{"paper_id":"1ded5c5b-0308-43f6-839a-0fe9b81c25fd","body_text":"Infertility is defined as the failure to conceive within 12 months of regular\nunprotected intercourse, affects approximately one in six couples and many of those\nwith prolonged unresolved infertility will be treated with Assisted Reproduction\nTreatments (ART) regardless of the cause ( Leijdekkers\n et al ., 2018 ). The increase in IVF/ICSI cycles is\nnot caused by a sudden epidemic of infertility, but by increased access and by an\nexpansion of their indications ( van Loendersloot\n et al ., 2014 ). Unfortunately, doing an IVF/ICSI\ncycle is not a guarantee of success. Some reports refer that up to 38-49% of couples\nthat start IVF will remain childless, even if they undergo up to six cycles ( Malizia  et al ., 2009 ).\nSubfertile couples should, therefore, be well informed about the chances of success\nwith IVF/ICSI cycles before starting their first or before continuing with a new\ntreatment ( van Loendersloot  et al .,\n2014 ). It is important to give the couple a real and fair expectation\nabout their odds to get a live birth child, weighed against the risks of the\ntreatment. On the other hand, since IVF/ICSI is expensive, the couple can decide if\nthe financial (and emotional) burden can be justified ( Hamdine  et al ., 2015 ). The threshold at which\nthe couple will start or continue treatment may differ according to insurance\ncompany’s support, the taxpayers’ funds, and the patients own option ( van Loendersloot et al., 2014 ). This\nprobability of success is also important in the management of public Fertility\nClinics and in the management of their waiting lists on public health national\nsystems. To facilitate patient counseling, clinical decision-making, and access to\nhealth care provision, prediction models for live birth after IVF have been\nconstructed ( Nelson & Lawlor, 2011 ).\nFertility prediction models, before treatment, are treatment-independent and couples\ntake part in these models before starting treatment ( Zarinara  et al ., 2016 ). They can be based on patient\nbaseline characteristics. Alternative models have incorporated the characteristics\nof the intermediate results of the first treatment cycle, thereby improving the\naccuracy of probability estimates for future cycles ( La Marca  et al ., 2011 ).\nA number of factors have been reported as influencing the success of IVF, either\npositively or negatively. Women’s age, antimullerian hormone (AMH) levels and antral\nfollicle count (AFC) have been consistently shown to be associated with IVF success\n( Khader  et al ., 2013 ).\nDuring the last few years, some important pretreatment predictors, which used live\nbirth as the primary outcome, have been published, and hereby we highlight them:\nA -  Nelson & Lawlor (2011) . This predictor\nmodel was made using a cohort of 144,018 IVF cycles (data from Human Fertilization\nand Embryology Authority - HFEA) undertaken in the United Kingdom (UK) between 2003\nand 2007, to examine the predictors of live birth with IVF treatment. This predictor\nincludes woman’s age, infertility duration, source of eggs, cause of infertility,\nnumber of previous IVF cycles, previous pregnancies, medication use and type of\ntreatment (IVF or ICSI). This model was built on considerably old datasets,\npre-dating significant changes in clinical practice that occurred from 2008 onwards,\ntherefore requiring a time adjustment. It was externally validated in 2014 ( te Velde  et al ., 2014 ) and\n2015 ( Smith  et al ., 2015 ) ,\nwhere it was found that it overestimated success rates. This predictor was used to\nproduce a web calculator tool:  www.ivfpredict.com .\nB -  La Marca  et al.  (2011) .\nUnlike the previous model, which included a lot of predictors, La Marca and\ncolleagues only included woman’s age and AMH in their model, as only these two\nfactors were identified as predictive with the multivariate analysis done. They\ndeveloped this predictor in an Italian cohort of 389 women, between 2005 and 2009\nand demonstrated that AMH is significantly associated with live birth and that this\nassociation is independent of age. The external validation of Nelson’s predictor\nmodel, made by  Khader  et al .\n(2013) , with 822 women in Glasgow, confirmed that AMH is an independent\npredictor of live birth, and AMH and age can be displayed as categories, rather\ncontinuous variables, with a clear benefit for applying the model in a clinical\nenvironment.\nC -  McLernon  et al . (2016) .\nFor the model development, data from 113,873 women and 184,269 completed cycles\n(data from HFEA of UK) between 1999 and 2009 were used. This model is the first to\nprovide an individualized estimate of the cumulative chance of a live birth over\nmultiple complete cycles of IVF/ICSI, with a complete cycle defined as all fresh and\nfrozen thawed embryo transfers resulting from one episode of ovarian stimulation\ncycle. In addition, it provides for a pre and posttreatment model. On the\npretreatment model, the predictors included are woman’s age; duration of\ninfertility, previous pregnancy; cause of infertility and type of treatment, not\nincluding AMH or AFC, ethnicity, BMI, smoking status or alcohol intake, since they\nwere unavailable in the HFEA database. Internal validation of the model showed\npromising results, and they provided a web calculator  https://w3.abdn.ac.uk/clsm/opis/tool/ivf1 .  Leijdekkers  et al . (2018)  recently validated\nthis model in an independent prospective cohort of 1515 Dutch women who participated\nin the OPTIMIST trial, and underwent their first IVF treatment between 2011 and 2014,\nin a total of 2881 completed cycles. They concluded that after minor recalibration\nof the pretreatment model, it proved valid in predicting the cumulative chance of a\nlive birth after multiple complete treatment cycles in another geographical context,\nand that adding AMH, AFC and BMI data, it gained only a marginal improvement of the\npredictive performance ( Leijdekkers  et\nal ., 2018 ). This validation can be questioned since it uses\npatients from a trial, who had done a restricted protocol with restricted doses,\nwhich in fact can lead to a different outcome, and they did not include anovulatory\nwomen, so it did not fully represent the overall patient population undergoing\nIVF/ICSI treatment.\nD -  Dhillon  et al . (2016) . In\nthis predictor model, the authors intended to incorporate key pretreatment\npredictors, such as BMI, ethnicity and ovarian reserve. In this cohort study, a\nmodel to predict live birth was derived using data collected from 9915 women, who\nunderwent IVF/ICSI treatment at any CARE (Center for Assisted Reproduction) clinic\nin the UK from 2008 to 2012. External validation was performed on data collected\nfrom 2,723 women who underwent treatment in 2013, which means, a different\npopulation in a different time, but at the same geographical place. The predictors\nthat showed to have a significant effect on the chances of live birth were: age,\ntubal factor, unexplained causes of infertility and being south Asian or black,\ndifferently from other predictor models.\nThe predictor models already published do not consider all the possible pretreatment\nvariables, even before figuring out if they are statistically significant or not. On\nthe other hand, they are often not user-friendly on the patient perspective, or they\nreflect a reality different from the center where they will be used. Yet, they do\nnot even explain in a simple way the couples’ odds have, although some of them have\nweb calculators available.\nIn the attempt to provide more specific information to patients, and better fit our\nreality, our main goal is to design a simple predictor model, easily\nunderstandable.\n\nA retrospective cohort study of all IVF/ICSI cycles started in our center between\n2012-2016. Only cycles with a live birth delivery after 24 weeks, or cycles with no\nsurplus embryos left were considered. Women’s age at oocyte retrieval varied between\n18-39 years old. The following data was evaluated: AMH; AFC; women’s and men’s age;\nbody mass index (BMI) both for men and women; smoking status; previous diagnosis;\ntype of treatment (IVF/ICSI); having had previous deliveries. Since our model aims\nat pretreatment counseling only, we did not include any oocyte or embryo\nfactors.\nAccording to local protocol, ovarian stimulation was performed with 100 to 450 IU\nof r-FSH or hMG (Gonal-f ® , Merck Serono;\nPuregon ® , MSD; Bemfola ® , Gedeon Richter)\nor HMG (Menopur ® , Ferring), based on ovarian reserve\nassessment, starting on cycle day 2 or 3, mostly within a GnRH antagonist\nflexible protocol (Cetrotide ® , Merck Serono; or\nOrgalutran ® , MSD) started on stimulation day 6. Final\noocyte maturation was induced with hCG (mostly 6,500 IU\nOvitrelle ® , Merck Serono) or GnRH agonist (0.2 mg\nDecapeptyl ® , Ferring) when at least two follicles of 17 mm\nin diameter were visualized by ultrasound. Oocyte retrieval was performed 35-37\nhours after final maturation. ICSI was performed in cases of altered semen\nparameters, according to the World Health Organization (WHO) criteria or in\ncases of previous conventional IVF fertilization failure, or low fertilization\nrate. One or two embryos were transferred 2, 3 or 5 days after oocyte retrieval.\nA fresh transfer was canceled whenever the progesterone level was over 1,5\nng/mL, more than 18 oocytes were expected or intracavitary uterine pathology was\nidentified during stimulation. The luteal phase was supplemented with vaginal\nmicronized natural progesterone (200mg Progeffik ® , Effik,\nthree times a day). Supernumerary embryos of sufficient quality were\ncryopreserved on days 2, 3 or at blastocyst stage. Patients who did not become\npregnant after fresh transfer could undergo frozen-thawed cycles under\nartificial endometrial preparation\nBiochemical pregnancy was defined as a ß-HCG level>10 UI/L, 14 days\nafter oocyte retrieval, and live birth was defined as at least one infant born\nalive after 24 weeks gestation, consistent with previous prediction models and\npublications. The hormonal measure of antimullerian hormone was done with blood\nserum sample using the Electrochemiluminescence (ECLIA) methodology, with the\nModular EVO (E170) Roche Diagnostics® equipment.\nUnivariate and multivariate analyses were used to examine the association of live\nbirth with baseline patient characteristics. The odds-ratios were determined for\nall the statistically significant variables ( p <0.05). The\ndiscrete variables were compared using the Chi-square test and the continuous\nones with the t-student test. When necessary, the continuous variables were\ncategorized. The results are presented according to the predictors founded. We\nused the SPSS 22.1 IBM software.\nThe Ethics Committee of our hospital approved this study.\n\nWe evaluated 739 cycles. Cycle results and baseline characteristics of patients are\ndescribed in  Tables 1  and  2 . Overall, 232 cycles ended up with at least\none live birth. Of the 739 started cycles, 9.1% were canceled (without oocyte pick\nup); 1.1% did not have oocytes (n=7); 4% had no embryos (n=31) and 1.4% had no\nembryos for transfer because of poor quality (n=10). Almost half of the initiated\ncycles, 46%, had a β-HCG serum test positive and 31.4% of the cycles achieved\na live birth. Overall, of the 624 cycles with day 2 embryos, 37% achieved a live\nbirth ( Table 1 ).\nCycle’s results.\nConcerning the continuous variables, there were no differences in the women’s\nage, AMH, AFC, women’s BMI, duration of infertility, men’s age and their BMI,\nand among the women who achieved a live birth and the ones who didn’t.\nDemographic factors such as ethnicity, smoking habits or previous children in\nboth women and men were not statistically significant either ( Table 2 ).\nBaseline characteristics of couples and their treatments\nAMH – Antimullerian hormone; AFC – antral follicle count; BMI – Body\nmass index.\nAs age, AFC and AMH have been commonly associated with live birth rates, these\nvariables were transformed in two different ways: age was exponentialized and\nAMH and AFC were logarithmized, as these curves better describe the expected\nbehavior of these variables on reproductive outcomes after IVF/ICSI. On the\nother hand, these variables were categorized in three classes to design the\npatient-friendly final model. In both transformations, these variables were\nhighly statistically significant ( p <0.001) ( Table 2 ). In a post-hoc sub-group analysis,\nwe noticed that couples undergoing treatment for ovulation disorder or pure male\nfactor seemed to have a more favorable scenario. When we tested this group\nagainst all other causes, we noticed that this was also statistically\nsignificant ( p =0.017).\nFor the multivariate analyses, we performed a binary regression. Only the\ncategorized women’s age, AFC, AMH and male factor or ovulatory factor showed to\nbe statistically significant to achieve a live birth. The p-value for the Hosmer\nand Lemeshow test was 0,740. The ROC curve had an area under the curve of 0.688\n(IC 0.649-0.728) ( Table 3 ). The data from\nthe regression results are simplified in  Table\n4 .\nRegression results\nModel probabilities according to age, AMH, AFC and cause of\ninfertility\n\nThe IVF/ICSI treatment predictors can consider pre and post-treatment variables. The\npretreatment models estimate the probability of a live birth using the\ncharacteristics of the couple when they intend to undergo an IVF/ICSI cycle, such as\nthe woman’s age, duration of infertility, type of infertility, previous pregnancy\nstatus of the couple, ovarian reserve and/or its biomarkers, and treatment type. The\npost-treatment variables include treatment-specific characteristics (number of\noocytes, cryopreservation of embryos, and the number and stage of embryos) from the\ncomplete cycles, along with the characteristics of the couple from the pretreatment\nmodel, in order to update the cumulative probability of achieving a live birth\n( Leijdekkers  et al .,\n2018 ;  McLernon  et al .,\n2016 ).\nYet, some models predict the probability of a live birth after a single fresh embryo\ntransfer only, excluding the important contribution of embryo cryopreservation and\nsubsequent treatment cycles to cumulative live birth rates ( Leijdekkers  et al ., 2018 ). They failed to\nconsider all embryo transfer attempts, which means that such prediction models are\nnot useful as counseling tools, underestimating the odds of success.\nRegarding the woman’s age, considered one of the most important predictors, it seems\nlogical to include it in the prediction models ( Leijdekkers  et al ., 2018 ;  Khader  et al ., 2013 ), which, on the other hand,\ndoes not occur with AMH.  Hamdine  et\nal . (2015)  demonstrated that although AMH added some value\nin predicting the 1-year cumulative live birth rate, its predictive accuracy was\nlimited and added little to prognosis based on the female age alone. The model\npublished by  McLernon  et al .\n(2016)  and its external validation by  Leijdekkers  et al . (2018)  did not include AMH. The last\nones consider that the addition of ovarian reserve measures, i.e. AMH and AFC, to\nthe prediction models revealed only a marginal improvement, stressing the extra\ncosts and physical burden on the patient ( Leijdekkers\n et al ., 2018 ;  Broer\n et al ., 2013 ). However, in one large study ( Nelson  et al ., 2007 ), AMH was\nshown to be associated with live births, regardless of age after treatment, and\nrecently a further large cohort study demonstrated that serum AMH concentrations may\npredict live births in women older than 34 years of age ( Lee  et al ., 2009 ). Other potential predictors\nfor live birth, such as ethnicity, smoking habit and alcohol intake, can be\nconsidered. The additional value of these variables for model performance are\nconsidered uncertain, as the reporting is remarkably subjective and/or often\nincomplete ( Leijdekkers  et al .,\n2018 ;  McLernon  et al .,\n2016 ).\nIn our study, when we used age, AMH and AFC, as continuous variables, there is no\nstatistical differences among the groups. However, since the relation of these\nvariables with the outcome is not linear in the literature, we decided to transform\nthese variables in two different ways. On the one hand, we categorized them and, on\nthe other hand, we exponentialized age and logarithmized AMH and AFC. With both\nmodeling we had a highly statistically significant difference\n( p <0.001). Despite the controversial data on ovarian reserve\nmeasures and their importance in prediction models, in our study they actually\nshowed an important relation with the outcome, live birth, improving the accuracy\nand making this predictor more reliable and user-friendly to patients. After a post\nhoc analysis, we also noticed that the couples undergoing treatment for ovulation\ndisorder or pure male factor had an odds ratio of 1.5 for the outcome and,\ntherefore, we decided to include it in the final model.\nIn Portugal, the last published results are from 2015 ( CNPMA, 2017 ). There, the overall cumulative delivery rate varies between\n25-30% (FIV/ICSI) per started cycle. Our model has the advantage of reflecting our\nparticular population and our particular work setting. On the other hand, it can be\nsimplified in a small table and it had a good overall result in the ROC curve\n(0.688), especially when compared with other models.\nThis model has some limitations. One of them was that it did not consider the\ncouples’ previous treatments. In fact, some patients had done previous treatments in\nother clinics and we could not access their data. Another limitation is that\nsub-groups were created after a post hoc analysis of the data and this might be a\nsource of bias. The consistency of these differences should be confirmed in other\nstudies. We are now planning to validate this model prospectively, first in our\npopulation and then in other clinical settings.\n\nAge, AMH and AFC, when sub-classified, are independently associated to the results of\nan IVF/ICSI treatment. The cause of infertility was also importantly associated when\nsub-categorized as male or ovulatory factor vs others. It is possible to calculate\nthe final treatment results based on a predictor. This predictor is easily\nunderstandable and can work as an important tool to help counseling patients in a\ndaily basis. It can grade patients’ probability of success in achieving a live birth\nbetween 5.9% and 51.1%.","source_license":"CC-BY-4.0","license_restricted":false}