First-trimester prediction of preeclampsia: Validation of screening algorithms in a Russian population


Дәйексөз келтіру

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Рұқсат жабық Рұқсат ақылы немесе тек жазылушылар үшін

Аннотация

Objective. To make an external evaluation of the effectiveness of three published algorithms in the first-trimester prediction of preeclampsia (PE) in a prospective cohort study of a Russian population. Material and methods. Clinical and anamnestic factors, echographicparameters (uterine artery pulsatility index), and biochemical markers (PAPP-A, PlGF, and sFlt-1) during singleton pregnancies were prospectively analyzed during combined screening at 11-13 weeks’ gestation. After receiving information on pregnancy outcomes, the risks for early-onset (<34 weeks) or late-onset (≥34 weeks) PE were calculated using the algorithms proposed by Poon (2009), Parra-Cordero (2013), and Crovetto (2015). The effectiveness of predictive algorithms was compared with that presented in the original publications and evaluated for factors explaining the prediction differences. Results. Three algorithms for the prediction of both early- and late-onset PE were examined in 4000pregnant women. Labor was induced in 29 (0.7%) women before 34 weeks’ pregnancy due to early-onset PE and in 73 (1.8%) women after 34 weeks’ gestation due to late-onset PE. Gestational hypertension was diagnosed in 119 (2.9%) cases. The area under the ROC curve for early-onset PE tests was 0.79 (0.76-0.81), 0.69 (0.66-0.72), and 0.75 (0.72-0.78) and that for late-onset PE tests was 0.74 (0.71-0.77), 0.65 (0.62-0.68), and 0.66 (0.630.69). The sensitivity of tests for early-onset PE ranged from 31.6% to 57.8%; that of tests for late-onset PE was 25.5 to 37.2% with an overall false-positive rate of 10%. Conclusion. External validation of algorithms in the first-trimester prediction of PE in a Russian population has demonstrated the high negative predictive value of the sensitivity that is lower than stated in the original studies and, in general, the moderate effectiveness in the prediction of early-onset PE. There is a need for further investigations to determine the most optimal algorithms for predicting PE.

Толық мәтін

Рұқсат жабық

Авторлар туралы

A. Kholin

Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia

Email: a_kholin@oparina4.ru
M.D., Associate Researcher, Department of Maternal Fetal Medicine

Kamilla Muminova

Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia

Email: k_mimonova@oparina4.ru
M.D. Associate Researcher

I. Balashov

Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia

Email: k_mimonova@oparina4.ru
M.D., Associate Reseracher, Laboratory of Bioinformatics

Z. Khodzhaeva

Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia

Email: z_khodzhaeva@oparina4.ru
M.D., Ph.D., Professor, Head of Department, Department of Maternal-Fetal Medicine

P. Borovikov

Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia

Email: p_borovikov@oparina4.ru
Head of Department, Laboratory of Bioinformatics

T. Ivanets

Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia

Email: t_ivanets@oparina4.ru
M.D., Head of Scientific and Diagnostic Laboratory

A. Gus

Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia

Email: a_gus@oparina4.ru
M.D., Ph.D., Head of Ultrasound Department

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