Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study.
Background: 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 tr...
| Publicado en: | BMC Medicine Vol. 20; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
9/15/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159100541&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159100541 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17417015 1CI2 jtl: BMC Medicine issn: 17417015 maglogo: N pubinfo: dt: 9/15/2022 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 159100541 159100541 NLM36104698 159100541 10.1186/s12916-022-02499-7 NLM36104698 159100541 ppf: 1 ppct: 13 formats: tig: atl: Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study. aug: au: Liao, Lauren D. Ferrara, Assiamira Greenberg, Mara B. Ngo, Amanda L. Feng, Juanran Zhang, Zhenhua Bradshaw, Patrick T. Hubbard, Alan E. Zhu, Yeyi affil: Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA sug: subj: Diabetes Mellitus, Gestational Diagnosis Diabetes Mellitus, Gestational Drug Therapy Female Hypoglycemic Agents Therapeutic Use Pregnancy Prospective Studies Blood Glucose Insulin Therapeutic Use Short Portable Mental Status Questionnaire Clinical Assessment Tools Impact of Events Scale Scales Female ab: Background: 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.Methods: Among a population-based cohort of 30,474 pregnancies with GDM delivered at Kaiser Permanente Northern California in 2007-2017, we selected those in 2007-2016 as the discovery set and 2017 as the temporal/future validation set. Potential predictors were extracted from electronic health records at different timepoints (levels 1-4): (1) 1-year preconception to the last menstrual period, (2) the last menstrual period to GDM diagnosis, (3) at GDM diagnosis, and (4) 1 week 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.Results: The super learner using levels 1-4 predictors had higher predictability [tenfold cross-validated C-statistic in discovery/validation set: 0.934 (95% CI: 0.931-0.936)/0.815 (0.800-0.829)], compared to levels 1, 1-2, and 1-3 (discovery/validation set C-statistic: 0.683-0.869/0.634-0.754). 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 (0.820-0.830)/0.798 (95% CI: 0.783-0.813)].Conclusions: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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