Study for Improving Maternal, Pregnancy and Child Outcomes
NCT ID: NCT03831490
Last Updated: 2024-05-09
Study Results
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Basic Information
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COMPLETED
13000 participants
OBSERVATIONAL
2018-11-09
2022-12-31
Brief Summary
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1. Creating a Swedish prediction model with population-specific risk factors, optimized for the Swedish health care system, identifying high-risk women for preterm preeclampsia and validate the model within the cohort. This would give us the possibility to start aspirin prophylaxis in time, which has been proven to reduce the risk of developing preterm preeclampsia by 50%.
2. Validating the Fetal Medicine Foundation prediction model for detection of preterm (\< 37 gestational weeks) preeclampsia in a Swedish population.
3. Creating a prediction model identifying high-risk women for overall preeclampsia during pregnancy and birth of a small for gestational age infant in order to plan individualized surveillance for early detection, which has been proven beneficial for both the mother and infant.
4. Creating a national pregnancy biobank with blood samples and individual clinical registry data, including pregnancy outcomes, enabling future research on prevention and early detection for various adverse pregnancy outcomes which could be such as preterm birth and intrauterine growth restriction.
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Detailed Description
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Risk assessment for preeclampsia enables both prevention and early prediction of the disease.
Swedish risk assessment for preeclampsia in early pregnancy is still obtained by maternal history and characteristics, without medical examinations, which only detects about 30% of women that will develop preeclampsia. Risk factors are evaluated individually without being incorporated into a combined model that would allow multivariable analysis. This approach has been proven to be poor due to low specificity and sensitivity. Lately a more complex prediction model has been developed by the Fetal Medicine Foundation, using multivariable analysis and including serum biomarkers and physiological measurements reflecting maternal adaption to pregnancy. Intervention with aspirin given to identified high-risk pregnancies according this model has been shown to decrease the incidence of preterm (\< 37 gestational weeks) preeclampsia (OR: 0.38; 95% CI 0.20-0.74), compared to placebo. Detection rates and cut-off values have been shown to vary between populations, depending on differences in population characteristics and incidence of disease, overfitting of the original model and differences in healthcare systems. Therefore, the model needs to be validated in Sweden. Further, the Fetal Medicine Foundation prediction model includes expensive covariates such as several biochemical markers and uterine artery Doppler. There is a need to create, validate and implement a cost-effective prediction model for first trimester screening for preeclampsia in a Swedish population, with the purpose to select who might benefit from aspirin prophylaxis to prevent preterm preeclampsia. We will study preeclampsia both according to the definition used in Sweden prior to 2020 and the definition used hereafter.
Early detection of preeclampsia remains one of the major focuses of maternal health care and is emphasized by the WHO, since it has proven to be beneficial for both the mother and unborn child. Small-for-gestational-age fetuses not identified before delivery have an increased risk of adverse perinatal outcomes, compared to those identified during pregnancy. Identification of high-risk pregnancies is therefore important in early pregnancy not only to plan for prophylactic interventions, but also to optimize surveillance and to plan deliveries. Today most Swedish women attend the same maternal health care program with increasing number of visits in the end of pregnancy. By risk identification in early pregnancy we can individualize maternal health care and target women at high risk early in pregnancy. High-risk pregnancies can be referred to specialized health care and normal pregnancies followed at the basic maternal health care.
The Swedish registry data is unique and combining it with a biobank containing blood samples from the first trimester could improve maternal healthcare and in the long run reduce adverse outcomes for Swedish women. A national first trimester pregnancy biobank would facilitate future research on prevention and prediction of pregnancy complications.
A total of 13000 enrolled individuals will be needed for creating the model and for validation.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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history
combining these 4 interventions will great an algorithm predicting preeclampsia
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
FEMALE
Yes
Sponsors
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Thermo Fisher Scientific, Inc
INDUSTRY
Perkin Elmer Inc.
INDUSTRY
Roche Pharma AG
INDUSTRY
Uppsala University
OTHER
Responsible Party
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Principal Investigators
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Lina Bergman, MD, PhD
Role: STUDY_CHAIR
Uppsala University
Peter Lindgren, MD, PhD
Role: STUDY_CHAIR
Karolinska Institutet
Anna Sandström, MD, PhD
Role: STUDY_CHAIR
Karolinska Institutet
Peter Conner, Ass Prof
Role: STUDY_CHAIR
Karolinska Institutet
Marius Kublickas, Ass Prof
Role: STUDY_CHAIR
Karolinska Institutet
Stefan Hansson, Professor
Role: STUDY_CHAIR
Lund University
Locations
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Eskilstuna hospital
Eskilstuna, Sörmland, Sweden
Södra Älvsborgs sjukhus
Borås, Västra Götalandsregionen, Sweden
Norra Älvsborgs sjukhus
Trollhättan, Västra Götalandsregionen, Sweden
County Council Dalarna
Falun, , Sweden
Gothenburg University, Sahlgrenska academy, dept of obstetrics and gynecology
Gothenburg, , Sweden
Lund University Hospital, dept of obstetrics and gynecology
Lund, , Sweden
Örebro University Hospital
Örebro, , Sweden
Karolinska Institute
Stockholm, , Sweden
Uppsala University Hopsital, department of women's and children's health
Uppsala, , Sweden
Countries
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References
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Duley L. The global impact of pre-eclampsia and eclampsia. Semin Perinatol. 2009 Jun;33(3):130-7. doi: 10.1053/j.semperi.2009.02.010.
Mol BWJ, Roberts CT, Thangaratinam S, Magee LA, de Groot CJM, Hofmeyr GJ. Pre-eclampsia. Lancet. 2016 Mar 5;387(10022):999-1011. doi: 10.1016/S0140-6736(15)00070-7. Epub 2015 Sep 2.
Wikstrom AK, Larsson A, Eriksson UJ, Nash P, Norden-Lindeberg S, Olovsson M. Placental growth factor and soluble FMS-like tyrosine kinase-1 in early-onset and late-onset preeclampsia. Obstet Gynecol. 2007 Jun;109(6):1368-74. doi: 10.1097/01.AOG.0000264552.85436.a1.
Wright D, Syngelaki A, Akolekar R, Poon LC, Nicolaides KH. Competing risks model in screening for preeclampsia by maternal characteristics and medical history. Am J Obstet Gynecol. 2015 Jul;213(1):62.e1-62.e10. doi: 10.1016/j.ajog.2015.02.018. Epub 2015 Feb 25.
Akolekar R, Syngelaki A, Poon L, Wright D, Nicolaides KH. Competing risks model in early screening for preeclampsia by biophysical and biochemical markers. Fetal Diagn Ther. 2013;33(1):8-15. doi: 10.1159/000341264. Epub 2012 Aug 16.
Rolnik DL, Wright D, Poon LC, O'Gorman N, Syngelaki A, de Paco Matallana C, Akolekar R, Cicero S, Janga D, Singh M, Molina FS, Persico N, Jani JC, Plasencia W, Papaioannou G, Tenenbaum-Gavish K, Meiri H, Gizurarson S, Maclagan K, Nicolaides KH. Aspirin versus Placebo in Pregnancies at High Risk for Preterm Preeclampsia. N Engl J Med. 2017 Aug 17;377(7):613-622. doi: 10.1056/NEJMoa1704559. Epub 2017 Jun 28.
Mosimann B, Pfiffner C, Amylidi-Mohr S, Risch L, Surbek D, Raio L. First trimester combined screening for preeclampsia and small for gestational age - a single centre experience and validation of the FMF screening algorithm. Swiss Med Wkly. 2017 Aug 25;147:w14498. doi: 10.4414/smw.2017.14498. eCollection 2017.
Oliveira N, Magder LS, Blitzer MG, Baschat AA. First-trimester prediction of pre-eclampsia: external validity of algorithms in a prospectively enrolled cohort. Ultrasound Obstet Gynecol. 2014 Sep;44(3):279-85. doi: 10.1002/uog.13435. Epub 2014 Aug 13.
O'Gorman N, Wright D, Syngelaki A, Akolekar R, Wright A, Poon LC, Nicolaides KH. Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 11-13 weeks gestation. Am J Obstet Gynecol. 2016 Jan;214(1):103.e1-103.e12. doi: 10.1016/j.ajog.2015.08.034. Epub 2015 Aug 19.
WHO Recommendations for Prevention and Treatment of Pre-Eclampsia and Eclampsia. Geneva: World Health Organization; 2011. Available from http://www.ncbi.nlm.nih.gov/books/NBK140561/
Lindqvist PG, Molin J. Does antenatal identification of small-for-gestational age fetuses significantly improve their outcome? Ultrasound Obstet Gynecol. 2005 Mar;25(3):258-64. doi: 10.1002/uog.1806.
Sreeja Sarojini AG, Andrew Pecora and K. Stephen Suh. Proactive Biobanking to Improve Research and Health Care. Journal of Tissue Science & Engineering.
Stephansson O, Petersson K, Bjork C, Conner P, Wikstrom AK. The Swedish Pregnancy Register - for quality of care improvement and research. Acta Obstet Gynecol Scand. 2018 Apr;97(4):466-476. doi: 10.1111/aogs.13266. Epub 2017 Dec 14.
Bergman L, Sandstrom A, Jacobsson B, Hansson S, Lindgren P, Larsson A, Imberg H, Conner P, Kublickas M, Carlsson Y, Wikstrom AK. Study for Improving Maternal Pregnancy And Child ouTcomes (IMPACT): a study protocol for a Swedish prospective multicentre cohort study. BMJ Open. 2020 Sep 23;10(9):e033851. doi: 10.1136/bmjopen-2019-033851.
Other Identifiers
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Dnr 2018-231
Identifier Type: -
Identifier Source: org_study_id
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