Prediction of preterm pre‐eclampsia at midpregnancy using a multivariable screening algorithm Article Swipe
Carin Black
,
Daniel L. Rolnik
,
Ahmed Al‐Amin
,
Stefan C. Kane
,
Caroline Stolarek
,
Adrienne White
,
Fabrício da Silva Costa
,
Shaun P. Brennecke
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1111/ajo.13113
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1111/ajo.13113
Background Competing risk models used for midpregnancy prediction of preterm pre‐eclampsia have shown detection rates (DR) of 85%, at fixed false‐positive rate (FPR) of 10%. The full algorithm used between 19 +0 and 24 +6 weeks includes maternal factors, mean arterial pressure (MAP), mean uterine artery pulsatility index (UtAPI), serum placental growth factor (PlGF) level in multiples of the median (MoM), and soluble Fms‐like tyrosine kinase‐1 (sFlt‐1) level in MoM. Aims To assess performance of the Fetal Medicine Foundation (FMF) algorithm at midpregnancy to screen for preterm (<37 weeks) pre‐eclampsia. The outcome measured was preterm pre‐eclampsia. Materials and Methods This is a prospective study including singleton pregnancies at 19–22 weeks gestation. Maternal bloods were collected and analysed using three different immunoassay platforms. Maternal characteristics, medical history, MAP, mean UtAPI, serum PlGF MoM and serum sFlt‐1 MoM were used for risk assessment. DR and FPR were calculated, and receiver operating characteristic curves produced. Results Five hundred and twelve patients were included. Incidence of preterm pre‐eclampsia was 1.6%. Using predicted risk of pre‐eclampsia of one in 60 or more and one in 100 or higher, as given by the FMF predictive algorithm, the combination with the best predictive performance for preterm pre‐eclampsia included maternal factors, MAP, UtAPI and PlGF MoM, giving DRs of 100% and 100%, respectively, and FPRs of 9.3 for all platforms and 12.9–13.5, respectively. Addition of sFlt‐1 to the algorithm did not appear to improve performance. sFlt‐1 MoM and PlGF MoM values obtained on the different platforms performed very similarly. Conclusions Second trimester combined screening for preterm pre‐eclampsia by maternal history, MAP, mean UtAPI and PlGF MoM using the FMF algorithm performed very well in this patient population.
Related Topics
Concepts
Medicine
Eclampsia
Obstetrics
Receiver operating characteristic
Algorithm
Placental growth factor
Gestation
Uterine artery
Pregnancy
Prospective cohort study
Gynecology
Mathematics
Internal medicine
Genetics
Vascular endothelial growth factor
Biology
VEGF receptors
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/ajo.13113
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/ajo.13113
- OA Status
- bronze
- Cited By
- 9
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3009818099
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3009818099Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/ajo.13113Digital Object Identifier
- Title
-
Prediction of preterm pre‐eclampsia at midpregnancy using a multivariable screening algorithmWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-03-02Full publication date if available
- Authors
-
Carin Black, Daniel L. Rolnik, Ahmed Al‐Amin, Stefan C. Kane, Caroline Stolarek, Adrienne White, Fabrício da Silva Costa, Shaun P. BrenneckeList of authors in order
- Landing page
-
https://doi.org/10.1111/ajo.13113Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/ajo.13113Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/ajo.13113Direct OA link when available
- Concepts
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Medicine, Eclampsia, Obstetrics, Receiver operating characteristic, Algorithm, Placental growth factor, Gestation, Uterine artery, Pregnancy, Prospective cohort study, Gynecology, Mathematics, Internal medicine, Genetics, Vascular endothelial growth factor, Biology, VEGF receptorsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1, 2023: 3, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.arterial | 40 |
| abstract_inverted_index.combined | 254 |
| abstract_inverted_index.factors, | 38, 202 |
| abstract_inverted_index.history, | 125, 261 |
| abstract_inverted_index.included | 200 |
| abstract_inverted_index.includes | 36 |
| abstract_inverted_index.maternal | 37, 201, 260 |
| abstract_inverted_index.measured | 92 |
| abstract_inverted_index.obtained | 243 |
| abstract_inverted_index.patients | 157 |
| abstract_inverted_index.pressure | 41 |
| abstract_inverted_index.receiver | 147 |
| abstract_inverted_index.sFlt‐1 | 134, 227, 237 |
| abstract_inverted_index.tyrosine | 64 |
| abstract_inverted_index.Competing | 1 |
| abstract_inverted_index.Incidence | 160 |
| abstract_inverted_index.Materials | 96 |
| abstract_inverted_index.algorithm | 27, 80, 230, 271 |
| abstract_inverted_index.collected | 114 |
| abstract_inverted_index.detection | 13 |
| abstract_inverted_index.different | 119, 246 |
| abstract_inverted_index.included. | 159 |
| abstract_inverted_index.including | 104 |
| abstract_inverted_index.multiples | 56 |
| abstract_inverted_index.operating | 148 |
| abstract_inverted_index.performed | 248, 272 |
| abstract_inverted_index.placental | 50 |
| abstract_inverted_index.platforms | 221, 247 |
| abstract_inverted_index.predicted | 167 |
| abstract_inverted_index.produced. | 151 |
| abstract_inverted_index.screening | 255 |
| abstract_inverted_index.singleton | 105 |
| abstract_inverted_index.trimester | 253 |
| abstract_inverted_index.(sFlt‐1) | 66 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Fms‐like | 63 |
| abstract_inverted_index.Foundation | 78 |
| abstract_inverted_index.algorithm, | 189 |
| abstract_inverted_index.gestation. | 110 |
| abstract_inverted_index.kinase‐1 | 65 |
| abstract_inverted_index.platforms. | 121 |
| abstract_inverted_index.prediction | 7 |
| abstract_inverted_index.predictive | 188, 195 |
| abstract_inverted_index.similarly. | 250 |
| abstract_inverted_index.Conclusions | 251 |
| abstract_inverted_index.assessment. | 140 |
| abstract_inverted_index.calculated, | 145 |
| abstract_inverted_index.combination | 191 |
| abstract_inverted_index.immunoassay | 120 |
| abstract_inverted_index.performance | 73, 196 |
| abstract_inverted_index.population. | 278 |
| abstract_inverted_index.pregnancies | 106 |
| abstract_inverted_index.prospective | 102 |
| abstract_inverted_index.pulsatility | 46 |
| abstract_inverted_index.12.9–13.5, | 223 |
| abstract_inverted_index.midpregnancy | 6, 82 |
| abstract_inverted_index.performance. | 236 |
| abstract_inverted_index.respectively, | 214 |
| abstract_inverted_index.respectively. | 224 |
| abstract_inverted_index.characteristic | 149 |
| abstract_inverted_index.pre‐eclampsia | 10, 163, 170, 199, 258 |
| abstract_inverted_index.characteristics, | 123 |
| abstract_inverted_index.false‐positive | 20 |
| abstract_inverted_index.pre‐eclampsia. | 89, 95 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5016572287 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I1331142282, https://openalex.org/I165779595 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.800000011920929 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.73445313 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |