Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers
Garrido-Giménez, Carmen (Universitat Autònoma de Barcelona. Departament de Pediatria, Obstetrícia i Ginecologia i de Medicina Preventiva i Salut Pública)
Cruz-Lemini, Monica (Universitat Autònoma de Barcelona. Departament de Pediatria, Obstetrícia i Ginecologia i de Medicina Preventiva i Salut Pública)
Álvarez Menéndez, Francisco V. (Hospital Universitario Central de Asturias)
Nan, Madalina Nicoleta (Institut d'Investigació Biomèdica Sant Pau)
Carretero, Francisco (Universidad de Oviedo)
Fernández-Oliva, Antonio (Institut d'Investigació Biomèdica Sant Pau)
Mora Brugués, Josefina (Institut d'Investigació Biomèdica Sant Pau)
Sánchez García, Olga (Institut d'Investigació Biomèdica Sant Pau)
García Osuna, Álvaro (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular)
Alijotas-Reig, Jaume (Universitat Autònoma de Barcelona. Departament de Medicina)
Llurba, E. (Universitat Autònoma de Barcelona. Departament de Pediatria, Obstetrícia i Ginecologia i de Medicina Preventiva i Salut Pública)

Date:	2023
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.
Grants:	Instituto de Salud Carlos III PI19/00702 Ministerio de Economía y Competitividad RD16/0022/0015 Ministerio de Economía y Competitividad PT13/0002/0028
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	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
Published in:	Journal of clinical medicine, Vol. 12 (january 2023) , ISSN 2077-0383

DOI: 10.3390/jcm12020431
PMID: 36675361

13 p, 842.5 KB

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