Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313637901> ?p ?o ?g. }
Showing items 1 to 68 of
68
with 100 items per page.
- W4313637901 endingPage "S753" @default.
- W4313637901 startingPage "S752" @default.
- W4313637901 abstract "Current early screening gestational diabetes mellitus (GDM) recommendations have yielded conflicting findings, suggesting that the current prediction parameters are insufficient. Machine learning is an analytic approach capable of processing complex interactions between variables that could yield improved predictive capabilities and enhance clinical decision making. We sought to fit a prediction model for GDM that could be implemented at the first prenatal visit. Model development utilized prenatal data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) cohort. Our primary outcome was a study criteria diagnosis of GDM, with pregestational diabetes and missing outcome data as exclusion criteria. Random forest (RF) models were developed using data commonly available during the first prenatal visit, including body mass index (BMI), laboratory data, and family history of diabetes, and evaluated using the area under the receiver operator curve (AUROC). The sensitivity and specificity of the RF model was then compared to current early screening parameters available in nuMoM2b (BMI over 30 kg/m2 or a first degree relative with diabetes). 8,242 nulliparous patients were included, with 359 (4.4%) developing GDM. RF modeling produced an AUROC of 0.71 [95% CI: 0.65, 0.77] on 26 features (Figure), with BMI at first prenatal visit, pre-pregnancy weight, age, and complete blood count results being the most predictive. The current early GDM screening criteria yielded a sensitivity and specificity of 0.63 and 0.61, respectively. In comparison, our model yielded a specificity of 0.69 (at a sensitivity of 0.63) and sensitivity of 0.70 (at a specificity of 0.61), thereby correctly identifying 11% more GDM patients than the current screening criteria (Table). GDM can be predicted with moderate discrimination using machine learning models trained on nulliparous patient available at the first prenatal visit. Our model outperforms the current early GDM screening parameters and can ultimately assist screening to predict a diagnosis for earlier management.View Large Image Figure ViewerDownload Hi-res image Download (PPT)" @default.
- W4313637901 created "2023-01-07" @default.
- W4313637901 creator A5001344367 @default.
- W4313637901 creator A5007207120 @default.
- W4313637901 creator A5012633614 @default.
- W4313637901 creator A5028437021 @default.
- W4313637901 creator A5049415543 @default.
- W4313637901 creator A5068303681 @default.
- W4313637901 date "2023-01-01" @default.
- W4313637901 modified "2023-09-27" @default.
- W4313637901 title "Using machine learning to predict the risk of developing gestational diabetes using a contemporary cohort" @default.
- W4313637901 doi "https://doi.org/10.1016/j.ajog.2022.11.1254" @default.
- W4313637901 hasPublicationYear "2023" @default.
- W4313637901 type Work @default.
- W4313637901 citedByCount "0" @default.
- W4313637901 crossrefType "journal-article" @default.
- W4313637901 hasAuthorship W4313637901A5001344367 @default.
- W4313637901 hasAuthorship W4313637901A5007207120 @default.
- W4313637901 hasAuthorship W4313637901A5012633614 @default.
- W4313637901 hasAuthorship W4313637901A5028437021 @default.
- W4313637901 hasAuthorship W4313637901A5049415543 @default.
- W4313637901 hasAuthorship W4313637901A5068303681 @default.
- W4313637901 hasConcept C126322002 @default.
- W4313637901 hasConcept C131872663 @default.
- W4313637901 hasConcept C134018914 @default.
- W4313637901 hasConcept C2779234561 @default.
- W4313637901 hasConcept C2779434492 @default.
- W4313637901 hasConcept C2780221984 @default.
- W4313637901 hasConcept C46973012 @default.
- W4313637901 hasConcept C54355233 @default.
- W4313637901 hasConcept C555293320 @default.
- W4313637901 hasConcept C58471807 @default.
- W4313637901 hasConcept C71924100 @default.
- W4313637901 hasConcept C72563966 @default.
- W4313637901 hasConcept C86803240 @default.
- W4313637901 hasConceptScore W4313637901C126322002 @default.
- W4313637901 hasConceptScore W4313637901C131872663 @default.
- W4313637901 hasConceptScore W4313637901C134018914 @default.
- W4313637901 hasConceptScore W4313637901C2779234561 @default.
- W4313637901 hasConceptScore W4313637901C2779434492 @default.
- W4313637901 hasConceptScore W4313637901C2780221984 @default.
- W4313637901 hasConceptScore W4313637901C46973012 @default.
- W4313637901 hasConceptScore W4313637901C54355233 @default.
- W4313637901 hasConceptScore W4313637901C555293320 @default.
- W4313637901 hasConceptScore W4313637901C58471807 @default.
- W4313637901 hasConceptScore W4313637901C71924100 @default.
- W4313637901 hasConceptScore W4313637901C72563966 @default.
- W4313637901 hasConceptScore W4313637901C86803240 @default.
- W4313637901 hasIssue "1" @default.
- W4313637901 hasLocation W43136379011 @default.
- W4313637901 hasOpenAccess W4313637901 @default.
- W4313637901 hasPrimaryLocation W43136379011 @default.
- W4313637901 hasRelatedWork W1968116260 @default.
- W4313637901 hasRelatedWork W2039515840 @default.
- W4313637901 hasRelatedWork W2140332838 @default.
- W4313637901 hasRelatedWork W2350321065 @default.
- W4313637901 hasRelatedWork W2353304521 @default.
- W4313637901 hasRelatedWork W2802250511 @default.
- W4313637901 hasRelatedWork W3116062483 @default.
- W4313637901 hasRelatedWork W4210567350 @default.
- W4313637901 hasRelatedWork W4210993797 @default.
- W4313637901 hasRelatedWork W4224233009 @default.
- W4313637901 hasVolume "228" @default.
- W4313637901 isParatext "false" @default.
- W4313637901 isRetracted "false" @default.
- W4313637901 workType "article" @default.