IVF in the Netherlands. Success rates, lifestyle, psychological factors, and costs.

Contains fulltext : 74944.pdf (publisher's version ) (Open Access)RU Radboud Universiteit Nijmegen, 12 maart 2010Promotores : Braat, D.D.M., Habbema, J.D.F. Co-promotores : Eijkemans, M.J., Verhaak, C.M.239 p

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

A detailed cost analysis of in vitro fertilization and intracytoplasmic sperm injection treatment.

Item does not contain fulltextOBJECTIVE: To provide detailed information about costs of in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) treatment stages and to estimate the cost per IVF and ICSI treatment cycle and ongoing pregnancy. DESIGN: Descriptive micro-costing study. SETTING: Four Dutch IVF centers. PATIENT(S): Women undergoing their first treatment cycle with IVF or ICSI. INTERVENTION(S): IVF or ICSI. MAIN OUTCOME MEASURE(S): Costs per treatment stage, per cycle started, and for ongoing pregnancy. RESULT(S): Average costs of IVF and ICSI hormonal stimulation were euro 1630 and euro 1585; the costs of oocyte retrieval were euro 500 and euro 725, respectively. The cost of embryo transfer was euro 185. Costs per IVF and ICSI cycle started were euro 2381 and euro 2578, respectively. Costs per ongoing pregnancy were euro 10,482 and euro 10,036, respectively. CONCLUSION(S): Hormonal stimulation covered the main part of the costs per cycle (on average 68% and 61% for IVF and ICSI, respectively) due to the relatively high cost of medication. The costs of medication increased with increasing age of the women, irrespective of the type of treatment (IVF or ICSI). Fertilization costs (IVF laboratory) constituted 12% and 20% of the total costs of IVF and ICSI. The total cost per ICSI cycle was 8.3% higher than IVF

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

Item does not contain fulltextBACKGROUND: 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

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

Item does not contain fulltextSTUDY 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

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

Item does not contain fulltextSTUDY 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 euro15 000 to >euro60 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 euro10 000 per live birth for endometriosis from age 34 onwards at 1 year duration. The highest CE ratio reached euro56 000 per live birth for unexplained infertility at age 30 and 3 years duration, dropping to values below euro 30 000 per live birth from age 32 onwards. It reached values below euro20 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

Absence from work and emotional stress in women undergoing IVF or ICSI: an analysis of IVF-related absence from work in women and the contribution of general and emotional factors.

Contains fulltext : 69482.pdf (publisher's version ) (Closed access)OBJECTIVE: To assess productivity losses due to absence from work during in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment and to describe the pattern of IVF-related absence from work. Additionally, the influence of general and psychological variables on IVF-related absence from work was analyzed. DESIGN: Prospective cohort study. SETTING: Eight IVF hospitals participated in the study. SAMPLE: Women undergoing their first treatment with IVF/ICSI. METHODS: The Health and Labour Questionnaire (HLQ) was used to estimate the costs of IVF-related absence from work (n=384). Diaries were used to collect background information and reasons for IVF-related absence. Psychological data were derived using the Spielberger State and Trait Anxiety Inventory (STAI), the Beck Depression Inventory for Primary Care (BDI-PC) and the Inventory Social Relations and the Illness Cognition Questionnaire. Regression analyses were performed using two models, one without and one with psychological data, to assess the impact of the different variables on IVF-related absence from work. MAIN OUTCOME MEASURE: IVF-related absence from work and the costs of productivity losses due to IVF/ICSI per treatment. RESULTS: Overall absence from work during IVF/ICSI treatment was on average 33 hours, of which 23 hours were attributed to IVF/ICSI. Costs of productivity losses due to IVF/ICSI were euro596 per woman. Significant predictors of IVF-related absence from work were the number of hours of paid work, age and self-reported physical and/or emotional problems due to IFV treatment. CONCLUSIONS: Women experiencing emotional complaints and women with physical complaints due to IVF/ICSI reported significantly more IVF-related absence from work

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

Item does not contain fulltextBACKGROUND: 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.1 januari 201

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

Increasing the daily dose of recombinant follicle stimulating hormone (Puregon) does not compensate for the age-related decline in retrievalble oocytes after ovarian stimulation

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