95a679c3b76945e63d865eab5ae3121f pmc_9866466.pdf 790ce25d9f8bbf25792d870ddeb6b126c9582773 pmc_9866466.pdf 26368588fc52b182e46745aa1866b23215eaa49b6b1143fecc2436020a47ce3d pmc_9866466.pdf Title: Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers Subject: 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 Author: Carmen Garrido-Giménez, 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, Elisa Llurba and on behalf of the EuroPE Working Group Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Fri Jan 6 02:07:20 2023 CET ModDate: Fri Jan 6 02:22:43 2023 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 13 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 862672 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- POCBCB+VnURWPalladioL Type 1 Custom yes yes no 10 0 VBFKXR+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 XEQFMZ+URWPalladioL-Roma Type 1 Custom yes yes no 20 0 QSZLKV+URWPalladioL-Ital Type 1 Custom yes yes no 24 0 FFAYKL+VnURWPalladioL-Bold Type 1 Custom yes yes no 28 0 XPUSBJ+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 46 0 BSDWLC+TimesNewRomanPSMT Type 1C WinAnsi yes yes no 49 0 BCGYZM+CMSY10 Type 1 Builtin yes yes no 74 0 OVNEXW+EURM10 Type 1 Builtin yes yes no 78 0 QLBIEB+URWPalladioL-BoldItal Type 1 Custom yes yes no 88 0 NLEANO+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 102 0 NLEAOP+PalatinoLinotype CID TrueType Identity-H yes yes yes 105 0 NLEAPA+PalatinoLinotype TrueType WinAnsi yes yes no 111 0 NLEAPC+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 114 0 NLEANO+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 128 0 NLEAPA+PalatinoLinotype TrueType WinAnsi yes yes no 131 0 NLEAPC+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 134 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-07-04 02:29:26 CEST RepresentationInformation: pmc_9866466.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2023-01-26 02:11:28 CET Size: 862672 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 321 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@44c8afef Item: Title: Materials and Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Children: Item: Title: Study Design Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Item: Title: Diagnostic Criteria Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Item: Title: Laboratory Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Item: Title: Statistical Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Children: Item: Title: Characteristics of the Study Population Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Predictive Model Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Children: Item: Title: Principal Findings Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Interpretation of Results and Comparison with Existing Literature Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Clinical Implications and Future Research Directions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Item: Title: Strengths and Limitations Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Info: Title: Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers Author: Carmen Garrido-Giménez, 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, Elisa Llurba and on behalf of the EuroPE Working Group Subject: 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. 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