{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T09:57:45Z","timestamp":1787997465135,"version":"build-2784847793"},"reference-count":39,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016989","name":"Korea University Medical Center","doi-asserted-by":"crossref","award":["K1925051"],"award-info":[{"award-number":["K1925051"]}],"id":[{"id":"10.13039\/100016989","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Maternal heart disease is suspected to affect preterm birth (PTB); however, validated studies on the association between maternal heart disease and PTB are still limited. This study aimed to build a prediction model for PTB using machine learning analysis and nationwide population data, and to investigate the association between various maternal heart diseases and PTB.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>A population-based, retrospective cohort study was conducted using data obtained from the Korea National Health Insurance claims database, that included 174,926 primiparous women aged 25\u201340 years who delivered in 2017. The random forest variable importance was used to identify the major determinants of PTB and test its associations with maternal heart diseases, i.e., arrhythmia, ischemic heart disease (IHD), cardiomyopathy, congestive heart failure, and congenital heart disease first diagnosed before or during pregnancy.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>Among the study population, 12,701 women had PTB, and 12,234 women had at least one heart disease. The areas under the receiver-operating-characteristic curves of the random forest with oversampling data were within 88.53 to 95.31. The accuracy range was 89.59 to 95.22. The most critical variables for PTB were socioeconomic status and age. The random forest variable importance indicated the strong associations of PTB with arrhythmia and IHD among the maternal heart diseases. Within the arrhythmia group, atrial fibrillation\/flutter was the most significant risk factor for PTB based on the Shapley additive explanation value.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Careful evaluation and management of maternal heart disease during pregnancy would help reduce PTB. Machine learning is an effective prediction model for PTB and the major predictors of PTB included maternal heart disease such as arrhythmia and IHD.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0283959","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T18:04:02Z","timestamp":1680285842000},"page":"e0283959","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Preterm birth and maternal heart disease: A machine learning analysis using the Korean national health insurance database"],"prefix":"10.1371","volume":"18","author":[{"given":"Jue Seong","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun-Saem","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujin","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwang-Sig","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6314-4621","authenticated-orcid":true,"given":"Ki Hoon","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"issue":"1","key":"pone.0283959.ref001","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1002\/ijgo.13195","article-title":"Global burden of preterm birth.","volume":"150","author":"SR Walani","year":"2020","journal-title":"Int J Gynaecol Obstet"},{"issue":"2","key":"pone.0283959.ref002","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.siny.2015.12.007","article-title":"Global burden of prematurity.","volume":"21","author":"MS Harrison","year":"2016","journal-title":"Semin Fetal Neonatal Med"},{"issue":"10063","key":"pone.0283959.ref003","doi-asserted-by":"crossref","first-page":"3027","DOI":"10.1016\/S0140-6736(16)31593-8","article-title":"Global, regional, and national causes of under-5 mortality in 2000\u201315: an updated systematic analysis with implications for the Sustainable Development Goals","volume":"388","author":"L Liu","year":"2016","journal-title":"Lancet"},{"issue":"34","key":"pone.0283959.ref004","doi-asserted-by":"crossref","first-page":"3165","DOI":"10.1093\/eurheartj\/ehy340","article-title":"2018 ESC Guidelines for the management of cardiovascular diseases during pregnancy","volume":"39","author":"V Regitz-Zagrosek","year":"2018","journal-title":"Eur Heart J"},{"issue":"13","key":"pone.0283959.ref005","first-page":"1","article-title":"Births: Final Data for 2018.","volume":"68","author":"JA Martin","year":"2019","journal-title":"Natl Vital Stat Rep."},{"issue":"11","key":"pone.0283959.ref006","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1038\/s41569-020-0390-z","article-title":"Pregnancy and cardiovascular disease.","volume":"17","author":"KP Ramlakhan","year":"2020","journal-title":"Nat Rev Cardiol"},{"issue":"41","key":"pone.0283959.ref007","doi-asserted-by":"crossref","first-page":"4252","DOI":"10.1093\/eurheartj\/ehab571","article-title":"Maternal and neonatal complications in women with congenital heart disease: a nationwide analysis","volume":"42","author":"AE Lammers","year":"2021","journal-title":"Eur Heart J"},{"issue":"47","key":"pone.0283959.ref008","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1093\/eurheartj\/ehz136","article-title":"Pregnancy outcomes in women with cardiovascular disease: evolving trends over 10 years in the ESC Registry Of Pregnancy And Cardiac disease (ROPAC).","volume":"40","author":"J Roos-Hesselink","year":"2019","journal-title":"Eur Heart J"},{"issue":"5","key":"pone.0283959.ref009","doi-asserted-by":"crossref","first-page":"e193667","DOI":"10.1001\/jamanetworkopen.2019.3667","article-title":"Association of adult congenital heart disease with pregnancy, maternal, and neonatal outcomes.","volume":"2","author":"K Ramage","year":"2019","journal-title":"JAMA Netw Open"},{"issue":"12","key":"pone.0283959.ref010","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.3349\/ymj.2022.0111","article-title":"Major adverse cardiovascular events in Korean congenital heart disease patients: a nationwide age- and sex-matched case-control study","volume":"63","author":"JS Lee","year":"2022","journal-title":"Yonsei Med J"},{"issue":"5","key":"pone.0283959.ref011","article-title":"Association of preterm birth with inflammatory bowel disease and salivary gland disease: machine learning analysis using national health insurance data.","volume":"19","author":"KS Lee","year":"2022","journal-title":"Int J Environ Res Public Health"},{"issue":"9","key":"pone.0283959.ref012","article-title":"Application of artificial intelligence in early diagnosis of spontaneous preterm labor and birth","volume":"10","author":"KS Lee","year":"2020","journal-title":"Diagnostics (Basel)."},{"issue":"5","key":"pone.0283959.ref013","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1007\/s00404-022-06405-7","article-title":"Association of preterm birth with medications: machine learning analysis using national health insurance data","volume":"305","author":"KS Lee","year":"2022","journal-title":"Arch Gynecol Obstet"},{"issue":"1","key":"pone.0283959.ref014","doi-asserted-by":"crossref","first-page":"19153","DOI":"10.1038\/s41598-022-23782-w","article-title":"Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015.","volume":"12","author":"K Wong","year":"2022","journal-title":"Sci Rep."},{"issue":"11","key":"pone.0283959.ref015","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1016\/j.annepidem.2018.08.008","article-title":"Application of machine-learning to predict early spontaneous preterm birth among nulliparous non-Hispanic black and white women.","volume":"28","author":"A Weber","year":"2018","journal-title":"Ann Epidemiol."},{"issue":"6","key":"pone.0283959.ref016","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1007\/s00404-019-05325-3","article-title":"Analysis of big data for prediction of provider-initiated preterm birth and spontaneous premature deliveries and ranking the predictive features","volume":"300","author":"T Khatibi","year":"2019","journal-title":"Arch Gynecol Obstet"},{"issue":"1","key":"pone.0283959.ref017","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s13755-020-00105-9","article-title":"Predicting risk of stillbirth and preterm pregnancies with machine learning.","volume":"8","author":"A Koivu","year":"2020","journal-title":"Health Inf Sci Syst"},{"issue":"3","key":"pone.0283959.ref018","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1002\/uog.20422","article-title":"Development and validation of predictive models for QUiPP App v.2: tool for predicting preterm birth in women with symptoms of threatened preterm labor.","volume":"55","author":"J Carter","year":"2020","journal-title":"Ultrasound Obstet Gynecol."},{"issue":"1","key":"pone.0283959.ref019","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1186\/s12884-021-03654-3","article-title":"A hierarchical procedure to select intrauterine and extrauterine factors for methodological validation of preterm birth risk estimation.","volume":"21","author":"PA Della Rosa","year":"2021","journal-title":"BMC Pregnancy Childbirth"},{"issue":"11","key":"pone.0283959.ref020","article-title":"Maternal cardiac output and fetal doppler predict adverse neonatal outcomes in pregnant women with heart disease","volume":"4","author":"RM Wald","year":"2015","journal-title":"J Am Heart Assoc"},{"issue":"23","key":"pone.0283959.ref021","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1161\/CIRCULATIONAHA.113.002810","article-title":"Uteroplacental blood flow, cardiac function, and pregnancy outcome in women with congenital heart disease","volume":"128","author":"PG Pieper","year":"2013","journal-title":"Circulation"},{"issue":"4","key":"pone.0283959.ref022","doi-asserted-by":"crossref","first-page":"e156","DOI":"10.1093\/eurjpc\/zwab121","article-title":"Association of pre-pregnancy cardiovascular risk factor burden with adverse maternal and offspring outcomes.","volume":"29","author":"MC Wang","year":"2022","journal-title":"Eur J Prev Cardiol"},{"issue":"6","key":"pone.0283959.ref023","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1067\/mob.2000.107621","article-title":"Preterm delivery in women with pregestational diabetes mellitus or chronic hypertension relative to women with uncomplicated pregnancies. The National institute of Child health and Human Development Maternal- Fetal Medicine Units Network","volume":"183","author":"BM Sibai","year":"2000","journal-title":"Am J Obstet Gynecol"},{"issue":"6","key":"pone.0283959.ref024","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1161\/CIRCULATIONAHA.116.025954","article-title":"Preterm delivery and maternal cardiovascular disease in young and middle-aged adult women","volume":"135","author":"LJ Tanz","year":"2017","journal-title":"Circulation"},{"issue":"4","key":"pone.0283959.ref025","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.jacc.2018.10.075","article-title":"Use of medication for cardiovascular disease during pregnancy: JACC state-of-the-art review","volume":"73","author":"DG Halpern","year":"2019","journal-title":"J Am Coll Cardiol"},{"issue":"5","key":"pone.0283959.ref026","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1161\/CIRCEP.114.001517","article-title":"Contemporary management of arrhythmias during pregnancy.","volume":"7","author":"AD Enriquez","year":"2014","journal-title":"Circ Arrhythm Electrophysiol"},{"issue":"8","key":"pone.0283959.ref027","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1016\/j.amjcard.2005.11.041","article-title":"Recurrence rates of arrhythmias during pregnancy in women with previous tachyarrhythmia and impact on fetal and neonatal outcomes.","volume":"97","author":"CK Silversides","year":"2006","journal-title":"Am J Cardiol"},{"issue":"2","key":"pone.0283959.ref028","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.advms.2019.07.003","article-title":"Incidence of atrial fibrillation in pregnancy and clinical significance: a meta-analysis.","volume":"64","author":"R Chokesuwattanaskul","year":"2019","journal-title":"Adv Med Sci"},{"issue":"27","key":"pone.0283959.ref029","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1093\/eurheartj\/eht199","article-title":"Endothelial dysfunction in coronary heart disease is more than a systemic process","volume":"34","author":"P Ganz","year":"2013","journal-title":"Eur Heart J"},{"issue":"5","key":"pone.0283959.ref030","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1067\/mob.2001.117638","article-title":"The role of thrombin in preterm parturition.","volume":"185","author":"MA Elovitz","year":"2001","journal-title":"Am J Obstet Gynecol"},{"issue":"1","key":"pone.0283959.ref031","doi-asserted-by":"crossref","first-page":"e85716","DOI":"10.1371\/journal.pone.0085716","article-title":"Maternal biomarkers of endothelial dysfunction and preterm delivery.","volume":"9","author":"X Chen","year":"2014","journal-title":"PLoS One"},{"issue":"14","key":"pone.0283959.ref032","doi-asserted-by":"crossref","first-page":"e021019","DOI":"10.1161\/JAHA.121.021019","article-title":"Maternal heart failure","volume":"10","author":"RA Bright","year":"2021","journal-title":"J Am Heart Assoc"},{"issue":"23","key":"pone.0283959.ref033","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1136\/heartjnl-2018-313156","article-title":"Maternal and fetal outcomes in pregnant women with heart failure","volume":"104","author":"AT Ng","year":"2018","journal-title":"Heart"},{"issue":"11","key":"pone.0283959.ref034","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1002\/pd.5770","article-title":"Adverse perinatal outcome and placental abnormalities in pregnancies with major fetal congenital heart defects: A retrospective case-control study","volume":"40","author":"V Giorgione","year":"2020","journal-title":"Prenat Diagn"},{"issue":"11","key":"pone.0283959.ref035","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1093\/eurheartj\/eht389","article-title":"Maternal and foetal angiogenic imbalance in congenital heart defects","volume":"35","author":"E Llurba","year":"2014","journal-title":"Eur Heart J"},{"issue":"11","key":"pone.0283959.ref036","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1161\/CIRCULATIONAHA.113.003904","article-title":"Chronic hypertension in pregnancy","volume":"129","author":"EW Seely","year":"2014","journal-title":"Circulation"},{"issue":"1","key":"pone.0283959.ref037","doi-asserted-by":"crossref","first-page":"e26","DOI":"10.1097\/AOG.0000000000003020","article-title":"ACOG Practice Bulletin No. 203: chronic hypertension in pregnancy","volume":"133","year":"2019","journal-title":"Obstet Gynecol"},{"issue":"3","key":"pone.0283959.ref038","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1093\/ije\/dyt251","article-title":"Neonatal morbidity associated with late preterm and early term birth: the roles of gestational age and biological determinants of preterm birth.","volume":"43","author":"HK Brown","year":"2014","journal-title":"Int J Epidemiol"},{"issue":"3","key":"pone.0283959.ref039","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.ajog.2004.09.021","article-title":"The Preterm prediction study: association between maternal body mass index and spontaneous and indicated preterm birth.","volume":"192","author":"I Hendler","year":"2005","journal-title":"Am J Obstet Gynecol"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0283959","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T18:04:26Z","timestamp":1680285866000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0283959"}},"subtitle":[],"editor":[{"given":"Gang","family":"Qin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2023,3,31]]},"references-count":39,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3,31]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0283959","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,31]]}}}