key":"e_1_2_12_52_1","doi-asserted-by":"publisher","DOI":"10.3390\/nu14163276"},{"issue":"6","key":"e_1_2_12_53_1","article-title":"Fetal and Umbilical Doppler Ultrasound in High\u2010Risk Pregnancies","volume":"6","author":"Alfirevic Z.","year":"2017","journal-title":"Cochrane Database of Systematic Reviews"},{"key":"e_1_2_12_54_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.24977"},{"key":"e_1_2_12_55_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.24860"},{"key":"e_1_2_12_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2022.08.041"}],"container-title":["BJOG: An International Journal of Obstetrics &amp; Gynaecology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/obgyn.onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/1471-0528.18226","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T10:14:51Z","timestamp":1754043291000},"score":1,"resource":{"primary":{"URL":"https:\/\/obgyn.onlinelibrary.wiley.com\/doi\/10.1111\/1471-0528.18226"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,23]]},"references-count":55,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1111\/1471-0528.18226"],"URL":"https:\/\/doi.org\/10.1111\/1471-0528.18226","archive":["Portico"],"relation":{},"ISSN":["1470-0328","1471-0528"],"issn-type":[{"value":"1470-0328","type":"print"},{"value":"1471-0528","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,23]]},"assertion":[{"value":"2025-01-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-05","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}