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 cli...
| Autores: | , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau) |
| Repositorio: | r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau |
| OAI Identifier: | oai:iibsantpau.fundanetsuite.com:p15692 |
| Acceso en línea: | https://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=15692 |
| Access Level: | acceso abierto |
| Palabra clave: | 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 |
| id |
ES_eb8e0f3312c1ecb7d093ee20dc17c9eb |
|---|---|
| oai_identifier_str |
oai:iibsantpau.fundanetsuite.com:p15692 |
| network_acronym_str |
ES |
| network_name_str |
España |
| repository_id_str |
|
| spelling |
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as BiomarkersGarrido-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.angiogenic factorsmachine-learningN-terminal pro-brain natriuretic peptide (NT-proBNP)placental growth factor (PlGF)predictionpreeclampsiasoluble fms-like tyrosine kinase 1 (sFlt-1)uric acidN-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. © 2023 by the authors.MDPI2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=15692Journal of Clinical MedicineISSN: 20770383reponame:r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pauinstname:Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau)Inglésinfo:eu-repo/semantics/openAccessoai:iibsantpau.fundanetsuite.com:p156922026-06-14T12:41:47Z |
| dc.title.none.fl_str_mv |
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers |
| title |
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers |
| spellingShingle |
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers Garrido-Giménez C. 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 |
| title_short |
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers |
| title_full |
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers |
| title_fullStr |
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers |
| title_full_unstemmed |
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers |
| title_sort |
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers |
| dc.creator.none.fl_str_mv |
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. |
| author |
Garrido-Giménez C. |
| author_facet |
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. |
| author_role |
author |
| author2 |
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. |
| author2_role |
author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
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 |
| topic |
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 |
| description |
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. © 2023 by the authors. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=15692 |
| url |
https://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=15692 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
MDPI |
| publisher.none.fl_str_mv |
MDPI |
| dc.source.none.fl_str_mv |
Journal of Clinical Medicine ISSN: 20770383 reponame:r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau instname:Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau) |
| instname_str |
Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau) |
| reponame_str |
r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau |
| collection |
r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau |
| repository.name.fl_str_mv |
|
| repository.mail.fl_str_mv |
|
| _version_ |
1869423238175522816 |
| score |
15,811543 |