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. 2023 Dec;32(12):1350-1359.
doi: 10.1002/pds.5665. Epub 2023 Jul 17.

Achieving comparability in glycemic control between antidiabetic treatment strategies in pregnancy when using real world data

Affiliations

Achieving comparability in glycemic control between antidiabetic treatment strategies in pregnancy when using real world data

Carolyn E Cesta et al. Pharmacoepidemiol Drug Saf. 2023 Dec.

Abstract

Purpose: Healthcare utilization databases often lack information on glycemic control, a key confounder when studying the safety of antidiabetic treatments, since patients with worse control are channeled to second-line agents, in particular insulin, versus first-line agents such as metformin. We evaluated whether adjustment for measured characteristics attains balance in glycemic control when comparing antidiabetic treatment strategies in pregnant women with pregestational type 2 diabetes (T2DM).

Methods: In a US insurance claims database, we identified 3360 women with T2DM pregnant between 2004 and 2015, of whom a subset of 996 had data on hemoglobin A1c (HbA1c ) levels. We selected insulin only as the comparator group and used propensity score (PS)-matching on comorbidities and proxies of diabetes severity, but not on HbA1c , to adjust for confounding. We used standardized differences (st.diff) to assess balance in claims-based covariates and mean HbA1c (% ± SD) in the subset.

Results: There were imbalances in claims-based covariates before PS-matching, with smaller differences when both treatment strategies included insulin. After PS-matching, balance was achieved in most claims-based covariates (st.diff <0.1). Mean HbA1c was similar before and after PS-matching when both treatments included insulin (e.g., 7.1 ± 1.5 vs. 7.7 ± 1.8 and 7.1 ± 1.5 vs. 7.5 ± 1.7, respectively, for metformin + insulin vs. insulin only). Differences in mean HbA1c remained after PS-matching when non-insulin treatments were compared to treatments including insulin (e.g., 6.3 ± 1.1 vs. 7.6 ± 1.7 for metformin only vs. insulin only).

Conclusions: Balance in both claims-based characteristics and glycemic control was attained after restricting the population to women with T2DM and comparing treatment strategies indicated for patients with similar diabetes severity. When comparing treatment strategies with versus without insulin, differences in glycemic control persisted after PS-matching even when balance was attained for other measured characteristics.

Keywords: antidiabetic medication; channeling bias; confounding; glycemic control; pregnancy; real world data; type 2 diabetes mellitus.

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Conflict of interest statement

Conflict of interest disclosure: SHD reports being an investigator on grants to her institution from Takeda for unrelated studies; receiving personal fees from J&J and Moderna outside the submitted work; and having served as an epidemiologist with the North America AED pregnancy registry, which is funded by multiple companies. BTB reports receiving research grants to Brigham and Women’s Hospital from Eli Lilly, Baxalta, and Pacira for unrelated studies; receiving personal fees from Aetion and from Alosa Foundation outside the submitted work; and has served on an expert panel for a postpartum hemorrhage quality improvement project that was conducted by the Association of Women’s Health, Obstetric, and Neonatal Nurses and funded by a grant from Merck for Mothers. KFH reports being an investigator on research grants to Brigham and Women’s Hospital from Takeda and UCB for unrelated studies; and receiving personal fees from Syneos Health outside the submitted work. EP reports being an investigator of a research grant to the Brigham and Women’s Hospital from Boehringer Ingelheim, not related to the topic of the submitted work. CEC, SV, and EWS report no conflicts of interest.

Figures

Figure 1:
Figure 1:
Flowchart of study population derivation LMP: estimated start of pregnancy, date of last menstrual period
Figure 2:
Figure 2:
Covariate balance before and after propensity score matching for selected baseline claims-based characteristics in the study population of pregnant women with type 2 diabetes comparing first trimester metformin treatment, alone or with insulin, versus insulin only * including nephropathy, retinopathy, neuropathy, diabetic foot, diabetes with other ophthalmic manifestations or retinal edema, chronic kidney disease (Stage III or higher), other renal conditions. ** including diabetes with other specified or unspecified manifestations Covariate assessment periods: Healthcare utilization: LMP-180 to LMP-1 days; Lab test ordered, comorbidities, medications: LMP-180 to LMP+90 days. Abbreviations: ACE, angiotensin converting enzyme; St.diff, standardized difference; LMP, last menstrual period; PS, propensity score. PS-matching results for the complete list of covariates presented in Supplementary Table S7 and S8.
Figure 3:
Figure 3:
Covariate balance before and after propensity score matching for selected baseline claims-based characteristics in the study population of pregnant women with type 2 diabetes with pre-pregnancy metformin only treatment comparing second trimester metformin use, alone or with insulin, versus insulin only. Note: The post-matching study population contained no individuals with diabetic ketoacidosis in the first comparison, and no individuals with diabetic ketoacidosis or hypoglycemia in the second comparison. * including nephropathy, retinopathy, neuropathy, diabetic foot, diabetes with other ophthalmic manifestations or retinal edema, chronic kidney disease (Stage III or higher), other renal conditions. ** including diabetes with other specified or unspecified manifestations Covariate assessment periods: Healthcare utilization: LMP-180 to LMP-1 days; Lab test ordered, comorbidities, medications: LMP-180 to LMP+90 days. Abbreviations: ACE, angiotensin converting enzyme; St.diff, standardized difference; LMP, last menstrual period; PS, propensity score. PS-matching results for the complete list of covariates presented in Supplementary Table S13 and S14.

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