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Enregistrement W4287974549

Noninvasive Fetal RhD Blood Group Genotyping: A Health Technology Assessment.

2020· review· en· W4287974549 sur OpenAlexaboutno aff

Notice bibliographique

RevuePubMed · 2020
Typereview
Langueen
DomaineMedicine
ThématiqueBlood groups and transfusion
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineGenotypingPregnancySystematic reviewPediatricsFamily medicineObstetricsEmergency medicineMEDLINE
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: RhD blood group incompatibility during pregnancy can cause serious health problems for the fetus. Noninvasive fetal RhD blood group genotyping is a test for fetal RhD status that may help prevent unnecessary preventive treatment (Rh immunoglobulin [RhIG] injections) and intensive pregnancy monitoring. We conducted a health technology assessment of noninvasive fetal RhD blood group genotyping for RhD-negative (RhD-) pregnancies. Our assessment evaluated the test's diagnostic accuracy, clinical utility, and cost-effectiveness, the budget impact of publicly funding this test, and patients' and providers' preferences and values. METHODS: We performed a systematic literature search of the clinical and economic evidence to conduct an overview of reviews for test accuracy, a systematic review for clinical utility, and a review of the test's cost-effectiveness compared with usual care. We assessed the risk of bias of each included systematic review and study using the ROBIS and RoBANs tools, respectively. We assessed the quality of the body of clinical evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We developed probabilistic Markov microsimulation models to determine the cost-effectiveness and cost-utility of noninvasive fetal RhD genotyping compared with usual care from the Ontario Ministry of Health perspective. We also estimated the 5-year budget impact of publicly funding this test in Ontario. To examine patient and provider preferences related to noninvasive fetal RhD genotyping, we conducted a literature survey of quantitative studies on preference; the Canadian Agency for Drugs and Technologies in Health (CADTH) performed a review of qualitative literature about patient preferences; and we conducted interviews and an online survey with Ontario patients. RESULTS: We included six systematic reviews in the overview of reviews on diagnostic test accuracy and 11 studies in the clinical utility review. Across systematic reviews, test accuracy was high for noninvasive fetal RhD genotyping. The evidence suggests that implementation of noninvasive fetal RhD genotyping may lead to avoidance of unnecessary RhIG prophylaxis (GRADE: Low), good compliance with targeted RhIG prophylaxis (GRADE: Very low), and high uptake of genotyping (GRADE: Low). Alloimmunization may not increase when using noninvasive fetal RhD genotyping to target prenatal RhIG prophylaxis (GRADE: Very low), and may allow unnecessary monitoring and invasive procedures to be avoided in alloimmunized pregnancies (GRADE: Very low).We included eight published economic studies that reported inconsistent results regarding the cost-effectiveness of noninvasive fetal RhD genotyping. In nonalloimmunized RhD- pregnancies, compared with usual care, the intervention identified more maternal alloimmunization cases (probability: 0.0022 vs. 0.0020) and was associated with a reduced number of RhIG injections per pregnancy (1.79 vs 1.43). It was more expensive ($154, 95% credible interval [CrI] $139 to $169) but had little impact on the QALYs of newborns followed over a 10-year time horizon (0.0007, 95% CrI -0.01 to 0.01). The cost of noninvasive fetal RhD genotyping and inclusion of paternal RhD typing were drivers of the cost-effectiveness results in this population. In alloimmunized RhD- pregnancies, noninvasive fetal RhD genotyping was associated with lower resource use during the pregnancy. Compared with usual care, it was less costly (-$6,280, 95% CrI -$6,325 to -$6,229) and more effective (0.19 QALYs, 95% CrI 0.17 to 0.20).The annual budget impact of publicly funding noninvasive fetal RhD genotyping in nonalloimmunized RhD- pregnancies in Ontario ranges from $2.6 million in year 1 (uptake of 80%) to $3.4 million in year 5 (uptake of 100%), with a 5-year total of about $14.8 million. In alloimmunized pregnancies, we estimate cost savings, from about $9 million in year 1 to about $12 million in year 5, with 5-year total savings of about $51.5 million.We included two studies in the survey of quantitative preferences literature. In the quantitative literature, RhD- pregnant people support routine offering of noninvasive fetal RhD genotyping as part of pregnancy care, with a preference to be adequately informed about the test process, attributes, timing, and risks in advance of the test, ideally in a dialogue with their health care provider. More than half of obstetric health care providers were supportive of offering the test. The qualitative review by CADTH and our own engagement with Ontario patients yielded similar results. Participants consistently expressed a desire for more information about the test and assurance about its safety. They also consistently mentioned the prevention of unnecessary monitoring and treatment as potential benefits. CONCLUSIONS: Noninvasive fetal RhD blood group genotyping is an accurate test to determine RhD incompatibility and guide management of RhD- pregnancies. Compared with usual care, noninvasive fetal RhD genotyping is less costly and more effective for the management of alloimmunized pregnancies. For nonalloimmunized pregnancies, noninvasive fetal RhD genotyping would generally not be considered cost-effective, compared with usual care, unless the cost of testing is much lower than what is proposed now. Publicly funding noninvasive fetal RhD genotyping for guiding the management of RhD- pregnancies in Ontario over next 5 years is associated with a total budget impact of about $15 million in nonalloimmunized pregnancies and total cost savings of about $51 million in alloimmunized pregnancies. Patients and providers indicated support for the routine use of noninvasive fetal RhD genotyping in RhD- pregnancies.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
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: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,988
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
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 Opus0,052
Tête enseignante GPT0,320
Écart entre enseignants0,268 · 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'étudeSans objet
Domainenon disponible
GenreSynthèse

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

En bref

Citations13
Publié2020
Routes d'admission1
Résumé présentoui

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