Evidence map›Paper›PMID 36973721›Full record

ArticleReproductive biology and endocrinology : RB&E2023

Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation.

Eva S van Marion, Esther B Baart, Margarida Santos, Linette van Duijn, Evert J P van Santbrink, Régine P M Steegers-Theunissen, Joop S E Laven, Marinus J C Eijkemans

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Article in Reproductive biology and endocrinology : RB&E, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Eva S van MarionDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre, PO Box 2040, 3000 CA, Rotterdam, the Netherlands. e.vanmarion@erasmusmc.nl.
Esther B BaartDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre, PO Box 2040, 3000 CA, Rotterdam, the Netherlands.
Margarida SantosFertility Center, Reinier de Graaf Hospital, Fonteynenburghlaan 5, 2275 CX, Voorburg, the Netherlands.
Linette van DuijnDepartment of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre, PO Box 2040, 3000 CA, Rotterdam, the Netherlands.
Evert J P van SantbrinkFertility Center, Reinier de Graaf Hospital, Fonteynenburghlaan 5, 2275 CX, Voorburg, the Netherlands.
Régine P M Steegers-TheunissenDepartment of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre, PO Box 2040, 3000 CA, Rotterdam, the Netherlands.
Joop S E LavenDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre, PO Box 2040, 3000 CA, Rotterdam, the Netherlands.
Marinus J C EijkemansDepartment of Data Science and Biostatistics, University Medical Centre, Utrecht University, PO Box 85500, 3508 GA, Utrecht, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe predictive capability of time-lapse monitoring (TLM) selection algorithms is influenced by patient characteristics, type and quality of data included in the analysis and the used statistical methods. Previous studies excluded DET cycles of which only one embryo implanted, introducing bias into the data. Therefore, we wanted to develop a TLM prediction model that is able to predict pregnancy chances after both single- and double embryo transfer (SET and DET).

methodsThis is a retrospective study of couples (n = 1770) undergoing an in vitro fertilization cycle at the Erasmus MC, University Medical Centre Rotterdam (clinic A) or the Reinier de Graaf Hospital (clinic B). This resulted in 2058 transferred embryos with time-lapse and pregnancy outcome information. For each dataset a prediction model was established by using the Embryo-Uterus statistical model with the number of gestational sacs as the outcome variable. This process was followed by cross-validation.

resultsPrediction model A (based on data of clinic A) included female age, t3-t2 and t5-t4, and model B (clinic B) included female age, t2, t3-t2 and t5-t4. Internal validation showed overfitting of model A (calibration slope 0.765 and area under the curve (AUC) 0.60), and minor overfitting of model B (slope 0.915 and AUC 0.65). External validation showed that model A was capable of predicting pregnancy in the dataset of clinic B with an AUC of 0.65 (95% CI: 0.61-0.69; slope 1.223, 95% CI: 0.903-1.561). Model B was less accurate in predicting pregnancy in the dataset of clinic A (AUC 0.60, 95% CI: 0.56-0.65; slope 0.671, 95% CI: 0.422-0.939).

conclusionOur study demonstrates a novel approach to the development of a TLM prediction model by applying the EU statistical model. With further development and validation in clinical practice, our prediction model approach can aid in embryo selection and decision making for SET or DET.

Indexed as

Fertilization in VitroPregnancy OutcomeChild, PreschoolFemaleHumansModels, StatisticalPregnancyPregnancy RateRetrospective StudiesUterusEmbryo transferIn vitro fertilisationPrediction modelStatistical modelsTime-lapse imaging

Identifiers

PMID36973721
PMCPMC10041771

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