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Postoev V.A.

Northern State Medical University

Usynina A.A.

Northern State Medical University

Grjibovski A.M.

Northern State Medical University

Menshikova L.I.

Northern State Medical University;
Russian Medical Academy of Continuous Professional Education

Son I.M.

Russian Medical Academy of Continuous Professional Education

Comparison of models for prediction of spontaneous preterm birth

Authors:

Postoev V.A., Usynina A.A., Grjibovski A.M., Menshikova L.I., Son I.M.

More about the authors

Journal: Medical Technologies. Assessment and Choice. 2024;46(4): 10‑19

DOI: 10.17116/medtech20244604110

Read: 1406 times

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Table of contents

COMPARISON OF MODELS FOR PREDICTION OF SPONTANEOUS PRETERM BIRTH
COMPARISON OF MODELS FOR PREDICTION OF SPONTANEOUS PRETERM BIRTH

To cite this article:

Postoev VA, Usynina AA, Grjibovski AM, Menshikova LI, Son IM. Comparison of models for prediction of spontaneous preterm birth. Medical Technologies. Assessment and Choice. 2024;46(4):10‑19. (In Russ.)
https://doi.org/10.17116/medtech20244604110

Подписка 2026 Медицинские технологии. Оценка и выбор (Medical Technologies. Assessment and Choice)
МСФ на ЯМ
Использование инфографики авторами научных работ
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Abstract:

OBJECTIVE

To compare the prognostic models for spontaneous preterm birth.

MATERIAL AND METHODS

A retrospective cohort study was based on the Arkhangelsk County Birth Registry data. We analyzed 50581 spontaneous births in 2012—2018. We developed 6 models (three each for logistic regression and classification trees): for prediction of preterm birth at pregnancy planning stage, at the first antenatal visit and at the beginning of the second trimester of pregnancy (20—22 weeks of gestation).

RESULTS

In 2012—2018, there were 2167 spontaneous preterm births (4.28%). The outcomes of previous pregnancies, some preexisting diseases (diabetes mellitus, epilepsy, obesity), behavioral factors and characteristics of the current pregnancy (multiple pregnancy, isthmic-cervical insufficiency, threat of abortion) were significant for prediction. Depending on predictors, AUC for regression models and models based on classification trees varied from 0.623 (95%CI: 0.610—0.636) to 0.703 (95%CI: 0.691—0.715) and from 0.561 (95%CI: 0.552—0.571) to 0.620 (95%CI: 0.608—0.632), respectively.

CONCLUSION

Binary logistic regression had higher predictive performance compared to classification trees for prediction of spontaneous preterm delivery. Maximum prognostic significance was achieved after inclusion of factors of data on anamnesis, lifestyle and health of the pregnant woman, as well as peculiarities of the current pregnancy.

Keywords:

preterm birth
prediction
logistic regression
decision trees
machine learning
birth registry
risk factors

Authors:

Postoev V.A.

Northern State Medical University

Usynina A.A.

Northern State Medical University

Grjibovski A.M.

Northern State Medical University

Menshikova L.I.

Northern State Medical University;
Russian Medical Academy of Continuous Professional Education

Son I.M.

Russian Medical Academy of Continuous Professional Education

Received:

28.04.2024

Accepted:

17.06.2024

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