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 Medicina Preventiva i Salut Pública)
Cruz-Lemini, Monica (Universitat Autònoma de Barcelona. Departament de Pediatria, Obstetrícia i Ginecologia i Medicina Preventiva i Salut Pública)
Álvarez Menéndez, Francisco V. (Hospital Universitario Central de Asturias)
Nan, Madalina Nicoleta (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya))
Carretero, Francisco (Universidad de Oviedo)
Fernández-Oliva, Antonio (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya))
Mora, Josefina (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya))
Sánchez-García, Olga (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya))
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, Elisa (Universitat Autònoma de Barcelona. Departament de Pediatria, Obstetrícia i Ginecologia i Medicina Preventiva i Salut Pública)

Data:	2023
Resum:	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.
Ajuts:	Instituto de Salud Carlos III PI19/00702 Ministerio de Economía y Competitividad RD16/0022/0015 Ministerio de Economía y Competitividad PT13/0002/0028
Drets:	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.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	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
Publicat a:	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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