{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T19:10:15Z","timestamp":1780686615023,"version":"3.54.1"},"reference-count":40,"publisher":"Walter de Gruyter GmbH","issue":"7","funder":[{"name":"Hubei Provincial Leading Public Health Talents Project","award":["WSJKRC2025007"],"award-info":[{"award-number":["WSJKRC2025007"]}]},{"name":"the Key R & D Projects of Hubei Province","award":["2022BCA019"],"award-info":[{"award-number":["2022BCA019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_cclm-2026-0088_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Antiphospholipid antibodies (aPLs) are closely associated with recurrent spontaneous abortion (RSA) and adverse pregnancy outcomes (POs). Non-criteria aPLs may aid in diagnosing seronegative aPL-related RSA, but their limited performance often leads to missed or delayed diagnosis. This study aimed to improve diagnostic and predictive accuracy by integrating criteria and non-criteria aPLs with machine learning (ML) algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-0088_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this multicenter prospective study, 1,321 participants were recruited and 751 included. Fifteen aPLs were measured using chemiluminescence immunoassay. Six ML algorithms were trained, and the optimal model was evaluated using the area under the curve (AUC), calibration curves, decision curve analysis (DCA), and Shapley additive explanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-0088_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using the 95th percentile as the cut-off, aPL positivity ranged from 2.62 to 12.13\u202f% in pregnant woman with a history of RSA and 4.29\u201319.74\u202f% in non-pregnant woman with a history of RSA. The light gradient boosting machine (LGBM) model achieved AUCs of 0.875 (pregnant) and 0.778 (non-pregnant) for RSA prediction, while the random forest (RF) model achieved an AUC of 0.885 for PO prediction\u00a0\u2013\u00a0surpassing all single indicators (AUC 0.516\u20130.647). Calibration, DCA, and SHAP analyses demonstrated strong clinical utility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_cclm-2026-0088_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The ML models substantially improved diagnostic and predictive performance for RSA and PO. The LGBM and RF models showed the best accuracy and may serve as auxiliary diagnostic and early warning tools. Larger external cohorts are needed for further validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/cclm-2026-0088","type":"journal-article","created":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T20:55:51Z","timestamp":1772398551000},"page":"1624-1635","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based integration of antiphospholipid antibodies for predicting pregnancy outcomes in patients with recurrent spontaneous abortion: a multicenter prospective study"],"prefix":"10.1515","volume":"64","author":[{"given":"Xinli","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine , 89674 Zhongnan Hospital of Wuhan University , Wuhan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiatong","family":"Chai","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , 89674 Zhongnan Hospital of Wuhan University , Wuhan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingrong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , 89674 Zhongnan Hospital of Wuhan University , Wuhan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , 89674 Zhongnan Hospital of Wuhan University , Wuhan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyu","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , 89674 Zhongnan Hospital of Wuhan University , Wuhan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fumei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , 89674 Zhongnan Hospital of Wuhan University , Wuhan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yirong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine , 89674 Zhongnan Hospital of Wuhan University , Wuhan , China"},{"name":"Hubei Engineering Center for Infectious Disease Prevention, Control and Treatment , Wuhan , China"},{"name":"Wuhan Research Center for Infectious Diseases and Cancer of the Chinese Academy of Medical Sciences , Wuhan , China"},{"name":"Hubei Provincial Clinical Medical Research Center for Molecular Diagnostics , Wuhan , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"2026060518413045480_j_cclm-2026-0088_ref_001","doi-asserted-by":"crossref","unstructured":"Devreese, KMJ. 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