{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T21:17:52Z","timestamp":1792185472077,"version":"4.4.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:00:00Z","timestamp":1663200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:00:00Z","timestamp":1663200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"US National Institutes of Health Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program","award":["K12HD052163"],"award-info":[{"award-number":["K12HD052163"]}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["K01DK120807"],"award-info":[{"award-number":["K01DK120807"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Kaiser Permanente Northern California Community Benefits Program","award":["RNG209492"],"award-info":[{"award-number":["RNG209492"]}]},{"name":"NIDDK Health Delivery Systems \u2013 Center for Diabetes Translational Research (HDS-CDTR) Pilot and Feasibility Program","award":["P30DK092924"],"award-info":[{"award-number":["P30DK092924"]}]},{"name":"National Science Foundation Graduate Research Fellowship","award":["DGE1752814"],"award-info":[{"award-number":["DGE1752814"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Gestational diabetes (GDM) is prevalent and benefits from timely and effective treatment, given the short window to impact glycemic control. Clinicians face major barriers to choosing effectively among treatment modalities [medical nutrition therapy (MNT) with or without pharmacologic treatment (antidiabetic oral agents and\/or insulin)]. We investigated whether clinical data at varied stages of pregnancy can predict GDM treatment modality.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Among a population-based cohort of 30,474 pregnancies with GDM delivered at Kaiser Permanente Northern California in 2007\u20132017, we selected those in 2007\u20132016 as the discovery set and 2017 as the temporal\/future validation set. Potential predictors were extracted from electronic health records at different timepoints (levels 1\u20134): (1) 1-year preconception to the last menstrual period, (2) the last menstrual period to GDM diagnosis, (3) at GDM diagnosis, and (4) 1\u00a0week after GDM diagnosis. We compared transparent and ensemble machine learning prediction methods, including least absolute shrinkage and selection operator (LASSO) regression and super learner, containing classification and regression tree, LASSO regression, random forest, and extreme gradient boosting algorithms, to predict risks for pharmacologic treatment beyond MNT.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The super learner using levels 1\u20134 predictors had higher predictability [tenfold cross-validated C-statistic in discovery\/validation set: 0.934 (95% CI: 0.931\u20130.936)\/0.815 (0.800\u20130.829)], compared to levels 1, 1\u20132, and 1\u20133 (discovery\/validation set C-statistic: 0.683\u20130.869\/0.634\u20130.754). A simpler, more interpretable model, including timing of GDM diagnosis, diagnostic fasting glucose value, and the status and frequency of glycemic control at fasting during one-week post diagnosis, was developed using tenfold cross-validated logistic regression based on super learner-selected predictors. This model compared to the super learner had only a modest reduction in predictability [discovery\/validation set C-statistic: 0.825 (0.820\u20130.830)\/0.798 (95% CI: 0.783\u20130.813)].<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Clinical data demonstrated reasonably high predictability for GDM treatment modality at the time of GDM diagnosis and high predictability at 1-week post GDM diagnosis. These population-based, clinically oriented models may support algorithm-based risk-stratification for treatment modality, inform timely treatment, and catalyze more effective management of GDM.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12916-022-02499-7","type":"journal-article","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:03:21Z","timestamp":1663200201000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study"],"prefix":"10.1186","volume":"20","author":[{"given":"Lauren D.","family":"Liao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Assiamira","family":"Ferrara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mara B.","family":"Greenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amanda L.","family":"Ngo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juanran","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick T.","family":"Bradshaw","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan E.","family":"Hubbard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7296-738X","authenticated-orcid":false,"given":"Yeyi","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"issue":"3","key":"2499_CR1","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1097\/01.AOG.0000113623.18286.20","volume":"103","author":"A Ferrara","year":"2004","unstructured":"Ferrara A, Kahn HS, Quesenberry CP, Riley C, Hedderson MM. An increase in the incidence of gestational diabetes mellitus: Northern California, 1991\u20132000. Obstet Gynecol. 2004;103(3):526\u201333.","journal-title":"Obstet Gynecol"},{"issue":"1","key":"2499_CR2","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s11892-015-0699-x","volume":"16","author":"Y Zhu","year":"2016","unstructured":"Zhu Y, Zhang C. Prevalence of gestational diabetes and risk of progression to type 2 diabetes: a global perspective. Curr Diab Rep. 2016;16(1):7.","journal-title":"Curr Diab Rep"},{"issue":"1","key":"2499_CR3","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/s41572-019-0098-8","volume":"5","author":"HD McIntyre","year":"2019","unstructured":"McIntyre HD, Catalano P, Zhang C, Desoye G, Mathiesen ER, Damm P. Gestational diabetes mellitus. Nat Rev Dis Primers. 2019;5(1):47.","journal-title":"Nat Rev Dis Primers"},{"issue":"1","key":"2499_CR4","doi-asserted-by":"publisher","first-page":"e17","DOI":"10.1097\/AOG.0000000000002159","volume":"130","author":"Practice Bulletin No","year":"2017","unstructured":"Practice Bulletin No. 180: Gestational Diabetes Mellitus. Obstet Gynecol. 2017;130(1):e17\u201337.","journal-title":"Obstet Gynecol"},{"issue":"Supplement 1","key":"2499_CR5","doi-asserted-by":"publisher","first-page":"S200","DOI":"10.2337\/dc21-S014","volume":"44","author":"American Diabetes Association","year":"2021","unstructured":"American Diabetes Association. 14. Management of diabetes in pregnancy: standards of medical care in diabetes-2021. Diabetes Care. 2021;44(Supplement 1):S200\u201310.","journal-title":"Diabetes Care"},{"issue":"1","key":"2499_CR6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.2337\/dc10-0607","volume":"34","author":"DK Tisi","year":"2011","unstructured":"Tisi DK, Burns DH, Luskey GW, Koski KG. Fetal exposure to altered amniotic fluid glucose, insulin, and insulin-like growth factor-binding protein 1 occurs before screening for gestational diabetes mellitus. Diabetes Care. 2011;34(1):139\u201344.","journal-title":"Diabetes Care"},{"issue":"5","key":"2499_CR7","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1111\/j.1479-828X.2010.01218.x","volume":"50","author":"JR Flack","year":"2010","unstructured":"Flack JR, Ross GP, Ho S, McElduff A. Recommended changes to diagnostic criteria for gestational diabetes: impact on workload. Aust N Z J Obstet Gynaecol. 2010;50(5):439\u201343.","journal-title":"Aust N Z J Obstet Gynaecol"},{"issue":"6","key":"2499_CR8","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1097\/AOG.0000000000000285","volume":"123","author":"W Camelo Castillo","year":"2014","unstructured":"Camelo Castillo W, Boggess K, Sturmer T, Brookhart MA, Benjamin DK Jr, Jonsson Funk M. Trends in glyburide compared with insulin use for gestational diabetes treatment in the United States, 2000\u20132011. Obstet Gynecol. 2014;123(6):1177\u201384.","journal-title":"Obstet Gynecol"},{"issue":"Suppl 1","key":"2499_CR9","doi-asserted-by":"publisher","first-page":"S39","DOI":"10.1016\/j.ijgo.2008.11.027","volume":"104","author":"EJ Coetzee","year":"2009","unstructured":"Coetzee EJ. Pregnancy and diabetes scenario around the world: Africa. Int J Gynaecol Obstet. 2009;104(Suppl 1):S39-41.","journal-title":"Int J Gynaecol Obstet"},{"issue":"5","key":"2499_CR10","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1038\/sj.jp.7210759","volume":"22","author":"L Goetzl","year":"2002","unstructured":"Goetzl L, Wilkins I. Glyburide compared to insulin for the treatment of gestational diabetes mellitus: a cost analysis. J Perinatol. 2002;22(5):403\u20136.","journal-title":"J Perinatol"},{"issue":"4","key":"2499_CR11","doi-asserted-by":"publisher","first-page":"15","DOI":"10.7812\/TPP\/15-225","volume":"20","author":"N Gordon","year":"2016","unstructured":"Gordon N, Lin T. The Kaiser Permanente Northern California adult member health survey. Perm J. 2016;20(4):15\u2013225.","journal-title":"Perm J"},{"issue":"17","key":"2499_CR12","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1001\/jama.296.17.2105","volume":"296","author":"AS Go","year":"2006","unstructured":"Go AS, Lee WY, Yang J, Lo JC, Gurwitz JH. Statin therapy and risks for death and hospitalization in chronic heart failure. JAMA. 2006;296(17):2105\u201311.","journal-title":"JAMA"},{"issue":"7","key":"2499_CR13","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1016\/0002-9378(82)90349-0","volume":"144","author":"MW Carpenter","year":"1982","unstructured":"Carpenter MW, Coustan DR. Criteria for screening tests for gestational diabetes. Am J Obstet Gynecol. 1982;144(7):768\u201373.","journal-title":"Am J Obstet Gynecol"},{"issue":"1","key":"2499_CR14","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s12884-017-1301-0","volume":"17","author":"Y Zhu","year":"2017","unstructured":"Zhu Y, Hedderson MM, Feng J, Mevi AA, Ferrara A. The Pregnancy Environment and Lifestyle Study (PETALS): a population-based longitudinal multi-racial birth cohort. BMC Pregnancy Childbirth. 2017;17(1):122.","journal-title":"BMC Pregnancy Childbirth"},{"issue":"Suppl 2","key":"2499_CR15","doi-asserted-by":"publisher","first-page":"S141","DOI":"10.2337\/dc07-s206","volume":"30","author":"A Ferrara","year":"2007","unstructured":"Ferrara A. Increasing prevalence of gestational diabetes mellitus: a public health perspective. Diabetes Care. 2007;30(Suppl 2):S141-146.","journal-title":"Diabetes Care"},{"issue":"6","key":"2499_CR16","doi-asserted-by":"publisher","first-page":"491 e491","DOI":"10.1016\/j.ajog.2012.04.019","volume":"206","author":"A Ferrara","year":"2012","unstructured":"Ferrara A, Hedderson MM, Ching J, Kim C, Peng T, Crites YM. Referral to telephonic nurse management improves outcomes in women with gestational diabetes. Am J Obstet Gynecol. 2012;206(6):491 e491-495.","journal-title":"Am J Obstet Gynecol"},{"key":"2499_CR17","doi-asserted-by":"publisher","unstructured":"van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol Biol.\u00a02007;6:Article25.\u00a0https:\/\/doi.org\/10.2202\/1544-6115.1309.","DOI":"10.2202\/1544-6115.1309"},{"issue":"1","key":"2499_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1\u201322.","journal-title":"J Stat Softw"},{"issue":"3","key":"2499_CR19","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837\u201345.","journal-title":"Biometrics"},{"issue":"1","key":"2499_CR20","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45(1):5\u201332.","journal-title":"Mach Learn"},{"issue":"7","key":"2499_CR21","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1007\/s10995-017-2269-0","volume":"21","author":"J Ares","year":"2017","unstructured":"Ares J, Martin-Nieto A, Diaz-Naya L, Tarton T, Menendez-Prada T, Ragnarsson CS, Delgado-Alvarez E, Menendez-Torre E. Gestational diabetes mellitus (GDM): relationship between higher cutoff values for 100 g Oral Glucose Tolerance Test (OGTT) and insulin requirement during pregnancy. Matern Child Health J. 2017;21(7):1488\u201392.","journal-title":"Matern Child Health J"},{"issue":"5","key":"2499_CR22","first-page":"381","volume":"5","author":"O Bakiner","year":"2013","unstructured":"Bakiner O, Bozkirli E, Ozsahin K, Sariturk C, Ertorer E. Risk factors that can predict antenatal insulin need in gestational diabetes. J Clin Med Res. 2013;5(5):381\u20138.","journal-title":"J Clin Med Res"},{"issue":"7","key":"2499_CR23","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1089\/jwh.2007.0667","volume":"17","author":"VH Gonzalez-Quintero","year":"2008","unstructured":"Gonzalez-Quintero VH, Istwan NB, Rhea DJ, Tudela CM, Flick AA, de la Torre L, Stanziano GJ. Antenatal factors predicting subsequent need for insulin treatment in women with gestational diabetes. J Womens Health (Larchmt). 2008;17(7):1183\u20137.","journal-title":"J Womens Health (Larchmt)"},{"issue":"6","key":"2499_CR24","first-page":"262","volume":"74","author":"SH Koning","year":"2016","unstructured":"Koning SH, Scheuneman KA, Lutgers HL, Korteweg FJ, van den Berg G, Sollie KM, Roos A, van Loon AJ, Links TP, van Tol KM, et al. Risk stratification for healthcare planning in women with gestational diabetes mellitus. Neth J Med. 2016;74(6):262\u20139.","journal-title":"Neth J Med"},{"issue":"8","key":"2499_CR25","doi-asserted-by":"publisher","first-page":"565","DOI":"10.3109\/09513590.2014.911274","volume":"30","author":"S Mitra","year":"2014","unstructured":"Mitra S, Nayak PK, Sahoo J, Mathew A, Padma A, Kamalanathan S, Agrawal S. Predictors for antenatal insulin requirement in gestational diabetes. Gynecol Endocrinol. 2014;30(8):565\u20138.","journal-title":"Gynecol Endocrinol"},{"issue":"3","key":"2499_CR26","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1111\/j.1479-828X.1995.tb01977.x","volume":"35","author":"YY Tan","year":"1995","unstructured":"Tan YY, Liauw PC, Yeo GS. Using glucose tolerance test results to predict insulin requirement in women with gestational diabetes. Aust N Z J Obstet Gynaecol. 1995;35(3):262\u20136.","journal-title":"Aust N Z J Obstet Gynaecol"},{"issue":"5","key":"2499_CR27","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1111\/j.1479-828X.2011.01329.x","volume":"51","author":"VW Wong","year":"2011","unstructured":"Wong VW, Jalaludin B. Gestational diabetes mellitus: who requires insulin therapy? Aust N Z J Obstet Gynaecol. 2011;51(5):432\u20136.","journal-title":"Aust N Z J Obstet Gynaecol"},{"issue":"12","key":"2499_CR28","doi-asserted-by":"publisher","first-page":"2036","DOI":"10.1080\/14767058.2018.1424820","volume":"32","author":"A Souza","year":"2019","unstructured":"Souza A, Costa RA, Paganoti CF, Rodrigues AS, Zugaib M, Hadar E, Hod M, Francisco RPV. Can we stratify the risk for insulin need in women diagnosed early with gestational diabetes by fasting blood glucose? J Matern Fetal Neonatal Med. 2019;32(12):2036\u201341.","journal-title":"J Matern Fetal Neonatal Med"},{"issue":"11","key":"2499_CR29","doi-asserted-by":"publisher","first-page":"2331","DOI":"10.1007\/s00125-016-4047-8","volume":"59","author":"RA Barnes","year":"2016","unstructured":"Barnes RA, Wong T, Ross GP, Jalaludin BB, Wong VW, Smart CE, Collins CE, MacDonald-Wicks L, Flack JR. A novel validated model for the prediction of insulin therapy initiation and adverse perinatal outcomes in women with gestational diabetes mellitus. Diabetologia. 2016;59(11):2331\u20138.","journal-title":"Diabetologia"},{"issue":"2","key":"2499_CR30","first-page":"105","volume":"31","author":"H Mendez-Figueroa","year":"2014","unstructured":"Mendez-Figueroa H, Daley J, Lopes VV, Coustan DR. Predicting the need for medical therapy in patients with mild gestational diabetes. Am J Perinatol. 2014;31(2):105\u201312.","journal-title":"Am J Perinatol"},{"issue":"10","key":"2499_CR31","doi-asserted-by":"publisher","first-page":"2214","DOI":"10.2337\/dc11-0499","volume":"34","author":"T Pertot","year":"2011","unstructured":"Pertot T, Molyneaux L, Tan K, Ross GP, Yue DK, Wong J. Can common clinical parameters be used to identify patients who will need insulin treatment in gestational diabetes mellitus? Diabetes Care. 2011;34(10):2214\u20136.","journal-title":"Diabetes Care"},{"issue":"1","key":"2499_CR32","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.diabres.2009.12.023","volume":"88","author":"AD Sapienza","year":"2010","unstructured":"Sapienza AD, Francisco RP, Trindade TC, Zugaib M. Factors predicting the need for insulin therapy in patients with gestational diabetes mellitus. Diabetes Res Clin Pract. 2010;88(1):81\u20136.","journal-title":"Diabetes Res Clin Pract"}],"container-title":["BMC Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12916-022-02499-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12916-022-02499-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12916-022-02499-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:04:00Z","timestamp":1663200240000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedicine.biomedcentral.com\/articles\/10.1186\/s12916-022-02499-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,15]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["2499"],"URL":"https:\/\/doi.org\/10.1186\/s12916-022-02499-7","relation":{},"ISSN":["1741-7015"],"issn-type":[{"value":"1741-7015","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,15]]},"assertion":[{"value":"15 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This data-only project was approved by the Kaiser Permanente Northern California Institutional Review Board, which waived the requirement for informed consent from participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Yeyi Zhu is a member of the BMC Medicine editorial board. None of the authors, including Yeyi Zhu, had a role in the peer review or handling of this manuscript. The authors, other than Yeyi Zhu, declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"307"}}