Cited 8 times in
Development of a Screening Tool for Predicting Adverse Outcomes of Gestational Diabetes Mellitus: A Retrospective Cohort Study
DC Field | Value | Language |
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dc.contributor.author | 권자영 | - |
dc.contributor.author | 김덕원 | - |
dc.contributor.author | 김영한 | - |
dc.contributor.author | 박용원 | - |
dc.contributor.author | 박지수 | - |
dc.date.accessioned | 2017-02-24T03:06:59Z | - |
dc.date.available | 2017-02-24T03:06:59Z | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 0025-7974 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/146246 | - |
dc.description.abstract | Gestational diabetes mellitus (GDM) is a common disease in pregnancy causing maternal and fetal complications. To prevent these adverse outcomes, optimal screening and diagnostic criteria must be adequate, timely, and efficient. This study suggests a novel approach that is practical, efficient, and patient- and clinician-friendly in predicting adverse outcomes of GDM. The authors conducted a retrospective cohort study via medical record review of patients admitted between March 2001 and April 2013 at the Severance Hospital, Seoul, South Korea. Patients diagnosed by a conventional 2-step method were evaluated according to the presence of adverse outcomes (neonatal hypoglycemia, hyperbilirubinemia, and hyperinsulinemia; admission to the neonatal intensive care unit; large for gestational age; gestational insulin therapy; and gestational hypertension). Of 802 women who had an abnormal 50-g, 1-hour glucose challenge test, 306 were diagnosed with GDM and 496 did not have GDM (false-positive group). In the GDM group, 218 women (71.2%) had adverse outcomes. In contrast, 240 women (48.4%) in the false-positive group had adverse outcomes. Women with adverse outcomes had a significantly higher body mass index (BMI) at entry (P = 0.03) and fasting blood glucose (FBG) (P = 0.03). Our logistic regression model derived from 2 variables, BMI at entry and FBG, predicted GDM adverse outcome with an area under the curve of 0.642, accuracy of 61.3%, sensitivity of 57.2%, and specificity of 66.9% compared with the conventional 2-step method with an area under the curve of 0.610, accuracy of 59.1%, sensitivity of 47.6%, and specificity of 74.4%. Our model performed better in predicting GDM adverse outcomes than the conventional 2-step method using only BMI at entry and FBG. Moreover, our model represents a practical, inexpensive, efficient, reproducible, easy, and patient- and clinician-friendly approach. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Lippincott Williams & Wilkins | - |
dc.relation.isPartOf | MEDICINE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Blood Glucose | - |
dc.subject.MESH | Body Mass Index | - |
dc.subject.MESH | Diabetes, Gestational/epidemiology* | - |
dc.subject.MESH | False Positive Reactions | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Glucose Tolerance Test | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Infant, Newborn | - |
dc.subject.MESH | Infant, Newborn, Diseases/epidemiology* | - |
dc.subject.MESH | Mass Screening/methods* | - |
dc.subject.MESH | Predictive Value of Tests | - |
dc.subject.MESH | Pregnancy | - |
dc.subject.MESH | Republic of Korea | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Risk Factors | - |
dc.title | Development of a Screening Tool for Predicting Adverse Outcomes of Gestational Diabetes Mellitus: A Retrospective Cohort Study | - |
dc.type | Article | - |
dc.publisher.location | United States | - |
dc.contributor.college | College of Medicine | - |
dc.contributor.department | Dept. of Obstetrics & Gynecology | - |
dc.contributor.googleauthor | Jee Soo Park | - |
dc.contributor.googleauthor | Deok Won Kim | - |
dc.contributor.googleauthor | Ja-Young Kwon | - |
dc.contributor.googleauthor | Yong Won Park | - |
dc.contributor.googleauthor | Young Han Kim | - |
dc.contributor.googleauthor | Hee Young Cho | - |
dc.identifier.doi | 10.1097/MD.0000000000002204 | - |
dc.contributor.localId | A00246 | - |
dc.contributor.localId | A00376 | - |
dc.contributor.localId | A00730 | - |
dc.contributor.localId | A01581 | - |
dc.relation.journalcode | J02214 | - |
dc.identifier.eissn | 1536-5964 | - |
dc.identifier.pmid | 26735528 | - |
dc.contributor.alternativeName | Kwon, Ja Young | - |
dc.contributor.alternativeName | Kim, Deok Won | - |
dc.contributor.alternativeName | Kim, Young Han | - |
dc.contributor.alternativeName | Park, Yong Won | - |
dc.contributor.affiliatedAuthor | Kwon, Ja Young | - |
dc.contributor.affiliatedAuthor | Kim, Deok Won | - |
dc.contributor.affiliatedAuthor | Kim, Young Han | - |
dc.contributor.affiliatedAuthor | Park, Yong Won | - |
dc.citation.volume | 95 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 2204 | - |
dc.identifier.bibliographicCitation | MEDICINE, Vol.95(1) : 2204, 2016 | - |
dc.date.modified | 2017-02-24 | - |
dc.identifier.rimsid | 51263 | - |
dc.type.rims | ART | - |
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