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

Garrido-Giménez, Carmen; Cruz-Lemini, Mónica; Álvarez, Francisco V.; Nan, Madalina Nicoleta; Carretero, Francisco; Fernández-Oliva, Antonio; Mora, Josefina; Sánchez-García, Olga; García-Osuna, Álvaro; Alijotas-Reig, Jaume; Llurba, Elisa; on behalf of the EuroPE Working Group,

doi:10.3390/jcm12020431

Open AccessArticle

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

by

Carmen Garrido-Giménez

^1,2,3,†

,

Mónica Cruz-Lemini

^1,2,3,†

,

Francisco V. Álvarez

⁴,

Madalina Nicoleta Nan

⁵,

Francisco Carretero

^4,6,

Antonio Fernández-Oliva

^1,2,

Josefina Mora

⁵,

Olga Sánchez-García

^2,3,

Álvaro García-Osuna

⁵

,

Jaume Alijotas-Reig

^7,8,*

,

Elisa Llurba

^1,2,3

and

on behalf of the EuroPE Working Group

^‡

¹

Department of Obstetrics and Gynecology, Maternal-Fetal Medicine Unit (Hospital de la Santa Creu i Sant Pau, Sant Antoni Maria Claret, 167), Universitat Autònoma de Barcelona, 08025 Barcelona, Spain

²

Women and Perinatal Health Research Group, Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU), Sant Quintí 77–79, 08041 Barcelona, Spain

³

Primary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Network (SAMID-RICORS, RD21/0012) and Maternal and Child Health Development Network (SAMID, RD16/0022), Instituto de Salud Carlos III, 28040 Madrid, Spain

⁴

Clinical Biochemistry, Laboratory Medicine, Hospital Universitario Central de Asturias and Department of Biochemistry and Molecular Biology, Universidad de Oviedo, 33011 Oviedo, Spain

⁵

Clinical Biochemistry, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, 08025 Barcelona, Spain

⁶

Cátedra de Inteligencia Analítica, Universidad de Oviedo, 33011 Oviedo, Spain

⁷

Systemic Autoimmune Disease Unit, Internal Medicine Department, Vall d’Hebron University Hospital, Departament de Medicina de la Universitat Autònoma de Barcelona, 08025 Barcelona, Spain

⁸

Systemic Autoimmune Diseases Research Group, Vall d’Hebron Research Institute/Vall d’Hebron Hospital, 08025 Barcelona, Spain

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

^‡

EuroPE Working~Group are listed in acknowledgments.

J. Clin. Med. 2023, 12(2), 431; https://doi.org/10.3390/jcm12020431

Submission received: 30 November 2022 / Revised: 27 December 2022 / Accepted: 28 December 2022 / Published: 5 January 2023

(This article belongs to the Special Issue Update on Diagnosis and Management of Preeclampsia and Fetal Growth Restriction)

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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.

Keywords: angiogenic factors; machine-learning; N-terminal pro-brain natriuretic peptide (NT-proBNP); placental growth factor (PlGF); prediction; preeclampsia; soluble fms-like tyrosine kinase 1 (sFlt-1); uric acid

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MDPI and ACS Style

Garrido-Giménez, C.; Cruz-Lemini, M.; Álvarez, F.V.; Nan, M.N.; Carretero, F.; Fernández-Oliva, A.; Mora, J.; Sánchez-García, O.; García-Osuna, Á.; Alijotas-Reig, J.; et al. Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers. J. Clin. Med. 2023, 12, 431. https://doi.org/10.3390/jcm12020431

AMA Style

Garrido-Giménez C, Cruz-Lemini M, Álvarez FV, Nan MN, Carretero F, Fernández-Oliva A, Mora J, Sánchez-García O, García-Osuna Á, Alijotas-Reig J, et al. Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers. Journal of Clinical Medicine. 2023; 12(2):431. https://doi.org/10.3390/jcm12020431

Chicago/Turabian Style

Garrido-Giménez, Carmen, Mónica Cruz-Lemini, Francisco V. Álvarez, Madalina Nicoleta Nan, Francisco Carretero, Antonio Fernández-Oliva, Josefina Mora, Olga Sánchez-García, Álvaro García-Osuna, Jaume Alijotas-Reig, and et al. 2023. "Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers" Journal of Clinical Medicine 12, no. 2: 431. https://doi.org/10.3390/jcm12020431

APA Style

Garrido-Giménez, C., Cruz-Lemini, M., Álvarez, F. V., Nan, M. N., Carretero, F., Fernández-Oliva, A., Mora, J., Sánchez-García, O., García-Osuna, Á., Alijotas-Reig, J., Llurba, E., & on behalf of the EuroPE Working Group. (2023). Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers. Journal of Clinical Medicine, 12(2), 431. https://doi.org/10.3390/jcm12020431

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

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