Individualized decision-making in IVF: calculating the chances of pregnancy.

STUDY QUESTION: Are we able to develop a model to calculate the chances of pregnancy prior to the start of the first IVF cycle as well as after one or more failed cycles? SUMMARY ANSWER: Our prediction model enables the accurate individualized calculation of the probability of an ongoing pregnancy with IVF. WHAT IS KNOWN ALREADY: To improve counselling, patient selection and clinical decision-making in IVF, a number of prediction models have been developed. These models are of limited use as they were developed before current clinical and laboratory protocols were established. STUDY DESIGN, SIZE, DURATION: This was a cohort study. The development set included 2621 cycles in 1326 couples who had been treated with IVF or ICSI between January 2001 and July 2009. The validation set included additional data from 515 cycles in 440 couples treated between August 2009 and April 2011. The outcome of interest was an ongoing pregnancy after transfer of fresh or frozen-thawed embryos from the same stimulated IVF cycle. If a couple became pregnant after an IVF/ICSI cycle, the follow-up was at a gestational age of at least 11 weeks. PARTICIPANTS/MATERIALS, SETTING, METHODS: Women treated with IVF or ICSI between January 2001 and April 2011 in a university hospital. IVF/ICSI cycles were excluded in the case of oocyte or embryo donation, surgically retrieved spermatozoa, patients positive for human immunodeficiency virus, modified natural IVF and cycles cancelled owing to poor ovarian stimulation, ovarian hyperstimulation syndrome or other unexpected medical or non-medical reasons. MAIN RESULTS AND THE ROLE OF CHANCE: Thirteen variables were included in the final prediction model. For all cycles, these were female age, duration of subfertility, previous ongoing pregnancy, male subfertility, diminished ovarian reserve, endometriosis, basal FSH and number of failed IVF cycles. After the first cycle: fertilization, number of embryos, mean morphological score per Day 3 embryo, presence of 8-cell embryos on Day 3 and presence of morulae on Day 3 were also included. In validation, the model had moderate discriminative capacity (c-statistic 0.68, 95% confidence interval: 0.63-0.73) but calibrated well, with a range from 0.01 to 0.56 in calculated probabilities. LIMITATIONS, REASONS FOR CAUTION: In our study, the outcome of interest was ongoing pregnancy. Live birth may have been a more appropriate outcome, although only 1-2% of all ongoing pregnancies result in late miscarriage or stillbirth. The model was based on data from a single centre. WIDER IMPLICATIONS OF THE FINDINGS: The IVF model presented here is the first to calculate the chances of an ongoing pregnancy with IVF, both for the first cycle and after any number of failed cycles. The generalizability of the model to other clinics has to be evaluated more extensively in future studies (geographical validation). Centres with higher or lower success rates could use the model, after recalibration, by adjusting the intercept to reflect the IVF success rates in their centre. STUDY FUNDING/COMPETING INTEREST(S): This project was funded by the NutsOhra foundation (Grant 1004-179). The NutsOhra foundation had no role in the development of our study, in the collection, analysis and interpretation of data; in writing of the manuscript, and in the decision to submit the manuscript for publication. There were no competing interests.

Expanding reproductive lifespan: a cost-effectiveness study on oocyte freezing.

BACKGROUND: The average age of women bearing their first child has increased strongly. This is an important reproductive health problem as fertility declines with increasing female age. Unfortunately, IVF using fresh oocytes cannot compensate for this age-related fertility decline. Oocyte freezing could be a solution. METHODS: We used the Markov model to estimate the cost-effectiveness of three strategies for 35-year-old women who want to postpone pregnancy till the age of 40: Strategy 1: women undergo three cycles of ovarian hyperstimulation at age 35 for oocyte freezing, then at age 40, use these frozen oocytes for IVF; Strategy 2: women at age 40 attempt to conceive without treatment; and the reference strategy: women at age 40 attempt to conceive and, if not pregnant after 1 year, undergo IVF. Sensitivity analyses were carried out to investigate assumptions of the model and to identify which model inputs had most impact on the results. RESULTS: Oocyte freezing (Strategy 1) resulted in a live birth rate of 84.5% at an average cost of €10,419. Natural conception (Strategy 2) resulted in a live birth rate of 52.3% at an average cost of €310 per birth. IVF (the reference strategy) resulted in a cumulative live birth rate of 64.6% at an average cost of €7798. The cost per additional live birth for the oocyte freezing strategy was €13,156 compared to the IVF strategy. If at least 61% of the women return to collect their oocytes, and if there is a willingness to pay €19,560 extra per additional live birth, the oocyte freezing strategy is the most cost-effective strategy. CONCLUSION: Oocyte freezing is more cost effective compared to IVF, if at least 61% of the women return to collect their oocytes and if one is willing to pay €19,560 extra per additional live birth. Our Markov model shows that, considering all the used assumptions, oocyte freezing provides more value for money than IVF.

Templeton prediction model underestimates IVF success in an external validation.

Prediction models for IVF can be used to identify couples that will benefit from IVF treatment. Currently there is only one prediction model with a good predictive performance that can be used for predicting pregnancy chances after IVF. That model was developed almost 15 years ago and since IVF has progressed substantially during the last two decades it is questionable whether the model is still valid in current clinical practice. The objective of this study was to validate the prediction model of Templeton for calculating pregnancy chances after IVF. The performance of the prediction model was assessed in terms of discrimination, i.e. the area under the receiver operation characteristic (ROC) curve and calibration. Likely causes for miscalibration were evaluated by refitting the Templeton model to the study data. The area under the ROC curve for the Templeton model was 0.61. Calibration showed a significant and systematic underestimation of success in IVF. Although the Templeton model can distinguish somewhat between women with a high and low success rate in IVF, it systematically underestimates pregnancy chances and has therefore no real value for current IVF practice.

Long-term LDL-c lowering in heterozygous familial hypercholesterolemia normalizes carotid intima-media thickness.

OBJECTIVE: We investigated the effectiveness of statins in daily practice in reducing the arterial wall thicknesses by comparing the carotid intima-media thickness (cIMT) between statin-treated familial hypercholesterolemia (FH) patients and their unaffected spouses. METHODS: FH subjects treated with LDL-c lowering medication for at least 5 years and their unaffected spouses were included in this observational study. Clinical data and carotid intima-media thickness (cIMT) as surrogate marker for atherosclerosis were acquired. RESULTS: In total 40 FH patients, age 48.4±4.2 years, and their 40 unaffected spouses, age 47.4±3.9 years, were included. Pre-treatment total cholesterol levels of FH patients were on average 9.3±2.0 mmol/L. Treated FH patients and unaffected spouses exhibited similar LDL-c (3.8±1.5 vs. 3.5±1.1 mmol/L; p=0.25) and total cholesterol levels (5.8±1.6 vs. 5.6±1.1 mmol/L; p=0.56). Also, in a multivariate model cIMT adjusted for age and sex did not differ between affected and spouses (95% CI: -0.032 to 0.092 mm; p=0.34). CONCLUSION: Long-term statin treatment normalizes cIMT in severe FH patients and therefore it is likely that the extreme risk of cardiovascular disease in FH patients is significantly reduced by this therapy.

Predictive factors in in vitro fertilization (IVF): a systematic review and meta-analysis.

BACKGROUND: Various models have been developed for the prediction of pregnancy after in vitro fertilization (IVF). These models differ from one another in the predictors they include. We performed a systematic review and meta-analysis to identify the most relevant predictors for success in IVF. METHODS: We systematically searched MEDLINE and EMBASE for studies evaluating IVF/ICSI outcome. Studies were included if they reported an unconditional odds ratio (OR) or whenever one could be calculated for one or more of the following factors: age, type of infertility, indication, duration of infertility, basal FSH, number of oocytes, fertilization method, number of embryos transferred and embryo quality. RESULTS: Fourteen studies were identified. A summary OR could be calculated for five factors. We found negative associations between pregnancy and female age [OR: 0.95, 95% confidence interval (CI): 0.94-0.96], duration of subfertility (OR: 0.99, 95% CI: 0.98-1.00) and basal FSH (OR: 0.94, 95% CI: 0.88-1.00). We found a positive association with number of oocytes (OR 1.04, 95% CI: 1.02-1.07). Better embryo quality was associated with higher pregnancy chances. No significant association was found for the type of infertility and fertilization method. A summary OR for IVF indication and number of embryos transferred could not be calculated, because studies reporting on these used different reference categories. CONCLUSIONS: Female age, duration of subfertility, bFSH and number of oocytes, all reflecting ovarian function, are predictors of pregnancy after IVF. Better quality studies are necessary, especially studies that focus on embryo factors that are predictive of success in IVF.