Comparison of predictive models for cumulative live birth rate after treatment with ART

Bardet, Léna; Excoffier, Jean-Baptiste; Salaün-Penquer, Noémie; Ortala, Matthieu; Pasquier, Maud; d’Argent, Emmanuelle Mathieu; Massin, Nathalie

doi:10.1016/j.rbmo.2022.03.020

Cited by 5 publications

(6 citation statements)

References 26 publications

(26 reference statements)

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“…Women who were younger than 40 years old, with a BMI less than 25 were higher pregnancy after IUI treatment (10). Abnormal sperm morphology and reduced sperm motility were prognostic factors for lower pregnancy rate of IUI (18-20). Pregnancies were increased with increased thickness in endometrium (21, 22).…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, the random forest, PLS and CNFE-SE models have been used in predicting clinical outcomes of IUI (18)(19)(20). However, these predictions can be improved by including a scoring system model for IUI.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence (AI), for example, including convolutional neural network (CNN), has already been widely used in enhancing diagnosis and treatment for various diseases (11-14); and random forest model and artificial neural network (ANN) have been used for improving accuracy in grading collected data (15-17). Indeed, the random forest, PLS and CNFE-SE models have been used in predicting clinical outcomes of IUI (18-20). However, these predictions can be improved by including a scoring system model for IUI.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Diagnosis Grading System for Patients Undergoing Intrauterine Inseminations: A Machine-learning Perspective

Jin

zong

Xue

2022

Preprint

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ObjectiveTo develop an innovative, non-invasive and objective grading system for enhancing clinicians’ assessment of intrauterine insemination (IUI) patients.DesignPatients who had undergone IUI treatments previously were divided into pregnant (N=4618) and non-pregnant(N=20974) groups. An evaluation index method was developed using collected clinical data from the two groups, particularly on indications of considerable differences between the two groups. The weight of each indicator was determined using random forest machine learning, and the indicators and patients’ conditions were classified using an entropy-based feature discretization technique. The indices for each indicator were further divided into five grades, from A to E, and given five points to one point, respectively. Effectiveness of the system was tested using the ten-fold cross-validation method.SettingReproductive medicine center, Shanghai, ChinaPatientsInfertile couples who had undergone IUI treatment.InterventionNone.Main Outcome MeasuresWeight of each indicator and grades of infertile patients.ResultsFrom the 25,592 medical records of infertile couples who had IUI, 4618 women were pregnant subsequently, with a mean age of 28.69±3.34 years. From the collected records, 18 indicators (e.g., body mass indices [BMI], endometrial thicknesses, couples’ ages, IUI cycle days, and semen situations) were selected to construct our diagnosis scoring system. Among the 18 indicators, BMI (weight, 12.49%), endometrial thickness (11.99%), female age (11.88%), semen density (10.41%), semen volume (8.92%), cycle day (7.38%) and male age (6.96%) were closely related to the pregnancy rates. Among patients with the final scores for > 75.29 individually, the pregnancy rates for them was > 56.35%. The system’s stability was 95.1% (95%CI,94.5%-95.7%) according to cross-validation data.ConclusionThis quick and objective machine learning-based approach can be used to simplify and enhance the decision-making process among clinicians, especially to advise and to select patients for better IUI outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of a Diagnosis Grading System for Patients Undergoing Intrauterine Inseminations: A Machine-learning Perspective

Jin

zong

Xue

2022

Preprint

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“…Automated algorithm can be used to develop diagnostic or prognostic models based on these correlations and establish visually appealing and highly accurate clinical prediction models 12 . In the fields of assisted reproductive technology (ART), predictive models have primarily been applied in the selection of embryos and prediction of pregnancy outcomes following in vitro fertilization (IVF) 13 . Nevertheless, only a limited number of prognostic models, some of which has disregarded immunological factors and exhibited poor performance, have specifically focused on pregnancy outcomes in URPL patients.…”

Section: Introductionmentioning

confidence: 99%

Immunological parameters of maternal peripheral blood as predictors of future pregnancy outcomes in patients with unexplained recurrent pregnancy loss

Li,

Wu,

Wang

et al. 2024

Acta Obstet Gynecol Scand

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IntroductionUnexplained recurrent pregnancy loss (URPL), affecting approximately 1%–5% of women, exhibits a strong association with various maternal factors, particularly immune disorders. However, accurately predicting pregnancy outcomes based on the complex interactions and synergistic effects of various immune parameters without an automated algorithm remains challenging.Material and methodsIn this historical cohort study, we analyzed the medical records of URPL patients treated at Xiangya Hospital, Changsha, China, between January 2020 and October 2022. The primary outcomes included clinical pregnancy and miscarriage. Predictors included complement, autoantibodies, peripheral lymphocytes, immunoglobulins, thromboelastography findings, and serum lipids. Least absolute shrinkage and selection operator (LASSO) analysis and logistic regression analysis was performed for model development. The model's performance, discriminatory, and clinical applicability were assessed using area under the curve (AUC), calibration curve, and decision curve analysis, respectively. Additionally, models were visualized by constructing dynamic and static nomograms.ResultsIn total, 502 patients with URPL were enrolled, of whom 291 (58%) achieved clinical pregnancy and 211 (42%) experienced miscarriage. Notable differences in complement, peripheral lymphocytes, and serum lipids were observed between the two outcome groups. Moreover, URPL patients with elevated peripheral NK cells (absolute counts and proportion), decreased complement levels, and dyslipidemia demonstrated a significantly increased risk of miscarriage. Four models were developed in this study, of which Model 2 demonstrated superior performance with only seven predictors, achieving an AUC of 0.96 (95% CI: 0.93–0.99) and an accuracy of 0.92. A web‐based platform was established to visually present model 2 and to facilitate its utilization by clinicians in outpatient settings (available from: https://yingrongli.shinyapps.io/liyingrong/).ConclusionsOur findings suggest that the implementation of such prediction models could serve as valuable tools for providing comprehensive information and facilitating clinicians in their decision‐making processes.

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“…For lesbian couples, both intrauterine insemination (IUI) and in vitro fertilization (IVF) with donated sperm are viable options. The former is simpler, cheaper, requires less medication and is associated with higher risks; the later, however, has higher rates of success and allows the creation of surplus embryos which can be used in future treatments in case of treatment failure or desire for a second child ( Bardet et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Reproductive plans and knowledge of assisted reproductive techniques among lesbian women: an international survey study

Brandão

Zadykowicz

Miscioscia³

et al. 2023

JBRA

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Objective Lesbian couples must resort to adoption or donated semen to achieve parenthood, the latter usually involving assisted reproductive technology. The aim of this study is to assess homosexual women’s knowledge about assisted reproductive techniques, the importance of perceived genetic and gestational relationships for their future mother-child bond, as well as their reproductive plans. Methods This is an observational study based on an anonymous survey disseminated online in several countries on different continents, addressed to homosexual women. Results From the 549 participants, most reported being well informed about reproductive options including assisted reproductive technology. The majority want to be a mother as part of a couple, mainly through assisted reproduction or step adoption of their partner’s child. The importance of a genetic or gestational relationships with their future child varies greatly between women. Among the sampled women, pregnancy was believed to have a slightly greater impact on the future mother-child connection compared to genetics. Conclusions Homosexual women are well informed about the assisted reproductive technology treatments. The majority considers it important to become a mother as a couple, mainly through assisted reproduction or step adoption of their partner’s child. The importance given to gestation or genetic mother-child relationships varies greatly between women, and it seems they believe pregnancy may have a slightly greater impact on the future mother-child connection compared to genetics.

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Comparison of predictive models for cumulative live birth rate after treatment with ART

Cited by 5 publications

References 26 publications

Development of a Diagnosis Grading System for Patients Undergoing Intrauterine Inseminations: A Machine-learning Perspective

Development of a Diagnosis Grading System for Patients Undergoing Intrauterine Inseminations: A Machine-learning Perspective

Immunological parameters of maternal peripheral blood as predictors of future pregnancy outcomes in patients with unexplained recurrent pregnancy loss

Reproductive plans and knowledge of assisted reproductive techniques among lesbian women: an international survey study

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