{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:43:34Z","timestamp":1787013814091,"version":"build-2736575974"},"reference-count":59,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T00:00:00Z","timestamp":1620604800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HI18C0378"],"award-info":[{"award-number":["HI18C0378"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1A2C3011850"],"award-info":[{"award-number":["2020R1A2C3011850"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["American J Rep Immunol"],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Problem<\/jats:title>\n                    <jats:p>Preterm birth (PTB) is a major cause of increased morbidity and mortality in newborns. The main cause of spontaneous PTB (sPTB) is the activation of an inflammatory response as a result of ascending genital tract infection. Despite various studies on the effects of the vaginal microbiome on PTB, a practical method for its clinical application has yet to be developed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method of study<\/jats:title>\n                    <jats:p>\n                      In this case\u2010control study, 94 Korean pregnant women with PTB (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a038) and term birth (TB;\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a056) were enrolled. Their cervicovaginal fluid (CVF) was sampled, and a total of 10 bacteria were analyzed using multiplex quantitative real\u2010time PCR (qPCR). The PTB and TB groups were compared, and a PTB prediction model was created using bacterial risk scores using machine learning techniques (decision tree and support vector machine). The predictive performance of the model was validated using random subsampling.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Bacterial risk scoring model showed significant differences (\n                      <jats:italic>P\u00a0<\/jats:italic>\n                      &lt;\u00a00.001). The PTB risk was low when the\n                      <jats:italic>Lactobacillus iners<\/jats:italic>\n                      ratio was 0.812 or more. In groups with a ratio under 0.812, moderate and high risk was classified as a\n                      <jats:italic>U<\/jats:italic>\n                      .\u00a0\n                      <jats:italic>parvum<\/jats:italic>\n                      ratio of 4.6 \u00d7 10\n                      <jats:sup>\u20133<\/jats:sup>\n                      . The sensitivity and specificity of the PTB prediction model using bacteria risk score were 71% and 59%, respectively, and 77% and 67%, respectively, when white blood cell (WBC) data were included.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Using machine learning, the bacterial risk score in CVF can be used to predict PTB.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/aji.13435","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T11:27:21Z","timestamp":1619522841000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Prediction of preterm birth based on machine learning using bacterial risk score in cervicovaginal fluid"],"prefix":"10.1111","volume":"86","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8142-7783","authenticated-orcid":false,"given":"Sunwha","family":"Park","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology College of Medicine Ewha Medical Research Institute Ewha Womans University Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daejoong","family":"Oh","sequence":"additional","affiliation":[{"name":"D&amp;P Biotech, Inc Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology College of Medicine Ewha Medical Research Institute Ewha Womans University Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gain","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology College of Medicine Ewha Medical Research Institute Ewha 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