Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Journal of clinical medicine - 12(2023), 2 vom: 05. Jan. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Garrido-Giménez, Carmen [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 08.03.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/jcm12020431 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM351860851 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a N-terminal pro-brain natriuretic peptide (NT-proBNP) | |
| 650 | 4 | |a angiogenic factors | |
| 650 | 4 | |a machine-learning | |
| 650 | 4 | |a placental growth factor (PlGF) | |
| 650 | 4 | |a prediction | |
| 650 | 4 | |a preeclampsia | |
| 650 | 4 | |a soluble fms-like tyrosine kinase 1 (sFlt-1) | |
| 650 | 4 | |a uric acid | |
| 700 | 1 | |a Cruz-Lemini, Mónica |e verfasserin |4 aut | |
| 700 | 1 | |a Álvarez, Francisco V |e verfasserin |4 aut | |
| 700 | 1 | |a Nan, Madalina Nicoleta |e verfasserin |4 aut | |
| 700 | 1 | |a Carretero, Francisco |e verfasserin |4 aut | |
| 700 | 1 | |a Fernández-Oliva, Antonio |e verfasserin |4 aut | |
| 700 | 1 | |a Mora, Josefina |e verfasserin |4 aut | |
| 700 | 1 | |a Sánchez-García, Olga |e verfasserin |4 aut | |
| 700 | 1 | |a García-Osuna, Álvaro |e verfasserin |4 aut | |
| 700 | 1 | |a Alijotas-Reig, Jaume |e verfasserin |4 aut | |
| 700 | 1 | |a Llurba, Elisa |e verfasserin |4 aut | |
| 700 | 1 | |a On Behalf Of The EuroPE Working Group |e verfasserin |4 aut | |
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