Predictive Models for Very Preterm Birth: Developing a Point-of-Care Tool

Abstract

Objective The objective of this study was to create three point-of-care predictive models for very preterm birth using variables available at three different time points: prior to pregnancy, at the end of the first trimester, and mid-pregnancy. Study Design This is a retrospective cohort study of 359,396 Ohio Medicaid mothers from 2008 to 2015. The last baby for each mother was included in the final dataset. Births prior to 22 weeks were excluded. Multivariable logistic regression was used to create three models. These models were validated on a cohort that was set aside and not part of the model development. The main outcome measure was birth prior to 32 weeks. Results The final dataset contained 359,396 live births with 6,516 (1.81%) very preterm births. All models had excellent calibration. Goodness-of-fit tests suggested strong agreement between the probabilities estimated by the model and the actual outcome experience in the data. The mid-pregnancy model had acceptable discrimination with an area under the receiver operator characteristic curve of approximately 0.75 in both the developmental and validation datasets. Conclusion Using data from a large Ohio Medicaid cohort we developed point-of-care predictive models that could be used before pregnancy, after the first trimester, and in mid-pregnancy to estimate the probability of very preterm birth. Future work is needed to determine how the calculator could be used to target interventions to prevent very preterm birth. Key Points

Development and validation of a risk prediction model for preterm birth in women with gestational diabetes mellitus

“…9 Some studies randomly split the data into development and validation sets, which constitutes internal validation. 13 We externally validated our model, with the results proving the generalizability of our model.…”

“…Among the current studies, many studies have not evaluated the calibration of the models, [10][11][12] and the majority of them lack external validation. 9,13 Moreover, many models with good discrimination included cervical length and fetal fibronectin, 10,[14][15][16] which cannot be routinely measured in the antenatal care in mainland China. Crucially, we have not found an available prediction model for preterm birth in pregnant women with GDM.…”

Association of Prepregnancy Body Mass Index With Risk of Severe Maternal Morbidity and Mortality Among Medicaid Beneficiaries

“…We used an existing database of Ohio live births (2012-2017) and fetal deaths (2012-2015) that had been previously linked to Medicaid claims data. 26 To complete the study cohort, we linked fetal death data from 2016 and 2017 to the Medicaid claims data within the same period using the Link Plus Beta, version 3.0 (Centers for Disease Control and Prevention) (eAppendix in the Supplement ). The unit of analysis was the pregnancy.…”

“…After excluding records with missing prepregnancy BMI data (n = 12 526), gestational age of 20 weeks or later on January 1, 2012 (n = 23 477), and delivery earlier than 20 weeks' gestation (n = 674), and then combining the records from multifetal gestation into a single pregnancy record, we were left with a final analytic cohort of 347 497 pregnancies among 276 691 Medicaid beneficiaries (Figure). Of these beneficiaries, the median (IQR) maternal age at delivery was 25 (21)(22)(23)(24)(25)(26)(27)(28)(29) years, and 210 470 (60.6%) had non-Hispanic White race and ethnicity.…”