Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers

dc.contributor
Institut Català de la Salut
dc.contributor
[Garrido-Giménez C, Cruz-Lemini M] Department of Obstetrics and Gynecology, Maternal-Fetal Medicine Unit, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Bellaterra, Spain. Women and Perinatal Health Research Group, Institut d’Investigació Biomèdica Sant Pau, Barcelona, Spain. Primary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Network and Maternal and Child Health Development Network, Instituto de Salud Carlos III, Madrid, Spain. [Álvarez FV] Clinical Biochemistry, Laboratory Medicine, Hospital Universitario Central de Asturias and Department of Biochemistry and Molecular Biology, Universidad de Oviedo, Oviedo, Spain. [Nan MN] Clinical Biochemistry, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Bellaterra, Spain. [Carretero F] Clinical Biochemistry, Laboratory Medicine, Hospital Universitario Central de Asturias and Department of Biochemistry and Molecular Biology, Universidad de Oviedo, Oviedo, Spain. Cátedra de Inteligencia Analítica, Universidad de Oviedo, Oviedo, Spain. [Fernández-Oliva A] Department of Obstetrics and Gynecology, Maternal-Fetal Medicine Unit, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Bellaterra, Spain. Women and Perinatal Health Research Group, Institut d’Investigació Biomèdica Sant Pau, Barcelona, Spain. [Alijotas-Reig J] Unitat de Malalties Autoimmunes Sistèmiques, Servei de Medicina Interna, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain. Grup de Recerca de Malalties Sistèmiques, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Vall d’Hebron Hospital Universitari, Barcelona, Spain
dc.contributor
Vall d'Hebron Barcelona Hospital Campus
dc.contributor.author
Cruz-Lemini, Mónica
dc.contributor.author
Álvarez, Francisco V.
dc.contributor.author
Nan, Madalina Nicoleta
dc.contributor.author
Carretero, Francisco
dc.contributor.author
Fernández-Oliva, Antonio
dc.contributor.author
Alijotas Reig, Jaume
dc.contributor.author
Garrido-Giménez, Carmen
dc.date.issued
2023-03-01T11:34:07Z
dc.date.issued
2023-03-01T11:34:07Z
dc.date.issued
2023-01-05
dc.identifier
Garrido-Giménez C, Cruz-Lemini M, Álvarez FV, Nan MN, Carretero F, Fernández-Oliva A, et al. Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers. J Clin Med. 2023 Jan 5;12(2):431.
dc.identifier
2077-0383
dc.identifier
https://hdl.handle.net/11351/9081
dc.identifier
10.3390/jcm12020431
dc.identifier
36675361
dc.description.abstract
Angiogenic factors; Preeclampsia; Uric acid
dc.description.abstract
Factores angiogénicos; Preeclampsia; Ácido úrico
dc.description.abstract
Factors angiogènics; Preeclàmpsia; Àcid úric
dc.description.abstract
N-terminal pro-brain natriuretic peptide (NT-proBNP) and uric acid are elevated in pregnancies with preeclampsia (PE). Short-term prediction of PE using angiogenic factors has many false-positive results. Our objective was to validate a machine-learning model (MLM) to predict PE in patients with clinical suspicion, and evaluate if the model performed better than the sFlt-1/PlGF ratio alone. A multicentric cohort study of pregnancies with suspected PE between 24+0 and 36+6 weeks was used. The MLM included six predictors: gestational age, chronic hypertension, sFlt-1, PlGF, NT-proBNP, and uric acid. A total of 936 serum samples from 597 women were included. The PPV of the MLM for PE following 6 weeks was 83.1% (95% CI 78.5-88.2) compared to 72.8% (95% CI 67.4-78.4) for the sFlt-1/PlGF ratio. The specificity of the model was better; 94.9% vs. 91%, respectively. The AUC was significantly improved compared to the ratio alone [0.941 (95% CI 0.926-0.956) vs. 0.901 (95% CI 0.880-0.921), p < 0.05]. For prediction of preterm PE within 1 week, the AUC of the MLM was 0.954 (95% CI 0.937-0.968); significantly greater than the ratio alone [0.914 (95% CI 0.890-0.934), p < 0.01]. To conclude, an MLM combining the sFlt-1/PlGF ratio, NT-proBNP, and uric acid performs better to predict preterm PE compared to the sFlt-1/PlGF ratio alone, potentially increasing clinical precision.
dc.description.abstract
This work was supported by public funds obtained in competitive calls with peer review (grant PI19/00702), Insituto de Salud Carlos III, Spanish Ministry of Health, by the Maternal and Child Health and Development Network (SAMID, RD16/0022/0015), Instituto de Salud Carlos III, Madrid, Spain, the Spanish Clinical Research and Clinical Trials Platform, SCReN (Spanish Clinical Research Network), funded by the ISCIII-General Subdirectorate for Evaluation and Promotion of Research, through project PT13/0002/0028, integrated in the 2013–2016 R + D + I State Plan and co-financed by and the European Regional Development Fund (FEDER); and by the Primary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin Network (RICORS, RD21/0012/0001), Instituto de Salud Carlos III, Madrid, Spain, funded by the Recovery, Transformation and Resilience Plan 2017–2020, ISCIII, and by the European Union-Next Generation EU. Dr Cruz-Lemini is supported by Juan Rodés contract JR19/00047, Instituto de Salud Carlos III-Spanish Ministry of Health. Funding sources were not involved in study design, collection, analysis, and interpretation of data.
dc.format
application/pdf
dc.language
eng
dc.publisher
MDPI
dc.relation
Journal of Clinical Medicine;12(2)
dc.relation
https://doi.org/10.3390/jcm12020431
dc.relation
info:eu-repo/grantAgreement/ES/PE2013-2016/RD16%2F0022%2F0015
dc.rights
Attribution 4.0 International
dc.rights
http://creativecommons.org/licenses/by/4.0/
dc.rights
info:eu-repo/semantics/openAccess
dc.source
Scientia
dc.subject
Marcadors bioquímics
dc.subject
Àcid úric
dc.subject
Preeclàmpsia - Diagnòstic
dc.subject
CHEMICALS AND DRUGS::Heterocyclic Compounds::Alkaloids::Xanthines::Uric Acid
dc.subject
CHEMICALS AND DRUGS::Biological Factors::Biomarkers
dc.subject
DISEASES::Female Urogenital Diseases and Pregnancy Complications::Pregnancy Complications::Hypertension, Pregnancy-Induced::Pre-Eclampsia
dc.subject
Other subheadings::Other subheadings::/diagnosis
dc.subject
ENFERMEDADES::enfermedades de los genitales femeninos y complicaciones del embarazo::complicaciones del embarazo::hipertensión inducida en el embarazo::preeclampsia
dc.subject
Otros calificadores::Otros calificadores::/diagnóstico
dc.subject
COMPUESTOS QUÍMICOS Y DROGAS::compuestos heterocíclicos::alcaloides::xantinas::ácido úrico
dc.subject
COMPUESTOS QUÍMICOS Y DROGAS::factores biológicos::biomarcadores
dc.title
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers
dc.type
info:eu-repo/semantics/article
dc.type
info:eu-repo/semantics/publishedVersion


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)