Predictive calculator of the risk of perinatal complications in women with pregestational diabetes mellitus

Abstract

Background. The prevalence of impaired carbohydrate metabolism among women of reproductive age is increasing worldwide. Despite tremendous progress in the treatment and management of blood glucose levels, pregnancy in women with pregestational diabetes still carries risks for the fetus.

This study aims to develop a calculator for predicting perinatal complications in women with pregestational diabetes mellitus by mathematical modeling.

Materials and Methods. This observational analytical study with a case-control design was conducted at the Altai Regional Clinical Perinatal Center “DAR” (Barnaul). The study included 147 women, with the main group comprising 95 pregnant women, including 47 with type 1 diabetes mellitus (group 1A) and 48 with type 2 diabetes mellitus (group 1B). No carbohydrate metabolism disorders were detected in 52 patients of the control group. All patients in the main group received insulin therapy. Medical documentation was analyzed, and statistical processing of the data was performed using mathematical modeling methods with appropriate software.

Results. In order to predict the combined indicator of perinatal complications, logistic regression analysis was used to calculate coefficients (b) for each of the indicators that have the most significant influence on the formation of complications.

The calculated values of regression coefficients can be utilized to predict the risk of perinatal complications in women with type 1 diabetes mellitus. For more practical use, a calculator for assessing the risk of perinatal complications in type 1 and type 2 diabetes mellitus was created using a computer program.

Diagnostic evaluation of the prognostic scale (calculator) for assessing perinatal complications risk assessment in type 2 diabetes mellitus demonstrated a sensitivity of 97.6%, specificity of 87.5%, and a prognostic value of positive risk assessment of 97.5%. Therefore, the calculator enables the prediction of the risk of perinatal complications in 97.5% of cases. At the same time, the prognostic scale of perinatal complications risk and the Perinatal Complications Risk Calculator for type 1 diabetes mellitus created on its basis showed 100% sensitivity and specificity.

Conclusion. The frequency of perinatal complications remains high, so the creation of a sufficiently effective prognostic model will make it possible to predict perinatal complications and influence the tactics of management of pregnant women and their newborns.

Keywords

pregestational diabetes mellitus / hyperglycemia / diabetic fetopathy

References

[1] Kapustin RV, Kopteeva EV, Alexeenkova EN, Tsybuk EM, Arzhanova ON. Analysis of Risk Factors and Perinatal Mortality Structure in Pregnant Patients with Diabetes Mellitus. Doctor.Ru. 2021;20(6):46–52. (In Russ). doi: 10.31550/1727-2378-2021-20-6-46-52

[2] Капустин Р.В., Коптеева Е.В., Алексеенкова Е.Н., Цыбук Е.М., Аржанова О.Н. Анализ факторов риска и структуры перинатальных потерь у беременных с сахарным диабетом // Доктор.Ру. 2021. Т. 20, № 6. С. 46–52. doi: 10.31550/1727-2378-2021-20-6-46-52

[3] Desoye G, Ringholm L, Damm P, Mathiesen ER, van Poppel MNM. Secular trend for increasing birthweight in offspring of pregnant women with type 1 diabetes: is improved placentation the reason? Diabetologia. 2023;66(1):33–43. doi: 10.1007/s00125-022-05820-4

[4] Desoye G., Ringholm L. Damm P., Mathiesen E.R., van Poppel M.N.M. Secular trend for increasing birthweight in offspring of pregnant women with type 1 diabetes: is improved placentation the reason? // Diabetologia. 2023. Vol. 66, N. 1. P. 33–43. doi: 10.1007/s00125-022-05820-4

[5] Cleary EM, Thung SF, Buschur EO. Pregestational Diabetes Mellitus. Feingold KR, Anawalt B, Blackman MR, et al., editors. 2021. In: Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000. [cited 2023 Jul 24]. Available from: https://pubmed.ncbi.nlm.nih.gov/34370429/

[6] Cleary E.M., Thung S.F., Buschur E.O. Pregestational Diabetes Mellitus. Feingold K.R., Anawalt B., Blackman M.R., et al., editors. 2021. In: Endotext [Internet]. South Dartmouth (MA) : MDText.com, Inc., 2000. Дата обращения: 24.07.2023. Доступ по ссылке: https://pubmed.ncbi.nlm.nih.gov/34370429/

[7] Prylutskaya VA, Sukalo AV. Predictive models for determining the likelihood of early neonatal hypoglycemia in children born to mothers with type 1 diabetes. Practical medicine. 2022;20(1):93–99. (In Russ). doi: 10.32000/2072-1757-2022-1-93-99

[8] Прилуцкая В.А., Сукало А.В. Прогностические модели для определения вероятности ранней неонатальной гипогликемии у детей, рождённых матерями с сахарным диабетом 1 типа // Практическая медицина. 2022. Т. 20, № 1. С. 93–99. doi: 10.32000/2072-1757-2022-1-93-99

[9] Kapustin RV, Kopteyeva EV, Tral TG, Tolibova GKh. Placental morphology in different types of diabetes mellitus. Journal of Obstetrics and Women’s Diseases. 2021;70(2):13–26. (In Russ). doi: 10.17816/JOWD57149

[10] Капустин Р.В., Коптеева Е.В., Траль Т.Г., Толибова Г.Х. Морфологическое строение плаценты при различных типах сахарного диабета // Журнал акушерства и женских болезней. 2021. Т. 70, № 2. С. 13–26. doi: 10.17816/JOWD57149

[11] Seah JM, Kam NM, Wong L, et al. Risk factors for pregnancy outcomes in Type 1 and Type 2 diabetes. Intern Med J. 2021;51(1):78–86. doi: 10.1111/imj.14840

[12] Seah J.M., Kam N.M., Wong L., et al. Risk factors for pregnancy outcomes in Type 1 and Type 2 diabetes // Intern Med J. 2021. Vol. 51, N. 1. P. 78–86. doi: 10.1111/imj.14840

[13] Timsit J, Ciangura C, Dubois-Laforgue D, Saint-Martin C, Bellanne-Chantelot C. Pregnancy in Women With Monogenic Diabetes due to Pathogenic Variants of the Glucokinase Gene: Lessons and Challenges. Front Endocrinol (Lausanne). 2022;12:802423. doi: 10.3389/fendo.2021.802423

[14] Timsit J., Ciangura C., Dubois-Laforgue D., Saint-Martin C., Bellanne-Chantelot C. Pregnancy in Women With Monogenic Diabetes due to Pathogenic Variants of the Glucokinase Gene: Lessons and Challenges // Front Endocrinol (Lausanne). 2022. Vol. 12. P. 802423. doi: 10.3389/fendo.2021.802423

[15] Pylypjuk CL, Day C, ElSalakawy Y, Reid GJ. The Significance of Exposure to Pregestational Type 2 Diabetes in Utero on Fetal Renal Size and Subcutaneous Fat Thickness. Int J Nephrol. 2022;2022:3573963. doi: 10.1155/2022/3573963

[16] Pylypjuk C.L., Day C., ElSalakawy Y., Reid G.J. The Significance of Exposure to Pregestational Type 2 Diabetes in Utero on Fetal Renal Size and Subcutaneous Fat Thickness // Int J Nephrol. 2022. Vol. 2022. P. 3573963. doi: 10.1155/2022/3573963

[17] Shingu KF, Waguri M, Takahara M, Katakami N, Shimomura I. Trends in maternal characteristics and perinatal outcomes among Japanese pregnant women with type 1 and type 2 diabetes from 1982 to 2020. J Diabetes Investig. 2022;13(10):1761–1770. doi: 10.1111/jdi.13841

[18] Shingu K.F., Waguri M., Takahara M., Katakami N., Shimomura I. Trends in maternal characteristics and perinatal outcomes among Japanese pregnant women with type 1 and type 2 diabetes from 1982 to 2020 // J Diabetes Investig. 2022. Vol. 13, N. 10. P. 1761–1770. doi: 10.1111/jdi.13841

[19] Chen ZY, Mao SF, Guo LH, et al. Effect of maternal pregestational diabetes mellitus on congenital heart diseases. World J Pediatr. 2023;19(4):303–314. doi: 10.1007/s12519-022-00582-w

[20] Chen Z.Y., Mao S.F., Guo L.H., et al. Effect of maternal pregestational diabetes mellitus on congenital heart diseases // World J Pediatr. 2023. Vol. 19, N. 4. P. 303–314. doi: 10.1007/s12519-022-00582-w

[21] Trukhacheva NV. Mathematical statistics in biomedical research using the Statistica package. Moscow: GEOTAR-Media; 2012. (In Russ).

[22] Трухачёва Н.В. Математическая статистика в медико-биологических исследованиях с применением пакета Statistica. Москва : ГЭОТАР-Медиа, 2012.

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