International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025
A new accurate model to assess intrauterine insemination success based on clinical parameters: Optimizing fertility treatment.
СтатьяНазвание и аннотация: Перевод в очереди
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Ejzenberg Dani, Callado Gustavo Yano, de Oliveira Gomes Tiago Jose, Cavalcanti Giovanna Santos, Soares Jose Maria, Baracat Edmund Chada, Monteleone Pedro Augusto
Аннотация
OBJECTIVE: To develop a predictive model for intrauterine insemination (IUI) success based on known clinical parameters.
METHODS: We created an application designed to calculate the probability of IUI success, utilizing data from couples who underwent fertility treatment. The model was developed through a retrospective analysis of clinical and laboratory variables. The main outcome was positive beta human chorionic gonadotrophin (β-hCG) test. The variables included in the model, determined through a multivariate analysis using stepwise regression, encompassed the etiology of infertility, FSH levels in the early proliferative phase, duration of infertility, total sperm count in the ejaculate, and Kruger morphology.
RESULTS: The study included data from 299 couples seeking fertility treatment at the Hospital das Clínicas of the University of São Paulo between 2008 and 2016, involving a total of 355 insemination cycles. All patients underwent IUI treatment for infertility. Various clinical and laboratory variables were examined to build the predictive model for IUI success. The model demonstrated high sensitivity (89.8%), specificity (88.2%), and an area under the ROC curve of 0.946 (95% CI: 0.910-0.982), indicating an overall accuracy of 94.6%. The Hosmer-Lemeshow test yielded a p value of 0.036. This calculator offers healthcare professionals the means to assess the suitability of IUI for individual patients, providing them with tailored guidance.
CONCLUSION: By enhancing the accuracy of IUI success predictions, this model has the potential to optimize the allocation of healthcare resources, alleviate patient stress and disappointment, and ultimately enhance patient outcomes within the field of infertility treatment.
METHODS: We created an application designed to calculate the probability of IUI success, utilizing data from couples who underwent fertility treatment. The model was developed through a retrospective analysis of clinical and laboratory variables. The main outcome was positive beta human chorionic gonadotrophin (β-hCG) test. The variables included in the model, determined through a multivariate analysis using stepwise regression, encompassed the etiology of infertility, FSH levels in the early proliferative phase, duration of infertility, total sperm count in the ejaculate, and Kruger morphology.
RESULTS: The study included data from 299 couples seeking fertility treatment at the Hospital das Clínicas of the University of São Paulo between 2008 and 2016, involving a total of 355 insemination cycles. All patients underwent IUI treatment for infertility. Various clinical and laboratory variables were examined to build the predictive model for IUI success. The model demonstrated high sensitivity (89.8%), specificity (88.2%), and an area under the ROC curve of 0.946 (95% CI: 0.910-0.982), indicating an overall accuracy of 94.6%. The Hosmer-Lemeshow test yielded a p value of 0.036. This calculator offers healthcare professionals the means to assess the suitability of IUI for individual patients, providing them with tailored guidance.
CONCLUSION: By enhancing the accuracy of IUI success predictions, this model has the potential to optimize the allocation of healthcare resources, alleviate patient stress and disappointment, and ultimately enhance patient outcomes within the field of infertility treatment.