Cost-effectiveness of 'immediate IVF' versus 'delayed IVF': a prospective study.

STUDY QUESTION: How does the cost-effectiveness (CE) of immediate IVF compared with postponing IVF for 1 year, depend on prognostic characteristics of the couple? SUMMARY ANSWER: The CE ratio, i.e. the incremental costs of immediate versus delayed IVF per extra live birth, is the highest (range of €15 000 to >€60 000) for couples with unexplained infertility and for them depends strongly on female age and the duration of infertility, whilst being lowest for endometriosis (range 8000-23 000) and, for such patients, only slightly dependent on female age and duration of infertility. WHAT IS KNOWN ALREADY: A few countries have guidelines for indications of IVF, using the diagnostic category, female age and duration of infertility. The CE of these guidelines is unknown and the evidence base exists only for bilateral tubal occlusion, not for the other diagnostic categories. STUDY DESIGN, SIZE, DURATION: A modelling approach was applied, based on the literature and data from a prospective cohort study among couples eligible for IVF or ICSI treatment, registered in a national waiting list in The Netherlands between January 2002 and December 2003. PARTICIPANTS/MATERIALS, SETTING, METHODS: A total of 5962 couples was included. Chances of natural ongoing pregnancy were estimated from the waiting list observations and chances of ongoing pregnancy after IVF from follow-up data of couples with primary infertility that began treatment. Prognostic characteristics considered were female age, duration of infertility and diagnostic category. Costs of IVF were assessed from a societal perspective and determined on a representative sample of patients. A cost-effectiveness comparison was made between two scenarios: (I) wait one more year and then undergo IVF for 1 year and (II) immediate IVF during 1 year, and try to conceive naturally in the following year. Comparisons were made for strata determined by the prognostic factors. The final outcome was a live birth. MAIN RESULTS AND THE ROLE OF CHANCE: The gain in live birth rate of the immediate IVF scenario versus postponed IVF increased with female age, and was independent from diagnostic category or duration of infertility. By contrast, the corresponding increase in costs primarily depended on diagnostic category and duration of infertility. The lowest CE ratio was just below €10 000 per live birth for endometriosis from age 34 onwards at 1 year duration. The highest CE ratio reached €56 000 per live birth for unexplained infertility at age 30 and 3 years duration, dropping to values below € 30 000 per live birth from age 32 onwards. It reached values below €20 000 per live birth with 3 years duration at age 34 and older. The CE ratio was in between for the three other diagnostic categories (i.e. Male infertility, Hormonal and Immunological/Cervical). LIMITATIONS, REASONS FOR CAUTION: We applied estimates of chances with IVF, excluding frozen embryos, for which we had no data. Therefore, we do not know the effect of frozen embryo transfers on the CE. WIDER IMPLICATIONS OF THE FINDINGS: The duration of infertility at which IVF becomes cost-effective depends, firstly, on the level of society's willingness to pay for one extra live birth, and secondly, given a certain level of willingness to pay, on the woman's age and the diagnostic category. In current guidelines, the chances of a natural conception should always be taken into account before deciding whether to start IVF treatment and at which time. STUDY FUNDING/COMPETING INTEREST(S): Supported by Netherlands Organisation for Health Research and Development (ZonMW, grant 945-12-013). ZonMW had no role in designing the study, data collection, analysis and interpretation of data or writing of the report. Competing interests: none.

Comparison of two models predicting IVF success; the effect of time trends on model performance.

STUDY QUESTION: How well does the recently developed UK model predicting the success rate of IVF treatment (the 2011 Nelson model) perform in comparison with a UK model developed in the early 1990s (the Templeton model)? SUMMARY ANSWER: Both models showed similar performance, after correction for the increasing success rate over time of IVF. WHAT IS KNOWN ALREADY: For counselling couples undergoing IVF treatment it is of paramount importance to be able to predict success. Several prediction models for the chance of success after IVF treatment have been developed. So far, the Templeton model has been recommended as the best approach after having been validated in several independent patient data sets. The Nelson model, developed in 2011 and characterized by the largest development sample containing the most recently treated couples, may well perform better. STUDY DESIGN, SIZE, DURATION: We tested both models in couples that were included in a national cohort study carried out in the Netherlands between the beginning of January 2002 and the end of December 2004. PARTICIPANTS/MATERIALS, SETTING, METHODS: We analysed the IVF cycles of Dutch couples with primary infertility (n = 5176). The chance of success was calculated using the two UK models that had been developed using the information collected in the Human Fertilisation and Embryology Authority database. Women were treated in 1991-1994 (Templeton) or 2003-2007 (Nelson). The outcome of success for both UK models is the occurrence of a live birth after IVF but the outcome in the Dutch data is an ongoing pregnancy. In order to make the outcomes compatible, we used a factor to convert the chance of live birth to ongoing pregnancy and use the overall terms 'success or no success after IVF'. The discriminative ability and the calibration of both models were assessed, the latter before and after adjustment for time trends in IVF success rates. MAIN RESULTS AND THE ROLE OF CHANCE: The two models showed a similarly limited degree of discriminative ability on the tested data (area under the receiver operating characteristic curve 0.597 for the Templeton model and 0.590 for the Nelson model). The Templeton model underestimated the success rate (observed 21% versus predicted 14%); the Nelson model overestimated the success rate (observed 21% versus predicted 29%). When the models were adjusted for the changing success rates over time, the calibration of both models considerably improved (Templeton observed 21% versus predicted 20%; Nelson observed 21% versus predicted 24%). LIMITATIONS, REASONS FOR CAUTION: We could only test the models in couples with primary infertility because detailed information on secondary infertile couples was lacking in the Dutch data. This shortcoming may have negatively influenced the performance of the Nelson model. WIDER IMPLICATIONS OF THE FINDINGS: The changes in success rates over time should be taken into account when assessing prediction models for estimating the success rate of IVF treatment. In patients with primary infertility, the choice to use the Templeton or Nelson model is arbitrary.

Who is at risk of emotional problems and how do you know? Screening of women going for IVF treatment.

BACKGROUND: Fertility problems are accompanied by a lot of emotional distress, resulting in a considerable proportion of female patients showing severe maladjustment after assisted reproductive technology. Although this interferes with their daily life, emotional distress has also shown to be related to dropout of treatment and deterioration of health behaviour. Early identification of women at risk enables the provision of timely psychosocial support and the focusing psychosocial resources on those who need it most. This study investigated the psychometric characteristics of a screening tool SCREENIVF to identify women at risk of emotional problems at an early stage of treatment. METHODS: Risk factors for emotional maladjustment were identified in a previous study and incorporated in SCREENIVF which consists of 34 items on general and infertility specific psychological factors. A group of 279 women in their first IVF treatment cycle completed SCREENIVF at both pretreatment and 3-4 weeks after the pregnancy test. Linear Regression analyses were performed to investigate the predictive value of SCREENIVF, and sensitivity and specificity as well as likelihood ratios were described. RESULTS: SCREENIVF successfully identified 75% of the patients as at risk or not at risk. The negative predictive value was high: 89%. The positive predictive value was low (48% in the total sample and 56% after unsuccessful treatment). Sensitivity was 69%, specificity was 77%. CONCLUSIONS: For its use as a first screening for emotional problems, SCREENIVF is an acceptable instrument to identify women at risk. These women could be offered more detailed diagnostics e.g. an interview to further investigate to what extent they could benefit from psychological treatment. In addition, physicians can anticipate on this risk profile when communicating with these patients.

Can differences in IVF success rates between centres be explained by patient characteristics and sample size?

BACKGROUND: Pregnancy rates cannot be used reliably for comparison of IVF clinic performance because of differences in patients between clinics. We investigate if differences in pregnancy chance between IVF centres remain after adjustment for patient mix. METHODS: We prospectively collected IVF and ICSI treatment data from 11 out of 13 IVF centres in the Netherlands, between 2002 and 2004. Adjustment for sampling variation was made using a random effects model. A prognostic index for subfertility-related factors was used to adjust for differences in patient mix. The remaining variability between centres was split into random variation and true differences. RESULTS: The crude 1-year ongoing pregnancy chance per centre differed by nearly a factor 3 between centres, with hazard ratios (HRs) of 0.48 (95% CI: 0.34-0.69) to 1.34 (95% CI: 1.18-1.51) compared with the mean 1-year ongoing pregnancy chance of all centres. After accounting for sampling variation, the difference shrank since HRs became 0.66 (95% CI: 0.51-0.85) to 1.28 (95% CI: 1.13-1.44). After adjustment for patient mix, the difference narrowed somewhat further to HRs of 0.74 (95% CI: 0.57-0.94) to 1.33 (95% CI: 1.20-1.48) and 17% of the variation between centres could be explained by patient mix. The 1-year cumulative ongoing pregnancy rate in the two most extreme centres was 36% and 55%. CONCLUSIONS: Only a minor part of the differences in pregnancy chance between IVF centres is explained by patient mix. Further research is needed to elucidate the causes of the remaining differences.

Anxiety and depression have no influence on the cancellation and pregnancy rates of a first IVF or ICSI treatment.

BACKGROUND: After many years of research, the impact of psychological distress on the IVF treatment outcome is still unclear. This study aimed to determine the influences of anxiety and depression before and during IVF or ICSI treatment on the cancellation and pregnancy rates of inductees. METHODS: In a multicentre prospective cohort study, we assessed anxiety and depression at baseline and the procedural anxiety level one day before oocyte retrieval, with the short versions of the State Anxiety Inventory (STAI) and the Beck Depression Inventory-Primary Care (BDI-PC). The effect of baseline anxiety and depression on the cancellation and pregnancy rates of 783 women in their first IVF or ICSI treatment was evaluated. We also determined if a change in anxiety from the start of treatment until just before oocyte retrieval affects the pregnancy rate. The predictive value of distress was assessed while controlling for several factors in subfertility treatment. RESULTS: Neither baseline nor procedural anxiety, nor depression affected the ongoing pregnancy rates, with odds ratios (ORs) of 1.04 (95% CI 0.82-1.33), 0.96 (95% CI 0.77-1.20) and 0.85 (95% CI 0.65-1.10), respectively. Neither did the anxiety gain score affect the pregnancy rate, OR 1.08 (95% CI 0.83-1.41). A cancellation of treatment could not be predicted by either anxiety or depression, OR 1.16 (95% CI 0.83-1.63) and 0.85 (95% CI 0.59-1.22), respectively. CONCLUSIONS: Inductees in IVF treatment can be reassured that anxiety and depression levels before and during treatment have no significant influence on the cancellation and pregnancy rates.

Pregnancy chances on an IVF/ICSI waiting list: a national prospective cohort study.

BACKGROUND: The effectiveness of IVF over expectant management has been proven only for bilateral tubal occlusion. We aimed to estimate the chance of pregnancy without treatment for IVF patients, using data on the waiting period before the start of IVF. METHODS: A prospective cohort study included all couples eligible for IVF or ICSI treatment, registered in a national waiting list in The Netherlands. The cumulative probability of treatment-free ongoing pregnancy on the IVF waiting list was assessed and the predictive effect of female age, duration of infertility, primary or secondary infertility and diagnostic category was estimated using Cox regression. RESULTS: We included 5962 couples on the waiting list. The cumulative probability of treatment-free ongoing pregnancy was 9% at 12 months. In multivariable Cox regression, hazard ratios were: 0.95 (P < 0.001) per year of the woman's age, 0.85 (P < 0.001) per year of duration of infertility, 0.71 (P = 0.005) for primary versus secondary infertility. Diagnostic category showed hazard ratios of 0.7, 1.6, 1.2, 1.7 and 2.6 for endometriosis, male factor, hormonal, immunological and unexplained infertility, respectively, compared with 'tubal infertility' (P < 0.001). The 12-months predicted probabilities ranged from 0% to 25%. CONCLUSIONS: The chance of an ongoing pregnancy without treatment while waiting for an IVF or ICSI is below 10% but may be as high as 25% within 1 year for selected patient groups. Timing of IVF should take predictive factors into consideration.

Predicting ongoing pregnancy chances after IVF and ICSI: a national prospective study.

BACKGROUND: The Dutch IVF guideline suggests triage of patients for IVF based on diagnostic category, duration of infertility and female age. There is no evidence for the effectiveness of these criteria. We evaluated the predictive value of patient characteristics that are used in the Dutch IVF guideline and developed a model that predicts the IVF ongoing pregnancy chance within 12 months. METHODS: In a national prospective cohort study, pregnancy chances after IVF and ICSI treatment were assessed. Couples eligible for IVF or ICSI were followed during 12 months, using the databases of 11 IVF centres and 20 transport IVF clinics. Kaplan-Meier analysis was performed to estimate the cumulative probability of an ongoing pregnancy, and Cox regression was used for assessing the effects of predictors of pregnancy. RESULTS: 4928 couples starting IVF/ICSI treatment were prospectively followed. On average, couples had 1.8 cycles in 12 months for both IVF and ICSI. The 1-year probability of ongoing pregnancy was 44.8% (95% CI 42.1-47.5%). ICSI for severe oligospermia had a significantly higher ongoing pregnancy rate than IVF indicated treatments, with a multivariate Hazard ratio (HR) of 1.22 (95% CI 1.07-1.39). The success rates were comparable for all diagnostic categories of IVF. The highest success rate is at age 30, with a slight decline towards younger women and women up to 35 and a sharp drop after 35. Primary subfertility with a HR of 0.90 (95% CI 0.83-0.99) and duration of subfertility with a HR of 0.97 (95% CI 0.95-0.99) per year significantly affected the pregnancy chance. CONCLUSIONS: The most important predictors of the pregnancy chance after IVF and ICSI are women's age and ICSI. The diagnostic category is of no consequence. Duration of subfertility and pregnancy history are of limited prognostic value.

Effects of subfertility cause, smoking and body weight on the success rate of IVF.

BACKGROUND: We investigated the separate and combined effects of smoking and body mass index (BMI) on the success rate of IVF for couples with different causes of subfertility. METHODS: The success rate of IVF was examined in 8457 women. Detailed information on reproduction and lifestyle factors was combined with medical record data on IVF treatment. All IVF clinics in The Netherlands participated in this study. The main outcome measures were live birth rate per first cycle of IVF differentiated for the major predictive factors. RESULTS: For male subfertility the delivery rate per cycle was significantly lower than unexplained subfertility, OR of 0.70 (95% CI 0.57-0.86); for tubal pathology, the delivery rate was slightly lower, OR = 0.86 (95% CI 0.70-1.01). Smoking was associated with a significantly lower delivery rate was slightly lower; for OR = 0.72 (95% CI 0.61-0.84) and a significantly higher abortion rate compared to non-smoking delivery rates of 21.4% and 16.4%, respectively (P=0.02). Women with a BMI of > or = 27 kg/m2 had a significantly lower delivery rate, with an OR of 0.67 (95% CI 0.48-0.94), compared with normal weight women (BMI > or = 20 and <27 kg/m2). CONCLUSIONS: Both smoking and overweight unfavourably affect the live birth rate after IVF. The devastating impact of smoking on the live birth rate in IVF treatment is comparable with an increase in female age of >10 years from age 20 to 30 years. Subfertile couples may improve the outcome of IVF treatment by lifestyle changes.

Cyclo-oxygenase products do not participate in the induction of ornithine decarboxylase in human epidermis.

Extensive animal data suggest that prostaglandins are involved in the epidermal induction of ornithine decarboxylase (ODC). We undertook this study to investigate their role during induction of hyperproliferation in human skin. Topical indomethacin (Elmetacin) or vehicle only were applied under occlusion on the backs of healthy volunteers. This was followed 1 h later by Sellotape stripping and biopsies were carried out for the estimation of the levels of ODC. There was no significant difference in the level of ODC in the indomethacin-treated and control sites. Also, test sites were irradiated with 3 MEDs of UVB, and this was immediately followed by the application of indomethacin or vehicle only on the irradiated sites. After 8 h biopsies were taken and the levels of ODC were again similar in both sites. The data indicate that the cyclo-oxygenase products in human epidermis do not contribute to the induction of ODC.