Fertility and sterility · 2022
A personalized medicine approach to ovulation induction/ovarian stimulation: development of a predictive model and online calculator from level-I evidence.
СтатьяМногоцентровое исследованиеПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Souter Irene, Sun Fangbai, Zhang Heping, Diamond Michael P, Legro Richard S, Wild Robert A, Hansen Karl R, Santoro Nanette, Eunice Kennedy Schriver National Institute of Child Health and Human Development Reproductive Medicine Network
Аннотация
OBJECTIVE: To estimate the probability of clinical or multiple pregnancy during ovulation induction (OI)/ovarian stimulation (OS).
DESIGN: Secondary analysis of two multicenter randomized clinical trials (combined).
SETTING: Multicenter.
PATIENTS: A total of 750 women with polycystic ovary syndrome and 900 women with unexplained infertility.
INTERVENTIONS: Ovulation induction/OS with either timed intercourse (polycystic ovary syndrome) or intrauterine insemination.
MAIN OUTCOME MEASURES: Clinical and multiple pregnancy rates/cycle, cumulative pregnancy rates. Age, body mass index, parity, diagnosis, medication, markers of ovarian reserve, and ovarian response were considered in multivariable regression models for clinical, multiple, and cumulative pregnancy rates. Receiver operating characteristic curves were created for clinical and multiple pregnancy rates.
RESULTS: Younger patient and partner age, treatment type, lower body mass index, and medication dose were all associated with clinical pregnancy. Variables associated with multiple pregnancy included the abovementioned variables (except age), in addition to diagnosis, parity, higher antral follicle count, antimüllerian hormone levels, and ovarian response. Gonadotropin use was associated with multiple pregnancy, with progressively increasing odds ratios (cycles 1-4). Receiver operating characteristic curves indicated the model's predictive power to be fair for clinical pregnancy (areas under the curve [95% confidence interval {CI}]: 0.78 [0.75-0.81] for cycle 1 and 0.70 [0.64-0.75] for cycle 4) and good-to-excellent for multiple pregnancy (areas under the curve [95% CI]: 0.78 [0.72-0.84] for cycle 1 and 0.86 [0.78-0.93] for cycle 4). Partner age, lower medication dose, parity, antimüllerian hormone levels, and diagnosis were associated with cumulative pregnancy rates.
CONCLUSIONS: Using the majority of the factors known to predict the outcome of OI/OS cycles, we constructed an easy-to-use formula that may predict individualized chances of clinical and multiple pregnancy for commonly used fertility treatments (https://pregnancyprediction.medicine.yale.edu/CalDirect.html).
CLINICAL TRIAL REGISTRATION NUMBERS: Assessing Multiple Intrauterine Gestations after Ovulation Stimulation NCT01044862; PPCOSII NCT00719186.
DESIGN: Secondary analysis of two multicenter randomized clinical trials (combined).
SETTING: Multicenter.
PATIENTS: A total of 750 women with polycystic ovary syndrome and 900 women with unexplained infertility.
INTERVENTIONS: Ovulation induction/OS with either timed intercourse (polycystic ovary syndrome) or intrauterine insemination.
MAIN OUTCOME MEASURES: Clinical and multiple pregnancy rates/cycle, cumulative pregnancy rates. Age, body mass index, parity, diagnosis, medication, markers of ovarian reserve, and ovarian response were considered in multivariable regression models for clinical, multiple, and cumulative pregnancy rates. Receiver operating characteristic curves were created for clinical and multiple pregnancy rates.
RESULTS: Younger patient and partner age, treatment type, lower body mass index, and medication dose were all associated with clinical pregnancy. Variables associated with multiple pregnancy included the abovementioned variables (except age), in addition to diagnosis, parity, higher antral follicle count, antimüllerian hormone levels, and ovarian response. Gonadotropin use was associated with multiple pregnancy, with progressively increasing odds ratios (cycles 1-4). Receiver operating characteristic curves indicated the model's predictive power to be fair for clinical pregnancy (areas under the curve [95% confidence interval {CI}]: 0.78 [0.75-0.81] for cycle 1 and 0.70 [0.64-0.75] for cycle 4) and good-to-excellent for multiple pregnancy (areas under the curve [95% CI]: 0.78 [0.72-0.84] for cycle 1 and 0.86 [0.78-0.93] for cycle 4). Partner age, lower medication dose, parity, antimüllerian hormone levels, and diagnosis were associated with cumulative pregnancy rates.
CONCLUSIONS: Using the majority of the factors known to predict the outcome of OI/OS cycles, we constructed an easy-to-use formula that may predict individualized chances of clinical and multiple pregnancy for commonly used fertility treatments (https://pregnancyprediction.medicine.yale.edu/CalDirect.html).
CLINICAL TRIAL REGISTRATION NUMBERS: Assessing Multiple Intrauterine Gestations after Ovulation Stimulation NCT01044862; PPCOSII NCT00719186.
Предметные рубрики MeSH
AdultBody Mass IndexClinical Decision-MakingCoitusDecision Support TechniquesFemaleFertilityFertility Agents, FemaleHumansInfertility, FemaleInsemination, ArtificialMaternal AgeOvulation InductionPolycystic Ovary SyndromePrecision MedicinePregnancyPregnancy RateRandomized Controlled Trials as TopicRisk AssessmentRisk FactorsTreatment Outcome