Online ISSN: 3007-0244,
Print ISSN:  2410-4280
THE MODERN POSSIBILITIES OF PREDICTION OF PRE-ECLAMPSIA AND ITS COMPLICATIONS. A LITERATURE REVIEW

Background: One of the main causes of maternal and perinatal morbidity and mortality around the world is hypertensive disorder during pregnancy. After diagnosis of severe preeclampsia it is necessary of accurate risk assessment and management for both the mother and the fetus at different times. The prognostic model is an alternative basis for clinical practice, for predicting patient's future outcomes and for making decisions to improve them.

The aim: analysis of literature data of models for the prediction of pre-eclampsia and its complications.

Materials and methods. During the article's preparation an analysis was conducted of 55 English and Russian publications from PubMed, Clinical Key, Web of Science Core Collection, eLibrary, Google Scholar databases for the last 10 years, from January 2009 to June. Inclusion criteria: publications that contained a forecasting tool (model) containing three or more variables and that provided a probability of outcome, or suggested diagnostic or therapeutic actions, used discrimination and/or calibration to describe the performance of the forecasting model, internal and external validation of the model. The following search queries were used for the search: "preeclampsia and prognosis", "preeclampsia and complications" "EPH AND probability learning", "Hypertension-Edema-Proteinuria Gestosis AND prediction", "prediction of preeclampsia", "predictors of gestosis", "complications of preeclampsia".

Results. The research publications contained models for early prediction of preeclampsia and adverse maternal and perinatal outcomes. This literature review helped to find problems with predicting adverse perinatal outcome in preeclampsia, all existing models have one important limitation for generalization-they are all designed for single pregnancy, there is a relative high cost of models, and there was no information about the impact of the model on clinical practice: the number of days of treatment, the number of unnecessary diagnostic and therapeutic measures, complications.

Conclusions. due to the presence of systematic failure and limitations in the studies found, as well as the lack of adequate external validation checks, the reliability and reliability of existing models for predicting preeclampsia and its complications are very questionable.

 

Gulnara T. Nurgaliyeva 1, https://orcid.org/0000-0002-2161-105X

Gulyash A. Tanysheva 2, http://orcid.org/0000-0002-9074-6302

Gulshat K. Manabaeva 1, https://orcid.org/0000-0002-8217-7680

 

1 Department of obstetrics and gynecology,

2 Department of internship for obstetrics and gynecology,

Semey State Medical University,

Semey, Republic of Kazakhstan

1.         Буштырева И.О., Курочка М.П., Гайда О.В. Прогностические критерии преэклампсии // Российский вестник Акушера-гинеколога. 2017. №4. С. 59–63.

2.         Долгушина В.Ф., Сюндюкова Е.Г., Чулков В.С. Оценка значимости клинической прогностической модели PIERS для прогнозирования неблагоприятных исходов беременности при преэклампсии // Журнал Международной Научной школы “Парадигма”. 2015. №7. С. 100–105.

3.         Костенко И.В., Оленко Е.С., Кодочигова А.И., Сушкова Н.В., Субботина В.Г., Делиникайтис Е.Г. Возможность развития преэклампсии у клинически здоровых женщин // Вестник медицинского института “Реавиз”. 2017. №1. С. 73–78.

4.         Лахно И.В. Современные возможности прогнозирования и профилактики преэкламп-сии // Здоровье женщины. 2016. №7. С. 44–48.

5.         Лемешевская Т.В., Прибушеня О.В. Возможности раннего прогнозирования гестоза по маркерам комбинированного пренатального скрининга первого триместра беременности // Современные перинаталь-ные медицинские технологии в решении проблем демографической безопасности. 2015. №8. С. 85–90.

6.         Лемешевская Т.В., Прибушеня О.В. Прогнозирование преэклампсии при проведении расширенного комбинированного пренатального скрининга первого триместра беременности // Акушерство и гинекология. 2017. №12. С.52–59.

7.         Слободина А.В., Рудакова Е.Б., Резванцев М.В., Толкач Ю.В. Прогнозирование развития преэклампсии и степени ее тяжести у беременных пациенток при помощи математической модели, основанной на результатах оценки содержания регуляторных аутоантител в крови // Уральский медицинский журнал. 2013. № 8. С. 22–27.

8.         Сюндюкова Е.Г. Оценка эффективности анамнестической модели прогноза развития преэклампсии // Современные проблемы науки и образования. 2017. №2. С. 85–89.

9.         Abalos E., Cuesta C., Carroli G., Qureshi Z., Widmer M., Vogel J., Souza J. Pre-eclampsia, eclampsia and adverse maternal and perinatal outcomes: a secondary analysis of the World Health Organization multicountry survey on maternal and newborn health // BJOG : an international journal of obstetrics and gynecology. 2014. №121. Р.14–24.

10.     Agrawal S., Maitra N. Prediction of adverse maternal outcomes in preeclampsia using a risk prediction model // Journal of Obstetrics and Gynecology of India. 2016. № 66. Р.104–111.

11.     Akkermans J., Payne B., Von Dadelszen P., Groen H., Vries J., Magee L., Mol B., Ganzevoort W. Predicting complications in pre-eclampsia: external validation of the fullPIERS Model using the PETRA trial dataset // European Journal of Obstetrics Gynecology and Reproductive Biology. 2014. №179. Р.58–62.

12.     Akolekar R., Syngelaki A., Sarquis R., Zvanca M., Nicolaides, K. Prediction of early, intermediate and late pre-eclampsia from maternal factors, biophysical and biochemical markers at 11–13 Weeks // Prenatal diagnosis. 2011. №31(6). Р.66–74.

13.     Allen R., Aquilina J. Prospective observational study to determine the accuracy of first trimester serum biomarkers and uterine artery Dopplers in combination with maternal characteristics and arteriography for the prediction of women at risk of preeclampsia and other adverse // Matern Fetal Neonatal Med. 2017. № 16. Р.1–17.

14.     Almeida S., Katz L., Coutinho I., Amorim M. Validation of fullPIERS Model for prediction of adverse outcomes among women with severe pre-eclampsia // Int J Gynaecol Obstet. 2017. № 138(2). Р. 142-147.

15.     Andrietti S., Silva M., Wright A., Wright D., Nicolaedes K. Competing-risks model in screening for pre-eclampsia by maternal factors and biomarkers at 35-37 weeks’ gestation // Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology. 2016. №48. Р. 72–79.

16.     Antwi E., Groenwold R., Browne J., Franx A., Agyepong I., Kwadwo K., Klipstein-Grobush K., Grobbee D. Development and validation of a Prediction Model for gestational hypertension in a Ghanaian Cohort // BMJ Open. 2017 (7). e012670.

17.     Antwi E., Klipstein-Grobush K., Browne J., Schielen P., Koram K., Agyepong I., Grobbee D. Improved prediction of gestational hypertension by inclusion of placental growth factor and pregnancy associated plasma protein-a (PAPP-A) in a sample of Ghanaian women // Reproductive Health. 2018. №15.1 Р.1–10.

18.     Baschat A.A., Magder L.S., Doyle L.E., Atlas R.O., Jenkins C.B., Blitzer M.G. Prediction of Preeclampsia Utilizing the First Trimester Screening Examination // American Journal of Obstetrics and Gynecology. 2014.№ 211.5 Р 514–514.

19.     Chang Y., Chen X., Cui H., Li X., Xu Y. New Predictive Model at 11 +0 to 13 +6 Gestational Weeks for Early-Onset Preeclampsia With Fetal Growth Restriction // Reproductive Sciences. 2017. № 24 (5) Р. 783–789.

20.     Churchill D., Duley L., Thornton J.G., Jones L. Interventionist versus Expectant Care for Severe Pre-Eclampsia between 24 and 34 Weeks’ Gestation // The Cochrane database of systematic reviews. 2013 (7). Р. 1-44.

21.     Collins G.S., Reitsma J.B., Altman D.G., Moon K.G. Reporting guideline for prediction model studies : TRIPOD Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis // Annals of Internal Medicin.2015. (162.1) Р.55–63.

22.     Croft P., Altman D.G., Croft P., Deeks J.J., Dunn K.M., Hay A.D., Hemingway H., LeResche L., Peat G., Perel P., Petersen S.E., Riley R.D., Roberts I., Sharpe M., Stevens R.J., Van Der Windt D.A., Von Korff M., Timmis A. The science of clinical practice: disease diagnosis or patient prognosis? Evidence about ‘What is likely to happen’ should shape clinical practice // BMC Medicine. 2015. №13(1) Р.1–8.

23.     Crovetto F., Figueras F., Truinfo S., Crispi F., Rodriguez-Sureda V., Peguero A., Dominguez C., Gratacos E. Added value of angiogenic factors for the prediction of Early and Late Preeclampsia in the first trimester of pregnancy // Fetal Diagnosis and Therapy. 2014. №35(4). Р.258–266.

24.     Crovetto F., Figueras F., Truinfo S., Crispi F., Rodriguez-Sureda V., Dominguez C., Llurba E., Gratacos E. First trimester screening for early and late preeclampsia based on maternal characteristics, biophysical parameters, and angiogenic factors // Prenatal Diagnosi.2015. № 35(2). Р.183–191.

25.     Debray T.P., Damen J.A., Snell K.I., Ensor J., Hooft L., Reitsma J.B., Riley R.D., Moons K.G. A guide to systematic review and meta-analysis of prediction model performance // Bmj. 2017. № 356. Р. 60–64.

26.     Gallo D.M., Wright D., Casanova C., Campanero M., Nicolaides K.H. Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 19-24 weeks’ gestation // American Journal of Obstetrics and Gynecology.2016. №214(5).Р. 619е1–619.e17.

27.     García B., Llurba E., Valle L., Gómez-Roig, M., Juan M., Perez-Matos C., Fernandez M., Garcia-Hernandez J.A., Alijotas-Reig J., Higueras M.T., Calero I., Perez-Hoyos S., Carreras E., Cabero L. Do knowledge of Uterine Artery resistance in the second trimester and Targeted surveillance Improve maternal and Perinatal Outcome? UTOPIA Study: a randomized controlled trial // Ultrasound in Obstetrics and Gynecology. 2016. №47(6). Р.680–689.

28.     Giguère Y., Masse J., Theriault S., Bujold E., Lafond J., Rousseau F., Forest J. Screening for pre-eclampsia early in pregnancy: performance of a multivariable model combining clinical characteristics and biochemical markers // BJOG: An International Journal of Obstetrics and Gynaecology. 2015. №122(3). Р.402–410.

29.     Gillon T.E., Pels A., Von Dadelszen P., MacDonell K., Magee L.A. Hypertensive disorders of pregnancy: A Systematic Review of international clinical practice guidelines // PLoS ONE. 2014 № 9(12). Р.1–20.

30.     Goetzinger K.R., Tuuli M., Cahill A.G., Macones G., Odibo A. Development and validation of a risk factor scoring system for first-trimester prediction of pre-eclampsia // Am J Perinatol.2014.№ 31(12). Р.1049–1056.

31.     Gorman.N.O., Wright D., Poon L.C., Rolnik D.L., Sygelaki A., Akolekar R., Cicero S., Janga D., Jani J., Francisca S., Matallana C., Papantoniou N. Accuracy of competing risks model in screening for pre-eclampsia by maternal factors and biomarkers at 11–13 Weeks’ Gestation // Ultrasound in Obstetrics and Gynecology. 2017. №49(6). Р.751–755.

32.     Grigorios A., Karampas A., Makarios I., Konstantinos C. Prediction of pre-eclampsia combining NGAL and other biochemical markers with Doppler in the first and/or second trimester of pregnancy. A pilot study // Journal of Obstetrics & Gynecology and Reproductive Biology. 2016 №205. Р.153–157.

33.     Hadley E.E., Poole A., Herrera S.R., Bradley L., Dutta E., Sukhavasi N., Ayad M., Costantine M., Pacheco L., Jain S., Saade G. External validation of the fullPIERS (Preeclampsia Integrated Estimate of RiSk) Model // American Journal of Obstetrics and Gynecology. 2016 №214(1). Р.259–260

34.     Hemingway Harry., Croft P., Perel P., Hayden J.A., Abrams K., Timmis A., Briggs A., Udumyan R., Moon K., Steyerberg E.W., Roberts I., Schroter S., Altman D.G., Riley R.D., Brunner N., Hingorani A.D., Kyzas P., Malats N., Peat G., Sauerbrei W. Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research // BMJ (Online). 2013. Р.1–11.

35.     Hingorani A.D., Van Der Windt D.A., Riley R.D., Abrams K., Moons K., Steyerberg E.W., Schroter S., Sauerbrei W., Altman D.G., Hemingway H., Briggs A., Brunner N., Croft P., Hayden J., Kyzas P., Malats N., Peat G., Perel P., Roberts I., Timmis A. Prognosis Research Strategy (PROGRESS) 4: Stratified Medicine Research // BMJ (Online). 2013. № 346 (2). Р.1–9.

36.     Kuc S., Koster M., Franx A., Schielen P., Visser G. Maternal characteristics, mean arterial pressure and serum markers in early prediction of preeclampsia // PLoS ONE. 2013 №8(5).Р.1–8.

37.     Lobo G., Nowak P., Panigassi A., Lima A., Araujio J., Nardozza L., Pares D. Validation of Fetal Medicine Foundation Algorithm for prediction of pre-eclampsia in the first trimester in an unselected Brazilian population // Journal of Maternal-Fetal and Neonatal Medicine. 2017. №7058(9). Р.1–7.

38.     Macdonald-Wallis C., Siverwood R.J., De Stavola B.L., Inskip H., Cooper C., Godfrey K.M., Crozier S., Fraser A., Nelson S.M., Lawlor D.A., Tilling K. Antenatal blood pressure for prediction of pre-eclampsia, preterm birth, and small for gestational age babies: development and validation in two general population cohorts // BMJ (Online). 2015. № 351. Р.1-11.

39.     Menzies J., Dadelszen P. The PIERS (preeclampsia Integrated Estimate of Risk) Models: univariable and cluster analyses // Hypertension in pregnancy. 2009. №27. Р.620–626.

40.     Metcalfe A., Langlois S., Macfarlane J., Vallance H., Joseph K. Prediction of obstetrical risk using maternal serum markers and clinical risk factors // Prenatal Diagnosis.2014 № 34(2). Р.172–179.

41.     Moons K., Altman D., Vergouwe Y., Royston P. Prognosis and prognostic research: application and impact of Prognostic Models in clinical practice // BMJ. 2009. № 338(6). 336-342.

42.     Moons K., de Groot J., Bouwmeester W., Vergouwe Y., Mallett S., Altman D.G., Reitsma J.B., Collins G.S. Critical appraisal and data extraction for Systematic Reviews of prediction Modelling Studies: The CHARMS Checklist // PLoS Medicine. 2014. №11(10). Р.1–12.

43.     Murphy K.E., Hannah M.E., Willan A.R., Hewson S.A., Ohlsson A., Kelly E.N., Mattews S.G., Saigal S., Asztalos E., Ross S., Delisle M.F., Amankwah K., Guselle P., Gafni A., Lee S.K., Armson B.A. Multiple Courses of Antenatal Corticosteroids for Preterm Birth (MACS): A Randomised Controlled Trial // The Lancet. 2008. № 372(12). Р. 20-27.

44.     Myatt L., Clifton R., Roberts J., Spong C., Hauth J.C., Varner M.W., Thorp J.M., Peaceman A.M., Ramin S.M., Carpenter M.W., Iamd J.D., Sciscione A., Harper M., Tolosa J.E., Saade G., Sorokin Y., Anderson G. First-Trimester Prediction of Preeclampsia in Low-Risk Nulliparous Women // Obstetrics and gynecology. 2013 №119(6). Р.1234–1242.

45.     North R.A., McCowan L., Dekker G.A., Poston L., Chan E., Stewart A.W., Black M.A., Taylor R.S., Walker J.J., Baker P.N., Kenny L.C. Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in International prospective cohort // BMJ. 2011. № 342.Р. 1-11.

46.     Odibo A., Zhong Y., Goetzinger K., Odibo L., Bick J. First-trimester placental protein 13, PAPP-A ,uterine Artery Doppler and maternal characteristics in the prediction of preeclampsia // Placenta.2011. №32(8). Р.598–602.

47.     O’Gorman N., Wright D., Syngelaki A., Akolekar R., Wright A., Poon L.C., Nicolades K.H. Competing Risks Model in screening for preeclampsia by maternal factors and biomarkers at 11-13 weeks gestation // American Journal of Obstetrics and Gynecology. 2016. №214(1).Р.103–114.

48.     Palomaki G.E., Haddow J.E., Haddow H., Salahuddin S., Geahchan C., Cerderia A., Verlohren S., Horowitz G., Thadhani R., Karumanchi S., Rana S. Modeling Risk for severe adverse outcomes using angiogenic factor measurements in women with suspected preterm preeclampsia // Prenatal Diagnosis.2015.№ 35(4). Р.386–393.

49.     Payne B.A., Hutcheon J., Ansermino M., Hall D.R., Bhutta Z.A., Bhutta S.Z., Biryabarema C., Grobman W.A., Groen H., Haniff F., Li J., Magee L., Merialdi M., Nakimuli A.,Qu Z., Sikandar R., Sass N., Sawchuck D., Steyn D., Widmer M., Zhou J., von Dadelszen P., for the miniPIERS Study Working Group. A Risk Prediction Model for the assessment and triage of women with hypertensive disorders of pregnancy in low-resourced settings: The miniPIERS (Pre-Eclampsia Integrated Estimate of RiSk) Multi-Country Prospective Cohort Study // PLoS Medicine.2014 № 11(1).Р.1-13.

50.     Payne B.A., Hutcheon J., Dunsmuir D., Cloete G., Dumont G., Hall D., Lim J., Magee L., Sikandar R., Qureshi R., van Papendorp E., Ansermino M., von Dadelszen P. Assessing the incremental value of blood Oxygen Saturation (SpO2) in the miniPIERS (Pre-Eclampsia Integrated Estimate of RiSk) Risk Prediction Model // Journal of Obstetrics and Gynaecology Canada.2015. № 37(1). Р.16–24.

51.     Payne B.A., Groen H., Ukah U.V., Ansermino, J.M., Bhutta Z., Grobman W., Hall D., Hutcheon J.A., Magee L., von Dadelszen P. Development and internal validation of a multivariable Model to predict perinatal death in pregnancy hypertension // Pregnancy Hypertension. 2015 №5(4). Р.315–321.

52.     Poon L.C., Nicolaides K.H. Early prediction of preeclampsia // Obstetrics and Gynecology International.2014. Р.1-11.

53.     Poon L.C., Kametas N.A., Chelemen T., Leal A., Nicolaides K.H. Maternal risk factors for hypertensive disorders in pregnancy: a multivariate approach // Journal of Human Hypertension.2010. № 24(2). Р.104–110.

54.     Rath D. Schlembach W. Prediction of pre-eclampsia: claim, reality and clinical consequences // Geburtshilfe Neonatol. 2013. №8. Р.117–118.

55.     Riley R., Hayden J., Steyerberg E., Moon K., Abrams K., Kyzas P.A., Malats N., Briggs A., Shroter S., Altman D.G., Hemingway H. for the PROGRESS Group Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research // PLoS Medicine. 2013. №10(2).Р.1-9.

56.     Ruiter M., Kwee A., Naaktgeboren C.A., Louhanepessy R.L., Franx A., Moons K., Koster M.P. Group RESPECT study. External validation of Prognostic Models for preeclampsia in a large Dutch Multicentre Prospective Cohort // American Journal of Obstetrics and Gynecology.2016. №243 Р.83-112.

57.     Say L., Chou D., Gemmill A., Tuncalp O., Moller A.B., Daniels J., Gulmezoglu A.M., Temmerman M., Alkema L. Global causes of maternal death: a WHO Systematic Analysis // The Lancet Global Health. 2014 №2(6). Р.323–333.

58.     Scott H., Danel I. Accountability for improving maternal and newborn health // Best Practice & Research Clinical Obstetrics & Gynaecology. 2016. №36. Р.45–56.

59.     Scazzocchio E. Figueras F., Crispi F., Meler E., Masoller N., Mula R., Gratacos E. Performance of a first-trimester screening of preeclampsia in a routine care low-risk setting // American Journal of Obstetrics and Gynecology.2013. № 208(3). Р.203.e1–203.e10.

60.     Scazzocchio E., Crovetto F., Triunfo S., Gratacos E., Figueras F. Validation of a first-trimester screening model for pre-eclampsia in an unselected population // Ultrasound in Obstetrics and Gynecology.2017. №49(2). Р.188–193.

61.     Shetty A.K. Global maternal, hewborn, and child health: successes, challenges, and opportunities // Pediatric Clinics of North America. 2016 № 63(1). Р.1–18.

62.     Steyerberg E et al. “Prognosis Research Strategy (PROGRESS) Series 3: Prognostic Model Research.” PLoS Med 10.2 (2013): 1–9. Web.

63.     Thangaratinam S., Langenveld J., Mol B.W., Khan K.S. Prediction of complications in Early-onset Pre-Eclampsia (PREP): Development and External Multinational Validation of Prognostic Models // BMC Medicine. 2017. Р.1–11.

64.     Tsiakkas A., Saiid Y., Wright A., Wright D., Nicolaides K.H. Competing Risks Model in screening for preeclampsia by maternal factors and biomarkers at 30-34 weeks’ gestation // American Journal of Obstetrics and Gynecology. 2016. №215(1). Р. 87.e1–87.e17.

65.     Ukah V.U., Payne B., Hutcheon J.A., Ansermino J.M., Ganzevoort W., Thangaratinam S., Magee L.A., von Dadelzen P. Assessment of the fullPIERS Risk Prediction Model in women with early-onset preeclampsia // Hypertension.2018. № 71. Р.659-665.

66.     Ukah, V.U., Payne B., Lee T., Magee L.A., von Dadelzen P., for the fullPIERS an miniPIERS working groups. External validation of the fullPIERS Model for predicting adverse maternal outcomes in pregnancy Hypertension in Low- and Middle-Income Countries // Hypertension.2017. №69(4). Р.705–711.

67.     Vis, J.Y., Wilms F.F., Kuin R.A., Reuvers J.M., Stam M.C., Pattinaja D.A., Mol B.W. Time to delivery after the first course of antenatal corticosteroids: a cohort study // American Journal of Perinatology.2011.№ 28(9). Р.683–688.

68.     von Dadelszen P., Menzies J.M., Payne B., Magee L.A. Predicting adverse outcomes in women with severe pre-eclampsia // Seminars in Perinatology. 2009. №33(3). Р.152–157.

69.     von Dadelszen P., Payne B., Jing L., Ansermino J.M., Lee T.,Walker J.J., Walley K.R., Lee S.K., Russel J.A., Magee L.A. for the PIERS study group. Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS Model // The Lancet.2011 № 377. Р.219–227.

70.     Wortelboer E.J., Koster M., Cuckle H.S., Stoutenbeek P., Schielen, P.C., Visser G.H. First-trimester Placental Protein 13 and Placental Growth Factor: markers for identification of women destined to develop early-onset pre-eclampsia // BJOG: An International Journal of Obstetrics and Gynaecology. 2010. №117(11). Р.1384–1389.

71.     Wright D., Syngelaki A., Akolekar R., Poon L.C., Nicolaides K.H. Competing Risks Model in screening for preeclampsia by maternal characteristics and medical history // American Journal of Obstetrics and Gynecology.2015. № 213(1). Р.62.e1–62.e10.

72.     Wynants L., Collins G.S., Van Calster K. Key steps and common pitfalls in developing and validating Risk Models // BJOG: An International Journal of Obstetrics and Gynaecology. 2017. №124(3). Р.423–432.

73.     Yen T.W., Payne B., Qu.Z., Hutcheon J.A., Lee T., Magee L.A., Walters B.N., von Dadelszen P. Using clinical symptoms to predict adverse maternal and perinatal outcomes in women with preeclampsia: data from the PIERS (Pre-Eclampsia Integrated Estimate of RiSk) Study // Journal of Obstetrics and Gynaecology Canada.2011. № 33(8). Р.803–809.

74.     Zakiyah N., Postma M.J., Baker P.N., van Asselt A.D. Pre-eclampsia diagnosis and treatment options: a Review of published Economic Assessments // PharmacoEconomics. 2015 №33(10). Р.1069–1082.

References:

1.         Bushtyreva I.O., Kurochka M.P., Gaida O.V. Prognosticheskie kriterii preeklampsii [Prognostic criteria for pre-eclampsia] // Rossiiskii vestnik Akushera-ginekologa [Russian Bulletin of the Obstetrician-Gynecologist]. 2017. №4. pp. 59–63. [in Russian]

2.         Dolgushina V.F., Syundyukova E.G., Chulkov V.S. Otsenka znachimosti klinicheskoi prognosticheskoi modeli PIERS dlya prognozirovaniya neblagopriyatnykh iskhodov beremennosti pri preeklampsii [To assess the significance of the clinical prognostic model PIERS for predicting adverse pregnancy outcomes in preeclampsia] // Zhurnal Mezhdunarodnoi Nauchnoi shkoly “Paradigma” [Journal of the international scientific school “Paradigm”]. 2015. №7. pp. 100–105. [in Russian]

3.         Kostenko I.V., Olenko E.S., Kodochigova A.I., Sushkova N.V., Subbotina V.G., Delinikaitis E.G. Vozmozhnost' razvitiya preeklampsii u klinicheski zdorovykh zhenshchin [The possibility of developing preeclampsia in clinically healthy women] // Vestnik meditsinskogo instituta “Reaviz” [Bulletin of the medical Institute “Reaviz"].2017. №1. pp. 73–78. [in Russian]

4.         Lakhno I.V., Sovremennye vozmozhnosti prognozirovaniya i profilaktiki preeklampsii [The modern possibilities of prediction and prevention of pre-eclampsia] // Zdorov'e zhenshchiny. [Women's Health]. 2016. №7. pp. 44–48. [in Russian]

5.         Lemeshevskaya T.V., Pribushenya O.V. Vozmozhnosti rannego prognozirovaniya gestoza po markeram kombinirovannogo prenatal'nogo skrininga pervogo trimestra beremennosti [The possibility of early prediction of preeclampsia markers of combined prenatal screening in the first trimester of pregnancy] // Sovremennye perinatal'nye meditsinskie tekhnologii v reshenii problem demograficheskoi bezopasnosti [Contemporary perinatal medical technologies in solving problems of demographic security]. 2015. №8. pp. 85–90. [in Russian]

6.         Lemeshevskaya T.V., Pribushenya O.V. Prognozirovanie pre-eklampsii pri provedenii rasshirennogo kombinirovannogo prenatal'nogo skrininga pervogo trimestra beremennosti [Prediction of preeclampsia during extended first-trimester combined prenatal screening] // Akusherstvo i ginekologiya [Obstetrics and gynecology]. 2017. №12. pp.52–59. [in Russian]

7.         Slobodina A.V., Rudakova E.B., Rezvantsev M.V., Tolkach Yu.V. Prognozirovanie razvitiya preeklampsii i stepeni ee tyazhesti u beremennykh patsientok pri pomoshchi matematicheskoi modeli, osnovannoi na rezul'tatakh otsenki soderzhaniya regulyatornykh autoantitel v krovi [Prognosis of the pre-eclampsia development and its severity in pregnant woman using a mathematic model based on the blood level of the regulatory auto-antibodies] // Ural'skii meditsinskii zhurnal [Ural medical journal]. 2013. № 8. pp. 22–27. [in Russian]

8.         Syundyukova E. G. Otsenka effektivnosti anamnesticheskoi modeli prognoza razvitiya preeklampsii [Performance assessment of the anamnestic model of the development forecast of preeclampsia] // Sovremennye problemy nauki i obrazovaniya [Modern problems of science and education]. 2017. №2. pp.85–89.[in Russian]

9.         Abalos E., Cuesta C., Carroli G., Qureshi Z., Widmer M., Vogel J., Souza J. Pre-eclampsia, eclampsia and adverse maternal and perinatal outcomes: a secondary analysis of the World Health Organization multicountry survey on maternal and newborn health // BJOG : an international journal of obstetrics and gynecology . 2014. №121. Р.14–24.

10.     Agrawal S., Maitra N. Prediction of adverse maternal outcomes in preeclampsia using a risk prediction model // Journal of Obstetrics and Gynecology of India. 2016. № 66. Р.104–111.

11.     Akkermans J., Payne B., Von Dadelszen P., Groen H., Vries J., Magee L., Mol B., Ganzevoort W. Predicting complications in pre-eclampsia: external validation of the fullPIERS Model using the PETRA trial dataset // European Journal of Obstetrics Gynecology and Reproductive Biology. 2014. №179. Р.58–62.

12.     Akolekar R., Syngelaki A., Sarquis R., Zvanca M., Nicolaides, K. Prediction of early, intermediate and late pre-eclampsia from maternal factors, biophysical and biochemical markers at 11–13 Weeks // Prenatal diagnosis. 2011. №31(6). Р.66–74.

13.     Allen R., Aquilina J. Prospective observational study to determine the accuracy of first trimester serum biomarkers and uterine artery Dopplers in combination with maternal characteristics and arteriography for the prediction of women at risk of preeclampsia and other adverse // Matern Fetal Neonatal Med. 2017. № 16. Р.1–17.

14.     Almeida S., Katz L., Coutinho I., Amorim M. Validation of fullPIERS Model for prediction of adverse outcomes among women with severe pre-eclampsia // Int J Gynaecol Obstet. 2017. № 138(2). Р. 142-147.

15.     Andrietti S., Silva M., Wright A., Wright D., Nicolaedes K. Competing-risks model in screening for pre-eclampsia by maternal factors and biomarkers at 35-37 weeks’ gestation // Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology. 2016. №48. Р. 72–79.

16.     Antwi E., Groenwold R., Browne J., Franx A., Agyepong I., Kwadwo K., Klipstein-Grobush K., Grobbee D. Development and validation of a Prediction Model for gestational hypertension in a Ghanaian Cohort // BMJ Open. 2017 (7). e012670.

17.     Antwi E., Klipstein-Grobush K., Browne J., Schielen P., Koram K., Agyepong I., Grobbee D. Improved prediction of gestational hypertension by inclusion of placental growth factor and pregnancy associated plasma protein-a (PAPP-A) in a sample of Ghanaian women // Reproductive Health. 2018. №15.1 Р.1–10.

18.     Baschat A.A., Magder L.S., Doyle L.E., Atlas R.O., Jenkins C.B., Blitzer M.G. Prediction of Preeclampsia Utilizing the First Trimester Screening Examination // American Journal of Obstetrics and Gynecology. 2014. № 211.5 Р 514–514.

19.     Chang Y., Chen X., Cui H., Li X., Xu Y. New Predictive Model at 11 +0 to 13 +6 Gestational Weeks for Early-Onset Preeclampsia With Fetal Growth Restriction // Reproductive Sciences. 2017. № 24 (5) Р. 783–789.

20.     Churchill D., Duley L., Thornton J.G., Jones L. Interventionist versus Expectant Care for Severe Pre-Eclampsia between 24 and 34 Weeks’ Gestation // The Cochrane database of systematic reviews. 2013 (7). Р. 1-44.

21.     Collins G.S., Reitsma J.B., Altman D.G., Moon K.G. Reporting guideline for prediction model studies : TRIPOD Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis // Annals of Internal Medicin.2015. (162.1) Р.55–63.

22.     Croft P., Altman D.G., Croft P., Deeks J.J., Dunn K.M., Hay A.D., Hemingway H., LeResche L., Peat G., Perel P., Petersen S.E., Riley R.D., Roberts I., Sharpe M., Stevens R.J., Van Der Windt D.A., Von Korff M., Timmis A. The science of clinical practice: disease diagnosis or patient prognosis? Evidence about ‘What is likely to happen’ should shape clinical practice // BMC Medicine. 2015. №13(1) Р.1–8.

23.     Crovetto F., Figueras F., Truinfo S., Crispi F., Rodriguez-Sureda V., Peguero A., Dominguez C., Gratacos E. Added value of angiogenic factors for the prediction of Early and Late Preeclampsia in the first trimester of pregnancy // Fetal Diagnosis and Therapy. 2014. №35(4). Р.258–266.

24.     Crovetto F., Figueras F., Truinfo S., Crispi F., Rodriguez-Sureda V., Dominguez C., Llurba E., Gratacos E. First trimester screening for early and late preeclampsia based on maternal characteristics, biophysical parameters, and angiogenic factors // Prenatal Diagnosi. 2015. № 35(2). Р.183–191.

25.     Debray T.P., Damen J.A., Snell K.I., Ensor J., Hooft L., Reitsma J.B., Riley R.D., Moons K.G. A guide to systematic review and meta-analysis of prediction model performance // Bmj. 2017. № 356. Р. 60–64.

26.     Gallo D.M., Wright D., Casanova C., Campanero M., Nicolaides K.H. Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 19-24 weeks’ gestation // American Journal of Obstetrics and Gynecology. 2016. №214(5).Р. 619е1–619.e17.

27.     García B., Llurba E., Valle L., Gómez-Roig, M., Juan M., Perez-Matos C., Fernandez M., Garcia-Hernandez J.A., Alijotas-Reig J., Higueras M.T., Calero I., Perez-Hoyos S., Carreras E., Cabero L. Do knowledge of Uterine Artery resistance in the second trimester and Targeted surveillance Improve maternal and Perinatal Outcome? UTOPIA Study: a randomized controlled trial // Ultrasound in Obstetrics and Gynecology. 2016. №47(6). Р.680–689.

28.     Giguère Y., Masse J., Theriault S., Bujold E., Lafond J., Rousseau F., Forest J. Screening for pre-eclampsia early in pregnancy: performance of a multivariable model combining clinical characteristics and biochemical markers // BJOG: An International Journal of Obstetrics and Gynaecology. 2015. №122(3). Р.402–410.

29.     Gillon T.E., Pels A., Von Dadelszen P., MacDonell K., Magee L.A. Hypertensive disorders of pregnancy: A Systematic Review of international clinical practice guidelines // PLoS ONE. 2014 № 9(12). Р.1–20.

30.     Goetzinger K.R., Tuuli M., Cahill A.G., Macones G., Odibo A. Development and validation of a risk factor scoring system for first-trimester prediction of pre-eclampsia // Am J Perinatol. 2014. № 31(12). Р.1049–1056.

31.     Gorman.N.O., Wright D., Poon L.C., Rolnik D.L., Sygelaki A., Akolekar R., Cicero S., Janga D., Jani J., Francisca S., Matallana C., Papantoniou N. Accuracy of competing risks model in screening for pre-eclampsia by maternal factors and biomarkers at 11–13 Weeks’ Gestation // Ultrasound in Obstetrics and Gynecology. 2017. №49(6). Р.751–755.

32.     Grigorios A., Karampas A., Makarios I., Konstantinos C. Prediction of pre-eclampsia combining NGAL and other biochemical markers with Doppler in the first and/or second trimester of pregnancy. A pilot study // Journal of Obstetrics & Gynecology and Reproductive Biology. 2016 №205. Р.153–157.

33.     Hadley E.E., Poole A., Herrera S.R., Bradley L., Dutta E., Sukhavasi N., Ayad M., Costantine M., Pacheco L., Jain S., Saade G. External validation of the fullPIERS (Preeclampsia Integrated Estimate of RiSk) Model // American Journal of Obstetrics and Gynecology. 2016 №214(1). Р.259–260

34.     Hemingway Harry., Croft P., Perel P., Hayden J.A., Abrams K., Timmis A., Briggs A., Udumyan R., Moon K., Steyerberg E.W., Roberts I., Schroter S., Altman D.G., Riley R.D., Brunner N., Hingorani A.D., Kyzas P., Malats N., Peat G., Sauerbrei W. Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research // BMJ (Online). 2013. Р.1–11.

35.     Hingorani A.D., Van Der Windt D.A., Riley R.D., Abrams K., Moons K., Steyerberg E.W., Schroter S., Sauerbrei W., Altman D.G., Hemingway H., Briggs A., Brunner N., Croft P., Hayden J., Kyzas P., Malats N., Peat G., Perel P., Roberts I., Timmis A. Prognosis Research Strategy (PROGRESS) 4: Stratified Medicine Research // BMJ (Online). 2013. № 346 (2). Р.1–9.

36.     Kuc S., Koster M., Franx A., Schielen P., Visser G. Maternal characteristics, mean arterial pressure and serum markers in early prediction of preeclampsia // PLoS ONE. 2013 №8(5). Р.1–8.

37.     Lobo G., Nowak P., Panigassi A., Lima A., Araujio J., Nardozza L., Pares D. Validation of Fetal Medicine Foundation Algorithm for prediction of pre-eclampsia in the first trimester in an unselected Brazilian population // Journal of Maternal-Fetal and Neonatal Medicine. 2017. №7058(9). Р.1–7.

38.     Macdonald-Wallis C., Siverwood R.J., De Stavola B.L., Inskip H., Cooper C., Godfrey K.M., Crozier S., Fraser A., Nelson S.M., Lawlor D.A., Tilling K. Antenatal blood pressure for prediction of pre-eclampsia, preterm birth, and small for gestational age babies: development and validation in two general population cohorts // BMJ (Online). 2015. № 351. Р.1-11.

39.     Menzies J., Dadelszen P. The PIERS (preeclampsia Integrated Estimate of Risk) Models: univariable and cluster analyses // Hypertension in pregnancy. 2009. №27. Р.620–626.

40.     Metcalfe A., Langlois S., Macfarlane J., Vallance H., Joseph K. Prediction of obstetrical risk using maternal serum markers and clinical risk factors // Prenatal Diagnosis.2014 № 34(2). Р.172–179.

41.     Moons K., Altman D., Vergouwe Y., Royston P. Prognosis and prognostic research: application and impact of Prognostic Models in clinical practice // BMJ. 2009. № 338(6). 336-342.

42.     Moons K., de Groot J., Bouwmeester W., Vergouwe Y., Mallett S., Altman D.G., Reitsma J.B., Collins G.S. Critical appraisal and data extraction for Systematic Reviews of prediction Modelling Studies: The CHARMS Checklist // PLoS Medicine. 2014. №11(10). Р.1–12.

43.     Murphy K.E., Hannah M.E., Willan A.R., Hewson S.A., Ohlsson A., Kelly E.N., Mattews S.G., Saigal S., Asztalos E., Ross S., Delisle M.F., Amankwah K., Guselle P., Gafni A., Lee S.K., Armson B.A. Multiple Courses of Antenatal Corticosteroids for Preterm Birth (MACS): A Randomised Controlled Trial // The Lancet. 2008. № 372(12). Р. 20-27.

44.     Myatt L., Clifton R., Roberts J., Spong C., Hauth J.C., Varner M.W., Thorp J.M., Peaceman A.M., Ramin S.M., Carpenter M.W., Iamd J.D., Sciscione A., Harper M., Tolosa J.E., Saade G., Sorokin Y., Anderson G. First-Trimester Prediction of Preeclampsia in Low-Risk Nulliparous Women // Obstetrics and gynecology. 2013 №119(6). Р.1234–1242.

45.     North R.A., McCowan L., Dekker G.A., Poston L., Chan E., Stewart A.W., Black M.A., Taylor R.S., Walker J.J., Baker P.N., Kenny L.C. Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in International prospective cohort // BMJ. 2011. № 342.Р. 1-11.

46.     Odibo A., Zhong Y., Goetzinger K., Odibo L., Bick J. First-trimester placental protein 13, PAPP-A ,uterine Artery Doppler and maternal characteristics in the prediction of preeclampsia // Placenta.2011. №32(8). Р.598–602.

47.     O’Gorman N., Wright D., Syngelaki A., Akolekar R., Wright A., Poon L.C., Nicolades K.H. Competing Risks Model in screening for preeclampsia by maternal factors and biomarkers at 11-13 weeks gestation // American Journal of Obstetrics and Gynecology. 2016. №214(1).Р.103–114.

48.     Palomaki G.E., Haddow J.E., Haddow H., Salahuddin S., Geahchan C., Cerderia A., Verlohren S., Horowitz G., Thadhani R., Karumanchi S., Rana S. Modeling Risk for severe adverse outcomes using angiogenic factor measurements in women with suspected preterm preeclampsia // Prenatal Diagnosis.2015.№ 35(4). Р.386–393.

49.     Payne B.A., Hutcheon J., Ansermino M., Hall D.R., Bhutta Z.A., Bhutta S.Z., Biryabarema C., Grobman W.A., Groen H., Haniff F., Li J., Magee L., Merialdi M., Nakimuli A.,Qu Z., Sikandar R., Sass N., Sawchuck D., Steyn D., Widmer M., Zhou J., von Dadelszen P., for the miniPIERS Study Working Group. A Risk Prediction Model for the assessment and triage of women with hypertensive disorders of pregnancy in low-resourced settings: The miniPIERS (Pre-Eclampsia Integrated Estimate of RiSk) Multi-Country Prospective Cohort Study // PLoS Medicine.2014 № 11(1).Р.1-13.

50.     Payne B.A., Hutcheon J., Dunsmuir D., Cloete G., Dumont G., Hall D., Lim J., Magee L., Sikandar R., Qureshi R., van Papendorp E., Ansermino M., von Dadelszen P. Assessing the incremental value of blood Oxygen Saturation (SpO2) in the miniPIERS (Pre-Eclampsia Integrated Estimate of RiSk) Risk Prediction Model // Journal of Obstetrics and Gynaecology Canada. 2015. № 37(1). Р.16–24.

51.     Payne B.A., Groen H., Ukah U.V., Ansermino, J.M., Bhutta Z., Grobman W., Hall D., Hutcheon J.A., Magee L., von Dadelszen P. Development and internal validation of a multivariable Model to predict perinatal death in pregnancy hypertension // Pregnancy Hypertension. 2015 №5(4). Р.315–321.

52.     Poon L.C., Nicolaides K.H. Early prediction of preeclampsia // Obstetrics and Gynecology International. 2014. Р.1-11.

53.     Poon L.C., Kametas N.A., Chelemen T., Leal A., Nicolaides K.H. Maternal risk factors for hypertensive disorders in pregnancy: a multivariate approach // Journal of Human Hypertension.2010. № 24(2). Р.104–110.

54.     Rath D., Schlembach W. Prediction of pre-eclampsia: claim, reality and clinical consequences // Geburtshilfe Neonatol. 2013. №8. Р.117–118.

55.     Riley R., Hayden J., Steyerberg E., Moon K., Abrams K., Kyzas P.A., Malats N., Briggs A., Shroter S., Altman D.G., Hemingway H., for the PROGRESS Group Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research // PLoS Medicine. 2013. №10(2).Р.1-9.

56.     Ruiter M., Kwee A., Naaktgeboren C.A., Louhanepessy R.L., Franx A., Moons K., Koster M.P., Group RESPECT study. External validation of Prognostic Models for preeclampsia in a large Dutch Multicentre Prospective Cohort // American Journal of Obstetrics and Gynecology.2016. №243 Р.83-112.

57.     Say L., Chou D., Gemmill A., Tuncalp O., Moller A.B., Daniels J., Gulmezoglu A.M., Temmerman M., Alkema L. Global causes of maternal death: a WHO Systematic Analysis // The Lancet Global Health. 2014 №2(6). Р.323–333.

58.     Scott H., Danel I. Accountability for improving maternal and newborn health // Best Practice & Research Clinical Obstetrics & Gynaecology . 2016. №36. Р.45–56.

59.     Scazzocchio E. Figueras F., Crispi F., Meler E., Masoller N., Mula R., Gratacos E. Performance of a first-trimester screening of preeclampsia in a routine care low-risk setting // American Journal of Obstetrics and Gynecology.2013. № 208(3). Р.203.e1–203.e10.

60.     Scazzocchio E., Crovetto F., Triunfo S., Gratacos E., Figueras F. Validation of a first-trimester screening model for pre-eclampsia in an unselected population // Ultrasound in Obstetrics and Gynecology.2017. №49(2). Р.188–193.

61.     Shetty A.K. Global maternal, hewborn, and child health: successes, challenges, and opportunities // Pediatric Clinics of North America. 2016 № 63(1). Р.1–18.

62.     Steyerberg, E et al. “Prognosis Research Strategy (PROGRESS) Series 3: Prognostic Model Research.” PLoS Med 10.2 (2013): 1–9. Web.

63.     Thangaratinam S., Langenveld J., Mol B.W., Khan K.S. Prediction of complications in Early-onset Pre-Eclampsia ( PREP ): Development and External Multinational Validation of Prognostic Models // BMC Medicine. 2017. Р.1–11.

64.     Tsiakkas A., Saiid Y., Wright A., Wright D., Nicolaides K.H. Competing Risks Model in screening for preeclampsia by maternal factors and biomarkers at 30-34 weeks’ gestation // American Journal of Obstetrics and Gynecology. 2016. №215(1). Р. 87.e1–87.e17.

65.     Ukah V.U., Payne B., Hutcheon J.A., Ansermino J.M., Ganzevoort W., Thangaratinam S., Magee L.A., von Dadelzen P. Assessment of the fullPIERS Risk Prediction Model in women with early-onset preeclampsia // Hypertension.2018. № 71. Р.659-665.

66.     Ukah, V.U., Payne B., Lee T., Magee L.A., von Dadelzen P., for the fullPIERS an miniPIERS working groups. External validation of the fullPIERS Model for predicting adverse maternal outcomes in pregnancy Hypertension in Low- and Middle-Income Countries // Hypertension.2017. №69(4). Р.705–711.

67.     Vis, J.Y., Wilms F.F., Kuin R.A., Reuvers J.M., Stam M.C., Pattinaja D.A., Mol B.W. Time to delivery after the first course of antenatal corticosteroids: a cohort study // American Journal of Perinatology. 2011. № 28(9). Р.683–688.

68.     von Dadelszen P., Menzies J.M., Payne B., Magee L.A. Predicting adverse outcomes in women with severe pre-eclampsia // Seminars in Perinatology. 2009. №33(3). Р.152–157.

69.     von Dadelszen P., Payne B., Jing L., Ansermino J.M., Lee T.,Walker J.J., Walley K.R., Lee S.K., Russel J.A., Magee L.A., for the PIERS study group. Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS Model // The Lancet.2011 № 377. Р.219–227.

70.     Wortelboer E.J., Koster M., Cuckle H.S., Stoutenbeek P., Schielen, P.C., Visser G.H. First-trimester Placental Protein 13 and Placental Growth Factor: markers for identification of women destined to develop early-onset pre-eclampsia // BJOG: An International Journal of Obstetrics and Gynaecology. 2010. №117(11). Р.1384–1389.

71.     Wright D., Syngelaki A., Akolekar R., Poon L.C., Nicolaides K.H. Competing Risks Model in screening for preeclampsia by maternal characteristics and medical history // American Journal of Obstetrics and Gynecology.2015. № 213(1). Р.62.e1–62.e10.

72.     Wynants L., Collins G.S., Van Calster K. Key steps and common pitfalls in developing and validating Risk Models // BJOG: An International Journal of Obstetrics and Gynaecology. 2017. №124(3). Р.423–432.

73.     Yen T.W., Payne B., Qu.Z., Hutcheon J.A., Lee T., Magee L.A., Walters B.N., von Dadelszen P. Using clinical symptoms to predict adverse maternal and perinatal outcomes in women with preeclampsia: data from the PIERS (Pre-Eclampsia Integrated Estimate of RiSk) Study // Journal of Obstetrics and Gynaecology Canada.2011. № 33(8). Р.803–809.

74.     Zakiyah N., Postma M.J., Baker P.N., van Asselt A.D. Pre-eclampsia diagnosis and treatment options: a Review of published Economic Assessments // Pharmaco Economics. 2015 №33(10). Р.1069–1082.

Number of Views: 871


Category of articles: Reviews

Bibliography link

Нургалиева Г.Т., Танышева Г.А., Манабаева Г.К. Современные возможности прогнозирования преэклампсии и её осложнений. Обзор литературы / / Наука и Здравоохранение. 2018. 4 (Т.20). С. 86-106.

Nurgaliyeva G.T., Tanysheva G.A., Manabaeva G.K. The modern possibilities of prediction of pre-eclampsia and its complications. A literature review // Nauka i Zdravookhranenie [Science & Healthcare]. 2018, (Vol.20) 4, pp. 86-106.

Нургалиева Г.Т., Танышева Г.А., Манабаева Г.К. Преэклампсияны болжаудың қазіргі заман мүмкіндіктері және оның асқынулары. Әдебиеттерді шолу / / Ғылым және Денсаулық сақтау. 2018. 4 (Т.20). Б. 86-106.

 


Авторизируйтесь для отправки комментариев