Assessing the Relationship between Gestational Glycemic Control and Risk of Preterm Birth in Women with Type 1 Diabetes: A Joint Modeling Approach

Abstract

Background. Characterizing maternal glucose sampling over the course of the entire pregnancy is an important step toward improvement in prediction of adverse birth outcome, such as preterm birth, for women with type 1 diabetes mellitus (T1DM). Objectives. To characterize the relationship between the gestational glycemic profile and risk of preterm birth using a joint modeling approach. Methods. A joint model was developed to simultaneously characterize the relationship between a longitudinal outcome (daily blood glucose sampling) and an event process (preterm birth). A linear mixed effects model using natural cubic splines was fitted to predict the longitudinal submodel. Covariates included mother’s age at last menstrual period, age at diabetes onset, body mass index, hypertension, retinopathy, and nephropathy. Various association structures (value, value plus slope, and area under the curve) were examined before selecting the final joint model. We compared the joint modeling approach to the time-dependent Cox model (TDCM). Results. A total of 16,480 glucose readings over gestation (range: 50-260 days) with 32 women (28%) having preterm birth was included in the study. Mother’s age at last menstrual period and age at diabetes onset were statistically significant (beta = 1.29, 95% CI 1.10, 1.72; beta = 0.84, 95% CI 0.62, 0.98) for the longitudinal submodel, reflecting that older women tended to have higher mean blood glucose and those with later diabetes onset tended to have a lower mean blood glucose level. The presence of nephropathy was statistically significant in the event submodel (beta = 2.29, 95% CI 1.05, 4.48). Cumulative association parameterization provided the best joint model fit. The joint model provided better fit compared to the time-dependent Cox model (DIC JM=19,895; DIC TDCM=19,932). Conclusion. The joint model approach was able to simultaneously characterize the glycemic profile and assess the risk of preterm birth and provided additional insights and a better model fit compared to the time-dependent Cox model.

Chronic glomerulonephritis and pregnancy: predictors of preterm birth

“…[10][11][12]. Неоднократно создавались прогностические модели ПР (чаще всего для общей популяции беременных, реже -для женщин с прегестационным сахарным диабетом), в том числе с применением искусственного интеллекта [13][14][15][16]. Однако мы не нашли в литературе моделей прогнозирования ПР у пациенток с ХГН.…”

Functional data analysis and prediction tools for continuous glucose-monitoring studies

“…Specifically, our group has recently published using self-glucose monitoring data to predict premature delivery in women with type 1 diabetes using a joint modelling approach. Using CGM data would most likely yield even better prediction to a very important obstetrical problem [37].…”

Does Birthweight Represent Imprinting for Life? Preliminary Findings from the Level and Timing of Diabetic Hyperglycemia in Utero: Transgenerational Effect on Adult Morbidity (TEAM) Study

“…What was surprising was the lack of association with maternal glycemic control during pregnancy. However, this will be further addressed when we have a larger sample size and are able to apply our methods of semiparametric regression and joint modelling using the glucose concentrations from self-glucose monitoring collected over gestation in these mothers, rather than the HbA 1 over pregnancy [20][21][22].…”