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A more accurate prediction to rule in and rule out pre-eclampsia using the sFlt-1/PlGF ratio and NT-proBNP as biomarkers

  • Paula Lafuente-Ganuza , Paloma Lequerica-Fernandez , Francisco Carretero , Ana I. Escudero , Eduardo Martinez-Morillo , Enric Sabria , Ignacio Herraiz , Alberto Galindo , Ana Lopez , Maria L. Martinez-Triguero and Francisco V. Alvarez EMAIL logo
Published/Copyright: November 16, 2019

Abstract

Background

The management of potential pre-eclamptic patients using the soluble FMS-like tyrosine kinase 1 (sFlt-1)/ placental growth factor (PlGF) ratio is characterised by frequent false-positive results.

Methods

A retrospective cohort study was conducted to identify and validate cut-off values, obtained using a machine learning model, for the sFlt-1/PlGF ratio and NT-proBNP that would be predictive of the absence or presence of early-onset pre-eclampsia (PE) in singleton pregnancies presenting at 24 to 33 + 6 weeks of gestation.

Results

For the development cohort, we defined two sFlt-1/PlGF ratio cut-off values of 23 and 45 to rule out and rule in early-onset PE at any time between 24 and 33 + 6 weeks of gestation. Using an sFlt-1/PlGF ratio cut-off value of 23, the negative predictive value (NPV) for the development of early-onset PE was 100% (95% confidence interval [CI]: 99.5–100). The positive predictive value (PPV) of an sFlt-1/PlGF ratio >45 for a diagnosis of early-onset PE was 49.5% (95% CI: 45.8–55.6). When an NT-proBNP value >174 was combined with an sFlt-1/PlGF ratio >45, the PPV was 86% (95% CI: 79.2–92.6). In the validation cohort, the negative and positive values were very similar to those found for the development cohort.

Conclusions

An sFlt-1/PlGF ratio <23 rules out early-onset PE between 24 and 33 + 6 weeks of gestation at any time, with an NPV of 100%. An sFlt-1/PlGF ratio >45 with an NT-proBNP value >174 significantly enhances the probability of developing early-onset PE.

Acknowledgments

The authors greatly appreciate all of the collaborators from different hospitals as well as the Catedra de Inteligencia Analitica, Universidad de Oviedo, Oviedo, Spain.

  1. Author contributions: P. Lafuente, P. Lequerica, and E. Martinez: statistical analysis and clinical characteristics of the patients; A. Galindo, I. Herraiz, A. Escudero, M. Martinez and E. Lopez: provision of study material or patients; F. Carretero: statistical analysis using machine learning; F.V. Alvarez: conceived and designed the study. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: I. Herraiz and A. Galindo have received lecture fees and consultancy payments from Roche Diagnostics. The rest of the authors report no conflict of interest.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2019-0939).


Received: 2019-09-05
Accepted: 2019-10-12
Published Online: 2019-11-16
Published in Print: 2020-02-25

©2020 Walter de Gruyter GmbH, Berlin/Boston

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