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Enregistrement W4381734045 · doi:10.1093/humrep/dead093.101

O-087 Patient-centric, machine learning (ML)-based personalised prognostics supports fertility specialists to improve access to assisted reproductive technology (ART) and increase overall live birth (LB) outcomes

2023· article· en· W4381734045 sur OpenAlexaffabout
Yao Mu, Elsie T. Nguyen, Matthew G. Retzloff, Ken Cadesky, L. April Gago, Susannah D. Copland, John E. Nichols, J. F. Payne, Barry A. Ripps, Mary Peavey, J Meriano, Barry W. Donesky, Joseph S. Bird, Jeremy M. Groll, X Chen, David K. Walmer, Tara Swanson, Marco Menabrito

Notice bibliographique

RevueHuman Reproduction · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueAssisted Reproductive Technology and Twin Pregnancy
Établissements canadiensCReATe Fertility CentreTranslational Research in Oncology
Organismes subventionnairesnon disponible
Mots-clésPrognosticsAssisted reproductive technologyFertilityInfertilityReproductive medicineCohortMedicineComputer sciencePopulationPregnancyEnvironmental healthData mining

Résumé

récupéré en direct d'OpenAlex

Abstract Study question Does the use of patient-centric, ML-prognostics counselling report (Univfy® PreIVF Report) affect assisted reproductive technology (ART) conversion (first ART cycle usage) and LB rate (LBR)? Summary answer The use of patient-centric, ML-prognostics counselling report (Univfy® PreIVF Report) by fertility specialists is associated with higher ART conversion and LBR among new patients. What is known already ART is a highly effective and safe treatment for clinical infertility. However, ART remains vastly underutilised resulting in missed opportunities to help more people build families. Commonly used age-based trends often do not address patients' perceived risks including their own ART success probability and ART cost burden as related to their personalised LB probabilities. We previously reported the use of artificial intelligence (AI)/ML to generate patient-centric counselling reports based on ART success prediction models developed and validated for each fertility centre to address their local patient populations in ways that are personalised, relevant and actionable. Study design, size, duration Retrospective cohort analysis. Eight fertility centres from 22 locations across 9 states (US) and Ontario, Canada contributed to the research design, compilation of outcomes data, and interpretation of results. Five centres provided ART utilisation and outcomes data for 15,289 new patients seen in each centre's study period when the Univfy® PreIVF Report was available and data were submitted for aggregated research analysis. Each centre provided 4-6 years of data within the period 2016-2022. Participants/materials, setting, methods The effect of Univfy or No-Univfy Group on ART conversion was analyzed by Chi square tests using aggregated data and separately for each centre's data, for 3 timed analyses, 180-Day, 360-Day and “Ever” (no restriction) after new patient visit. Patients who received the Univfy® PreIVF Report prior to IUI or ART conversion, or had no such conversion after receiving it were placed into the Univfy Group. The No-Univfy Group comprises patients who did not receive a report. Main results and the role of chance Univfy report usage was associated with higher conversions to Direct-ART (by 2.6-, 2.4-, 1.9-folds) and Any-ART (by 2.9-, 3.0-, 2.4-folds) in the aggregated data when analyzed for 180-Day, 360-Day and Ever, respectively; p-value < 0.001. Direct-ART is ART conversion without prior IUI(s); Any-ART conversion includes ART conversion with or without prior IUI(s). In the centre-specific analyses, the fold increase in Direct-ART and Any-ART conversions ranged from 1.8 to 4.5 and 2.2 to 4.7, respectively, in the 360-Day period; p-value <0.001. Univfy® PreIVF Report usage was associated with an increase in estimated LBR ranging from 2.1 to 1.3 folds for the Univfy Group compared to No-Univfy Group (360-Day analysis, p < 0.001) based on conservative versus liberal scenarios. Similar ART conversion and LBR results were observed for 180-Day and Ever analyses, p < 0.001. We used conservative to liberal assumptions for IUI-LBR and NC-LBR because IUI and NC outcomes were not readily available. (Conservative: IUI-LBR 15%, natural conception (NC)-LBR 5%; Liberal: IUI-LBR 25%, NC-LBR 20%). Estimated LBR for ART used clinical ongoing pregnancies and documented live births as LBs and the following LBR assumptions: freeze-all with no transfers yet (50%); gestational carrier ART (45%); ART with unknown outcomes (0%). Limitations, reasons for caution This study was not prospective or randomised. The intended report usage was to support physicians when counselling patients. Although we observed comparable report and ART usage across predicted ART-LB probabilities, there is potential unintentional bias towards higher report or ART utilisation among patients with more favorable clinical characteristics. Wider implications of the findings These results represent our retrospective experience in diverse geographies in North America. We endeavor to collaborate with additional centres to test the reproducibility of AI/ML-driven, validated personalized IVF prognostics on improved overall live birth outcomes and ART access when counselling patients about treatment options. Trial registration number not applicable

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,007
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,043
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,007
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,026
Tête enseignante GPT0,303
Écart entre enseignants0,277 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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