The POPI tool: prediction model of outcome of pregnancy in IVF (original) (raw)
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F1000Research, 2019
Background: In developed countries, the prevalence of infertility ranges from 3.5% to 16.7%. Therefore, the number of in vitro fertilization technique (IVF) and its subtype intracytoplasmic sperm injection (ICSI) treatments has been significantly increasing across Europe. Several factors affect the success rate of in vitro treatments, which can be used to calculate the probability of success for each couple. As these treatments are complicated and expensive with a variable probability of success, the most common question asked by IVF patients is ‘‘What are my chances of conceiving?”. The main aim of this study is to develop a validated model that estimates the chance of a live birth before they start their IVF non-donor cycle. Methods: A logistic regression model was developed based on the retrospective study of 737 IVF cycles. Each couple was characterized by 14 variables (woman’s and man’s age, duration of infertility, cause of infertility, woman’s and man’s body mass index (BMI),...
External validation of a prediction model for an ongoing pregnancy after intrauterine insemination
Fertility and Sterility, 2007
Objective: To assess the accuracy of our recently developed prediction model in a prospective validation study to predict the outcome of intrauterine insemination (IUI). Design: Descriptive prospective validation study. Setting: Seven fertility centers in the Netherlands. Patient(s): Couples treated with IUI of whom the female partner had a regular cycle. Intervention(s): Intrauterine insemination with or without controlled ovarian hyperstimulation. Main Outcome Measure(s): Ongoing pregnancy after intrauterine insemination. Performance of the prediction model was assessed with calibration and discriminative capacity. Calibration was assessed by comparing the predicted ongoing pregnancy rate with the observed ongoing pregnancy rate. Discriminative capacity was assessed with receiver operation characteristic (ROC) analysis. For daily practice, a score worksheet of the validated model was developed to estimate the chance of an ongoing pregnancy after IUI per treatment cycle. Result(s): We included 1,079 subfertile couples who underwent 4,244 cycles of IUI. There were 278 ongoing pregnancies, that is, an ongoing pregnancy rate of 6.6% per cycle. External validation of the model showed good calibration. The predicted probability never differed by more than 1.5% of the mean observed probability. The area under the ROC curve was 0.56 (95% confidence interval, 0.53-0.59) at external validation. Conclusion(s): The prediction model was able to make a good distinction between couples with a good pregnancy chance and those with a poor pregnancy chance after IUI. This model can help in deciding which couples will benefit from IUI and which couples will not. (Fertil Steril Ò 2007;88:425-31.
Human Reproduction
STUDY QUESTION Can we develop an IVF prediction model to estimate individualized chances of a live birth over multiple complete cycles of IVF in couples embarking on their second complete cycle of treatment? SUMMARY ANSWER Yes, our prediction model can estimate individualized chances of cumulative live birth over three additional complete cycles of IVF. WHAT IS KNOWN ALREADY After the completion of a first complete cycle of IVF, couples who are unsuccessful may choose to undergo further treatment to have their first child, while those who have had a live birth may decide to have more children. Existing prediction models can estimate the overall chances of success in couples before commencing IVF but are unable to revise these chances on the basis of the couple’s response to a first treatment cycle in terms of the number of eggs retrieved and pregnancy outcome. This makes it difficult for couples to plan and prepare emotionally and financially for the next step in their treatment. ST...
Human Reproduction Update, 2008
BACKGROUND:To review the accuracy of multivariate models for the prediction of ovarian reserve and pregnancy in women undergoing IVF compared with the antral follicle count (AFC) as single test. METHODS: We performed a computerized MEDLINE and EMBASE search to identify articles published on multivariate models for ovarian reserve testing in patients undergoing IVF. In order to be selected, articles had to contain data on the outcome of IVF in terms of either pregnancy and/or poor response and on the prediction of these events based on a multivariate model. For the selected studies, sensitivity and specificity of the test in the prediction of poor ovarian response and nonpregnancy were calculated. Overall performance was assessed by estimating a summary receiver operating characteristic (ROC) curve, which was compared with the ROC curve for the AFC as the current best single test. RESULTS: We identified 11 studies reporting on the predictive capacity of multivariate models in ovarian reserve testing. All studies reported on the prediction of poor ovarian response, whereas none reported on the occurrence of pregnancy. The sensitivity for prediction of poor ovarian response varied between 39% and 97% and the specificity between 50% and 96%. Logistic regression analysis indicated that cohort studies provided a significantly better discriminative performance than case-control studies. As cohort studies are superior to case-control studies, further analysis was limited to the cohort studies. For the cohort studies, a summary ROC curve could be estimated, which had a shape similar to that previously made for the AFC. CONCLUSIONS: The accuracy of multivariate models for the prediction of ovarian response in women undergoing IVF is similar to the accuracy of AFC. No data are available on the capacity of these models to predict pregnancy, let alone live birth. On the basis of these findings, the use of more than one single test for the assessment of ovarian reserve cannot currently be supported.
Prediction models in reproductive medicine: a critical appraisal†
Human Reproduction Update, 2009
† Introduction † Methods † Results † Discussion † Supplementary data background: Prediction models have been developed in reproductive medicine to help assess the chances of a treatment-(in)dependent pregnancy. Careful evaluation is needed before these models can be implemented in clinical practice. methods: We systematically searched the literature for papers reporting prediction models in reproductive medicine for three strategies: expectant management, intrauterine insemination (IUI) or in vitro fertilization (IVF). We evaluated which phases of development these models had passed, distinguishing between (i) model derivation, (ii) internal and/or external validation, and (iii) impact analysis. We summarized their performance at external validation in terms of discrimination and calibration. results: We identified 36 papers reporting on 29 prediction models. There were 9 models for the prediction of treatment-independent pregnancy, 3 for the prediction of pregnancy after IUI and 17 for the prediction of pregnancy after IVF. All of the models had completed the phase of model derivation. For six models, the validity of the model was assessed only in the population in which it was developed (internal validation). For eight models, the validity was assessed in populations other than the one in which the model was developed (external validation), and only three of these showed good performance. One model had reached the phase of impact analysis. conclusions: Currently, there are three models with good predictive performance. These models can be used reliably as a guide for making decisions about fertility treatment, in patients similar to the development population. The effects of using these models in patient care have to be further investigated.
IVF Births and Pregnancies: An Exploration of Two Methods of Assessment Using Life-Table Analysis
Purpose: Our purpose was to explore two methods of expressing the performance of IVF programs. Methods: Using life-table methods, hazard and cure rates and a “monthly fecundability rate” were calculated for an Ontario IVF clinic. The rates were evaluated for their meaningfulness as indicators of the clinic’s performance. Results and Conclusions: While the hazard rate describes monthly fertility among those who will eventually become pregnant, the fecundability rate describes fertility for all patients who enter the program, making it the more appropriate index for program comparisons. However, from a prospective patient’s perspective, both methods are valid indices for summarizing a program’s performance.
European Journal of Obstetrics & Gynecology and Reproductive Biology, 2013
Objective: To examine common clinical determinants, including patient age; levels of anti-Mü llerian hormone (AMH), inhibin B, and follicle-stimulating hormone (FSH); antral follicle count (AFC); and number of oocytes retrieved, to predict live births in women undergoing in vitro fertilization. Study design: Women undergoing cycles of intracytoplasmic sperm injection (ICSI) for the first time were reviewed retrospectively, and serum levels of AMH, inhibin B, and FSH, as well as AFC (days 1 and 4 of pre-ICSI menstrual period) and patient age were analyzed as determinants of live birth rates. Results: Of the patients studied, 35.71% (891/2495) became pregnant, with live births achieved in 32.20% (806/2495) of cycles initiated and in 46.37% (806/1738) of embryo transfers. Clinical pregnancy rate was 35.71% (891/2495) for cycles initiated and 51.26% (891/2318) for embryo transfers. Univariate analysis revealed that the odds of live birth significantly decreased with increasing age, declining AMH or inhibin B concentrations, and fewer oocytes retrieved. At AMH levels greater than 5.7 ng/ml, the odds of live birth were 3.18 times greater than for AMH levels less than 1.9 ng/ml [95% confidence interval (CI), 1. 89-5.43]. Using multivariate logistic regression, only AMH (OR = 1.89; 95% CI, 1.00-3.60; p < 0.05) and AFC (OR = 1.86; 95% CI, 1.02-3.40; p < 0.05) showed statistically significant associations with live birth. Area under the curve for ROC (ROC AUC ) indicated that AMH (AUC = 0.60) surpassed AFC (AUC = 0.59), number of oocytes retrieved (AUC = 0.59), inhibin B (AUC = 0.55), FSH (ROC AUC = 0.54) and chronological age (ROC AUC = 0.53) in predicting live birth. Conclusions: In this assessment of various indices (i.e., age; levels of AMH, inhibin B, and FSH; AFC; and quantity of oocytes retrieved) for predicting live births for IVF patients, AMH, AFC and the quantity of oocytes retrieved constituted the most reliable determinants. ß
Fertility and Sterility, 2014
Objective: To develop a model predictive of live-birth rates (LBR) and multiple birth rates (MBR) for an individual considering assisted reproduction technology (ART) using linked cycles from Society for Assisted Reproductive Technology Clinic Outcome Reporting System (SART CORS) for 2004-2011. Design: Longitudinal cohort. Setting: Clinic-based data. Patient(s): 288,161 women with an initial autologous cycle, of whom 89,855 did not become pregnant and had a second autologous cycle and 39,334 did not become pregnant in the first and second cycles and had a third autologous cycle, with an additional 33,598 women who had a cycle using donor oocytes (first donor cycle). Intervention(s): None. Main Outcome Measure(s): LBRs and MBRs modeled by woman's age, body mass index, gravidity, prior full-term births, infertility diagnoses by oocyte source, fresh embryos transferred, and cycle, using backward-stepping logistic regression with results presented as adjusted odds ratios (AORs) and 95% confidence intervals. Result(s): The LBRs increased in all models with prior full-term births, number of embryos transferred; in autologous cycles also with gravidity, diagnoses of male factor, and ovulation disorders; and in donor cycles also with the diagnosis of diminished ovarian reserve. The MBR increased in all models with number of embryos transferred and in donor cycles also with prior full-term births. For both autologous and donor cycles, transferring two versus one embryo greatly increased the probability of a multiple birth (AOR 27.25 and 38.90, respectively). Conclusion(s): This validated predictive model will be implemented on the Society for Assisted Reproductive Technology Web site (www.sart.org) so that patients considering initiating a course of ART can input their data on the Web site to generate their expected outcomes. (Fertil