{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T03:09:44Z","timestamp":1787022584759,"version":"build-2736575974"},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:bold>Objective:<\/jats:bold>\n                    To predict gestational diabetes mellitus (GDM) or normoglycemic status using first trimester maternal characteristics.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    We used data from a prospective cohort study. First trimester maternal characteristics were compared between women with and without GDM. Association of these variables with sugar values at glucose challenge test (GCT) and subsequent GDM was tested to identify key parameters. A predictive algorithm for GDM was developed and receiver operating characteristics (ROC) statistics was used to derive the optimal risk score. We defined normoglycemic state, when GCT and all four sugar values at oral glucose tolerance test, whenever obtained, were normal. Using same statistical approach, we developed an algorithm to predict the normoglycemic state.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    Maternal age, race, prior GDM, first trimester BMI, and systolic blood pressure (SBP) were all significantly associated with GDM. Age, BMI, and SBP were also associated with GCT values. The logistic regression analysis constructed equation and the calculated risk score yielded sensitivity, specificity, positive predictive value, and negative predictive value of 85%, 62%, 13.8%, and 98.3% for a cut-off value of 0.042, respectively (ROC-AUC \u2013 area under the curve 0.819, CI \u2013 confidence interval 0.769\u20130.868). The model constructed for normoglycemia prediction demonstrated lower performance (ROC-AUC 0.707, CI 0.668\u20130.746).\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusions:<\/jats:bold>\n                    GDM prediction can be achieved during the first trimester encounter by integration of maternal characteristics and basic measurements while normoglycemic status prediction is less effective.\n                  <\/jats:p>","DOI":"10.1515\/jpm-2014-0149","type":"journal-article","created":{"date-parts":[[2014,8,19]],"date-time":"2014-08-19T13:01:00Z","timestamp":1408453260000},"page":"283-289","source":"Crossref","is-referenced-by-count":33,"title":["First trimester prediction of maternal glycemic status"],"prefix":"10.1515","volume":"43","author":[{"given":"Rinat","family":"Gabbay-Benziv","sequence":"first","affiliation":[{"name":"Obstetrics, Gynecology and Reproductive Science, University of Maryland Medical Center, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren E.","family":"Doyle","sequence":"additional","affiliation":[{"name":"Obstetrics, Gynecology and Reproductive Science, University of Maryland Medical Center, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miriam","family":"Blitzer","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of Maryland Medical Center, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmet A.","family":"Baschat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2014,8,19]]},"container-title":["Journal of Perinatal Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/jpm-2014-0149\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/jpm-2014-0149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T23:25:47Z","timestamp":1678749947000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/jpm-2014-0149\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,8,19]]},"references-count":0,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2014,9,13]]},"published-print":{"date-parts":[[2015,5,1]]}},"alternative-id":["10.1515\/jpm-2014-0149"],"URL":"https:\/\/doi.org\/10.1515\/jpm-2014-0149","relation":{},"ISSN":["1619-3997","0300-5577"],"issn-type":[{"value":"1619-3997","type":"electronic"},{"value":"0300-5577","type":"print"}],"subject":[],"published":{"date-parts":[[2014,8,19]]}}}