Literature DB >> 33280406

Prediction of Preeclampsia-Related Adverse Outcomes With the sFlt-1 (Soluble fms-Like Tyrosine Kinase 1)/PlGF (Placental Growth Factor)-Ratio in the Clinical Routine: A Real-World Study.

Lisa Antonia Dröge1, Frank Holger Perschel2,3, Natalia Stütz1, Anna Gafron4, Lisa Frank3, Andreas Busjahn5, Wolfgang Henrich1, Stefan Verlohren1.   

Abstract

This retrospective real-world study investigated the clinical use of the sFlt-1 (soluble fms-like tyrosine kinase 1)/PlGF (placental growth factor) ratio alone or in combination with other clinical tests to predict an adverse maternal (maternal death, kidney failure, hemolysis elevated liver enzymes low platelets-syndrome, pulmonary edema, disseminated intravascular coagulation, cerebral hemorrhage, or eclampsia) or fetal (delivery before 34 weeks because of preeclampsia and/or intrauterine growth restriction, respiratory distress syndrome, necrotizing enterocolitis, intraventricular hemorrhage, placental abruption or intrauterine fetal death or neonatal death within 7 days post natum) pregnancy outcome in patients with signs and symptoms of preeclampsia. We evaluated the sFlt-1/PlGF-ratio cutoff values of 38 and 85 and evaluated its integration into a multimarker model. Of 1117 subjects, 322 (28.8%) developed an adverse fetal or maternal outcome. Patients with an adverse versus no adverse outcome had a median sFlt-1/PlGF-ratio of 177 (interquartile range, 54-362) versus 14 (4-64). Risk-stratification with the sFlt-1/PlGF cutoff values into high- (>85), intermediate- (38-85), and low-risk (<38) showed a significantly shorter time to delivery in high- and intermediate- versus low-risk patients (4 versus 8 versus 29 days). When integrating all available clinical information into a multimarker model, an area under the curve of 88.7% corresponding to a sensitivity, specificity, positive and negative predictive value of 80.0%, 87.3%, 75.0%, and 90.2% was reached. The sFlt-1/PlGF-ratio alone was inferior to the full model with an area under the curve of 85.7%. As expected, blood pressure and proteinuria were significantly less accurate with an area under the curve of 69.0%. Combining biomarker measurements with all available information in a multimarker modeling approach increased detection of adverse outcomes in women with suspected disease.

Entities:  

Keywords:  HELLP syndrome; blood pressure; maternal death; preeclampsia; pregnancy outcome

Year:  2020        PMID: 33280406     DOI: 10.1161/HYPERTENSIONAHA.120.15146

Source DB:  PubMed          Journal:  Hypertension        ISSN: 0194-911X            Impact factor:   10.190


  13 in total

Review 1.  Long-Term Cardiovascular Disease Risk in Women After Hypertensive Disorders of Pregnancy: Recent Advances in Hypertension.

Authors:  Kavia Khosla; Sarah Heimberger; Kristin M Nieman; Avery Tung; Sajid Shahul; Anne Cathrine Staff; Sarosh Rana
Journal:  Hypertension       Date:  2021-08-15       Impact factor: 10.190

2.  Toward a new taxonomy of obstetrical disease: improved performance of maternal blood biomarkers for the great obstetrical syndromes when classified according to placental pathology.

Authors:  Roberto Romero; Eunjung Jung; Tinnakorn Chaiworapongsa; Offer Erez; Dereje W Gudicha; Yeon Mee Kim; Jung-Sun Kim; Bomi Kim; Juan Pedro Kusanovic; Francesca Gotsch; Andreea B Taran; Bo Hyun Yoon; Sonia S Hassan; Chaur-Dong Hsu; Piya Chaemsaithong; Nardhy Gomez-Lopez; Lami Yeo; Chong Jai Kim; Adi L Tarca
Journal:  Am J Obstet Gynecol       Date:  2022-09-03       Impact factor: 10.693

3.  Development and evaluation of a nomogram for adverse outcomes of preeclampsia in Chinese pregnant women.

Authors:  Jiangyuan Zheng; Li Zhang; Yang Zhou; Lin Xu; Zuyue Zhang; Yaling Luo
Journal:  BMC Pregnancy Childbirth       Date:  2022-06-20       Impact factor: 3.105

4.  Predicting Preeclampsia Pregnancy Termination Time Using sFlt-1.

Authors:  Hiroaki Tanaka; Kayo Tanaka; Sho Takakura; Naosuke Enomoto; Tomoaki Ikeda
Journal:  Front Med (Lausanne)       Date:  2022-06-20

5.  Clinical Application of a Multiparameter-Based Nomogram Model in Predicting Preeclampsia.

Authors:  Wenyue Chen; Sufang Sun
Journal:  Evid Based Complement Alternat Med       Date:  2022-06-13       Impact factor: 2.650

6.  Identifying preeclampsia-associated genes using a control theory method.

Authors:  Xiaomei Li; Lin Liu; Clare Whitehead; Jiuyong Li; Benjamin Thierry; Thuc D Le; Marnie Winter
Journal:  Brief Funct Genomics       Date:  2022-07-27       Impact factor: 4.840

Review 7.  A literature review and best practice advice for second and third trimester risk stratification, monitoring, and management of pre-eclampsia: Compiled by the Pregnancy and Non-Communicable Diseases Committee of FIGO (the International Federation of Gynecology and Obstetrics).

Authors:  Liona C Poon; Laura A Magee; Stefan Verlohren; Andrew Shennan; Peter von Dadelszen; Eyal Sheiner; Eran Hadar; Gerard Visser; Fabricio Da Silva Costa; Anil Kapur; Fionnuala McAuliffe; Amala Nazareth; Muna Tahlak; Anne B Kihara; Hema Divakar; H David McIntyre; Vincenzo Berghella; Huixia Yang; Roberto Romero; Kypros H Nicolaides; Nir Melamed; Moshe Hod
Journal:  Int J Gynaecol Obstet       Date:  2021-07       Impact factor: 4.447

8.  Assessing the Role of Uric Acid as a Predictor of Preeclampsia.

Authors:  Ana I Corominas; Yollyseth Medina; Silvia Balconi; Roberto Casale; Mariana Farina; Nora Martínez; Alicia E Damiano
Journal:  Front Physiol       Date:  2022-01-13       Impact factor: 4.566

9.  sFlt-1/PlGF ratio for prediction of preeclampsia in clinical routine: A pragmatic real-world analysis of healthcare resource utilisation.

Authors:  Anne Dathan-Stumpf; Anna Rieger; Stefan Verlohren; Cyrill Wolf; Holger Stepan
Journal:  PLoS One       Date:  2022-02-24       Impact factor: 3.240

10.  Establishment of a nomogram model for predicting adverse outcomes in advanced-age pregnant women with preterm preeclampsia.

Authors:  Bohan Lv; Yan Zhang; Guanghui Yuan; Ruting Gu; Jingyuan Wang; Yujiao Zou; Lili Wei
Journal:  BMC Pregnancy Childbirth       Date:  2022-03-19       Impact factor: 3.007

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