|Year : 2019 | Volume
| Issue : 2 | Page : 77-83
Development and validation of a nomogram for predicting the probability of live birth in infertile women
Meng Zhang1, Hai-Qing Tian1, Tao Bu2, Xia Li1, Xiao-Hui Wan1, Duo-Lao Wang3, Hua Xu1, Xin-Min Mao1, Qing-Li Wang1, Xiao-Lin La1
1 Reproductive Medicine Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China
2 Center of Statistics and Information, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China
3 Department of Clinical Sciences and International Public Health, Liverpool School of Tropical Medicine, University of Liverpool, Liverpool L69 3BX, United Kingdom
|Date of Submission||13-Feb-2019|
|Date of Web Publication||9-Jul-2019|
Prof Xiao-Lin La
Department of Reproductive Medicine, The First Affiliated Hospital of Xinjiang Medical University, 137 Liyushan Road, Urumqi 830054
Source of Support: None, Conflict of Interest: None
Objective: To develop a nomogram to predict the probability of live birth on the basis of the association of patient characteristics in subfertile individuals or couples.
Methods: A retrospective study was conducted from January 2014 to December 2015. A nomogram was built from a training cohort and tested on an independent validation cohort. A total of 2,257 patients who had undergone their first nondonor cycle of in vitro fertilization (IVF) (including intracytoplasmic sperm injection) were randomly split 2:1 into training (n = 1,527) and validation (n = 730) cohorts.
Results: There were no statistically significant differences in the patients' baseline and cycle characteristics between the training and validation cohorts. On multiple logistic regression analysis, female age, antral follicle count, tubal factor, anovulation, ethnicity, unexplained fertility, and male factor were significantly associated with live birth. The nomogram had a C-index of 0.700 (95% confidence interval [CI]: 0.698–0.701) in the training cohort and 0.684 (95% CI: 0.681–0.687) in the validation cohort.
Conclusions: Our nomogram can predict the probability of live birth for infertile women and can be used to guide clinicians and couples to decide on an IVF treatment option.
Keywords: In vitro Fertilization/Intracytoplasmic Sperm Injection; Live Birth Rate; Nomogram; Predictive Model; Treatment Outcome Prediction
|How to cite this article:|
Zhang M, Tian HQ, Bu T, Li X, Wan XH, Wang DL, Xu H, Mao XM, Wang QL, La XL. Development and validation of a nomogram for predicting the probability of live birth in infertile women. Reprod Dev Med 2019;3:77-83
|How to cite this URL:|
Zhang M, Tian HQ, Bu T, Li X, Wan XH, Wang DL, Xu H, Mao XM, Wang QL, La XL. Development and validation of a nomogram for predicting the probability of live birth in infertile women. Reprod Dev Med [serial online] 2019 [cited 2019 Oct 17];3:77-83. Available from: http://www.repdevmed.org/text.asp?2019/3/2/77/262388
Meng Zhang, Hai.Qing Tian and Tao Bu equally contributed to this article.
| Introduction|| |
Infertility is a major issue for couples of childbearing age all over the world that can lead to distress, as well as discrimination and ostracism., The World Health Organization and the American Society for Reproductive Medicine have defined infertility as a disease., A significant proportion of individuals or couples who experience infertility may be subjected to social stigmatization, psychological distress, and economic restrictions.
Infertility affects approximately 15%–20% of reproductive couples in the world.,, In 2010, a systematic review that analyzed household survey data from 277 demographic and reproductive health surveys of 190 countries and territories and reported that among women of reproductive age (20–44 years), approximately 1.9% were unable to have a live birth (primary infertility) and 10.5% were unable to have another child (secondary infertility).
In vitro fertilization (IVF) is now widely used for the treatment of infertility. However, IVF cannot guarantee success, as nearly 57% of couples that start IVF will remain childless, even after undergoing up to six complete IVF cycles. Therefore, the probability of successful live birth with IVF treatment is an important factor to consider for infertile/subfertitle couples and clinicians when making decisions regarding when a patient should consider abandoning IVF, as the ratio of successful live birth probability and cost-effectiveness is very low. The difficulty in utilizing a predictive model is that it generally requires computer software for calculations, which may render the model impractical. Alternatively, the treatment outcome prediction could be calculated by applying a simple graphic calculation tool called nomogram, which embodies predictors derived from regression modeling and attempts to combine proven prognostic factors to improve prediction accuracy rather than the use of individual risk factors or physician experience. The attractive properties of a nomogram are that it can be printed, can be employed as an educational tool, and can be easily applied for personalized clinical decision-making without the requirement of computer software.
In this study, we present a nomogram able to predict the probability of live birth for women who plan to receive IVF/intracytoplasmic sperm injection (ICSI) treatment. The aim is to help patients make difficult decisions based on their probability for success and thus accept the assisted reproductive treatment, remain childless, or seek adoption.
| Methods|| |
Study design and patients
A retrospective cohort study was conducted. Patients who underwent IVF/ICSI from January 2014 to December 2015 were identified, and 2,257 women at the First Affiliated Hospital of Xinjiang Medical University (Xinjiang, China) were enrolled (patients were excluded if they had receive IVF treatment in other institutions). The study was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (K201903-01), and all participants provided signed informed consent before enrolment in the study.
Inclusion and exclusion criteria
The inclusion criteria were: (1) women with ≥1-year history of infertility; (2) women who had undergone their first cycle of IVF or ICSI; and (3) women with a retrieved number of oocytes >0. The exclusion criteria were: (1) women in whom the IVF/ICSI cycle was canceled before oocyte retrieval; (2) women aged >45 years; and (3) women or partner with abnormal chromosome karyotype excluding chromosome polymorphism.
Data were retrieved from the clinical database available at the Reproductive Medicine Center. For each participant, the following baseline demographics and cycle data parameters were recorded: female age, basal follicle-stimulating hormone (FSH) levels, antral follicle count (AFC), body mass index (BMI) (for which women were classified as follows: underweight, <18.5 kg/m2; moderate weight, 18.5–24.9 kg/m2; overweight, 25.0–29.9 kg/m2; and obese: >30 kg/m2), basal luteinizing hormone (LH), estradiol (E2), basal testosterone (basal T), duration of infertility, presence of dysmenorrhea (yes/no), ovarian dysfunction (yes/no), ovulatory disorder (yes/no), endometriosis (yes/no), tubal factor (yes/no), gonadotropin (Gn) days, Gn dose, number of fertilized oocytes (2PN), retrieved oocytes, and metaphase II (MII).
The primary outcome for the nomogram was live birth, defined as any birth event in which at least one baby was born alive and survived for more than 1 month. This definition is consistent with previous studies., Patients underwent pregnancy tests after 28 days of embryo transfer, and confirmed pregnant women were followed up by a nurse for successful delivery.
Statistical analyses were performed using R version 3.3.1 for Windows (http://www.r-project.org/). The mean (standard deviation) for continuous variables and frequency (percentage) for categorical variables were used to summarize the characteristics of the participants. Categorical variables were compared using the Chi-square test or Fisher's exact test. Continuous variables were compared using the t-test or Mann–Whitney U-test depending on data distribution. Logistic regression analysis was used for establishing a predictive model by means of univariate and multivariate analyses.
Model building and validation
[Figure 1] shows the study flow. In order to avoid overfitting of data, the entire dataset was randomly split 2:1 into two parts: two-thirds of the dataset was used as a training set (66.7%, n = 1,527) and one-third of the dataset was defined as the validation (33.3%, n = 730) dataset (seed number: 25,070,416). The study was performed according to previously published guidelines. A multivariable model was then constructed in the training cohort. The final regression model was chosen on the basis of the clinical and statistical significance of the predictors, although statistical significance played an important role. The predictive model was validated using the validation cohort. The concordance index (C-index), which is the nonparametric area under the receiver operating curve and is a measure of the ability of the nomogram to discriminate patients with different outcomes, was used to evaluate the performance of the nomogram. Possible values of the C-index ranged from 0.5 (random predictions) to 1.0 (perfect prediction). The C-index was also calculated for an internal independent validation dataset. The calibration of the nomogram, which measures the distance of predictions from observed outcomes, was assessed using a calibration plot by plotting the predicted probability of the nomogram; bootstrapping was used for bias correction.
A nomogram was formulated on the basis of the results of multivariate analysis by applying the rms package in R version 3.3.1 (http://www.r-project.org/). A final regression model was selected according to both the clinical and statistical significance of the predictors and was then organized as a nomogram designed to calculate patient-specific probabilities of cumulative pregnancy. Values for each of the model covariates were mapped to points on a scale ranging from 0 to 100, with the total points obtained for each covariate mapped to the probability of live birth. A two-tailed P < 0.05 was considered statistically significant.
| Results|| |
Overall, 2,257 patients who had undergone their first nondonor cycle of IVF (including ICSI) at the First Affiliated Hospital of Xinjiang Medical University from January 2014 to December 2015 were included in this study.
Baseline and cycle characteristics of training and validation cohorts
Patient baseline and cycle characteristics in the training and validation cohorts are summarized in [Table 1] and [Table 2]. There were no statistically significant differences in the patients' baseline or cycle characteristics between the two cohorts. The live birth rate of the training and validation cohort was 49.12% (750/1,527) and 48.08% (351/730), respectively.
|Table 1: Patient characteristics in the training and validation datasets|
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Univariate and multivariate analyses
The univariate and multivariable analyses for predicting a live birth are shown in [Table 3]. At univariable analysis, female age, AFC, tubal factor, ovarian dysfunction, ethnicity, unexplained infertility, and male factors were associated with the live birth rate. After multiple logistic regression analysis, female age (odds ratio [OR] = 0.90, 95% confidence interval [CI]: 0.88–0.92), AFC (OR = 1.08, 95% CI: 1.04–1.13), tubal factors (OR = 0.48, 95% CI: 0.37–0.62), anovulation (OR = 0.23, 95% CI: 0.10–0.50), ethnicity (Uyghurs vs. Han: OR = 0.53, 95% CI: 0.36–0.77; Others vs. Han: OR = 0.65, 95% CI: 0.45–0.95), unexplained infertility (OR = 0.51, 95% CI: 0.31–0.84), and male factors (OR = 0.42, 95% CI: 0.28–0.63) were associated with a live birth.
|Table 3: Predictive factors of live birth in univariate and multivariate analyses|
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Nomogram development and internal validation
A nomogram was built, accounting for both statistical and clinical significance [Figure 2]. The nomogram had a C-index of 0.700 (95% CI: 0.698–0.701) in the training cohort, which indicated good performance. The final nomogram was validated in a validation dataset. The validation cohort had a slightly lower C-index 0.684 (95% CI: 0.681–0.687). Calibration curves for the training and validation cohort suggested excellent model calibration, with optimal agreement between the prediction by the proposed nomogram and actual observation [Figure 3] and [Figure 4].
|Figure 2: Nomogram to predict live birth rate after in vitro fertilization. The nomogram simplifies output calculations from a set of input variable values and makes the relative contribution of each factor to the overall score intuitively obvious. A vertical line is drawn from each input variable to the first scale (individual score) and the sum of these outputs is used to calculate the probability of live birth from the lower scale (total score).|
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|Figure 3: Calibration of the model to predict live birth rate for the training dataset.|
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|Figure 4: Calibration of the model to predict the live birth rate for the test dataset.|
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| Discussion|| |
Our study showed that a nomogram could be applied in decision–analytic modeling for infertile couples. The nomogram predicts the outcome probability of the decision analysis recommendation based on individual baseline factors (age, AFC, etiology of infertility, ethnicity, and BMI). For IVF/ICSI treatment, prognosis is an important consideration when patients plan to start treatment. An individualized probability of success is a key indicator for patients in the decision-making process. A precise prediction of treatment outcome could help both physicians and patients make optimal decisions.
The illustrative nature of the nomogram allows for an intuitive understanding of how different predictors may contribute to risk. The advantage of a nomogram is that it allows a more transparent observation of the associations between baseline predictors of the patient and the predicted outcome of the model. The regression coefficients were converted to a 100-point scale on producing the nomogram. The linear predictor was then mapped to the predicted probability axis. To illustrate how this nomogram could be used to aid in decision-making, we may consider two hypothetical patients: patient 1 is 30 years old, Han, has an AFC of 14, presents tubal factors, and a BMI of 23 kg/m2; patient 2 is 40 years old, Uyghurs, has an AFC of 6, presents ovarian dysfunction, and a BMI of 32 kg/m2. Using the nomogram [Supplementary Figure 1] and [Supplementary Figure 2], the sum of the points of the first patient: 0 + 9 + 25 + 27 + 34 + 42 + 43 + 58 + 59, was 297, which mapped to a probability of live birth of 66%. For the second patient, the points 0 + 0 + 4 + 19 + 27 + 29 + 33 + 34 + 58 totaled 204, which mapped to a probability of live birth of 15%. The lower predicted live birth probability for the patient might deter the clinician from recommending or the patient from undergoing IVF treatment.
The age of the woman is a significant factor in determining the probability of delivering a child after assisted reproduction. Natural aging limits fertility in the general population and in patients who seek treatment. Female age is the most significant factor influencing the probability of live birth. The nomogram, formulated by multiple logistic regression analysis, demonstrates that the probability of live birth decreased with increased age, which is in line with previous studies.,,,, There is evidence that IVF helps overcome infertility in women aged 39 years old or younger, but women over 40 years of age should be informed that IVF/ICSI does not help completely overcome the age-dependent decrease in fertility.,
Apart from chronological age, ovarian age, referred to as the ovarian reserve, may improve the prediction of the live birth rate. Estimation of ovarian reserve includes basal FSH, anti-Müllerian hormone levels, and sonographic estimates of ovarian volume or the number of antral follicles, defined as the AFC. The AFC is recognized as a biomarker of ovarian response and has the highest predictive value,, and is associated with live births independent of age., Our results are in agreement with those of previous studies, indicating that the AFC is strongly associated with live birth.,
We observed a nonsignificant inverse U-shaped relationship between BMI and live births, indicating that both high BMI and low BMI may have detrimental effects on live birth. We failed to demonstrate a statistically significant association between the BMI and the live birth rate due to the small sample size, but several studies have confirmed the above association. A possible explanation for this observation may be that the female reproductive system is sensitive to energy imbalances. A low BMI may indicate low energy intake and findings from animal studies, suggesting that inadequate energy intake influences gonadotropins concentrations, follicle growth, and oocyte quality. This finding was also observed in a human study, which showed an inverted U-shaped association between the number of embryos and the BMI. Obese women have a higher risk of adverse health outcomes (including early pregnancy loss, fetal death, stillbirth, neonatal death, congenital anomalies, and pregnancy complications). A systematic review concluded that obese women had a lower probability of pregnancy following IVF, required higher dosage of gonadotropins, had a lower rate of live births and a higher rate of miscarriage.
Our results show statistically significant ethnic disparity in IVF outcomes. Ethnic differences in IVF outcomes may be attributed to several factors, such as genetic, environmental, lifestyle, dietary structure, and socioeconomic status. Several reasons could explain why Uyghurs have the lowest live birth rate: they have higher BMI levels probably owing to excessive consumption of red meat and mutton tallow. Cross-sectional studies suggested that Uyghurs and other minorities lack reproductive medical knowledge due to the poor health and medical conditions in undeveloped rural spots, as well as reported a younger age of first sexual intercourse – the lowest age stated by survey results was only 13 years old., Reproductive system infections could be caused by repeated induced abortions resulting from unprotected sexual behavior. Crude cottonseed oil, which could lead to infertility, was directly used as cooking oil in some areas because of dietary habits.,
Validation of the nomogram is essential to avoid overfitting of the model and to determine generalizability. The large sample size in this cohort guaranteed its representativeness and generalizability for infertility patients. In the current study, calibration plots showed optimal agreement between prediction and actual observation, which guaranteed the repeatability and reliability of the developed nomogram. The C-index of our developed nomogram indicated good performance for predicting the probability of live birth. The nomogram derived from a logistic regression model was much simpler for the end user. Both clinicians and patients could perform an individualized survival prediction through this easy-to-use scoring system. In addition, this tool could provide information for patient stratification in the design of clinical studies, gaining better equivalence between study arms. Still, our nomogram is limited by the retrospective nature of data collection, the failure to incorporate some recognized prognostic parameters (e.g., smoking and alcohol consumption), and the fact that all participants were from a single institution. Further efforts on prospective data collection and patient follow-up, wider geographic recruitment, and incorporation of some other factors are encouraged to improve this model. Despite its limitations, this study provides clinicians and patients with a clinically meaningful predictive tool that can aid in decision-making.
In conclusions, this study proposes a nomogram that could predict the probability of a live birth for an infertile woman undergoing her first IVF/ICSI nondonor cycle. Although further studies are required, including validation in other populations, this nomogram can become a valuable tool in estimating the probability of individual live birth and could help clinicians and couples decide on an IVF treatment option. In this study, we offered evidence that the nomogram can be successfully used for IVF/ICSI treatment outcome prediction in women with infertility.
Supplementary information is linked to the online version of the paper on the Reproductive and Developmental Medicine website.
We would like to thank our patients and their families as well as the doctors and nurses who assisted with recruitment for this study.
Financial support and sponsorship
This work was supported by the Special Research Project of Young Science and Technology Talents of Health Commission of Xinjiang Uygur Autonomous Region (Grant No. WJWY-201935).
Conflicts of interest
There are no conflicts of interest.
| References|| |
Chachamovich JR, Chachamovich E, Ezer H, Fleck MP, Knauth D, Passos EP. Investigating quality of life and health-related quality of life in infertility: A systematic review. J Psychosom Obstet Gynaecol 2010;31:101-10. doi: 10.3109/0167482X.2010.481337.
Cui W. Mother or nothing: The agony of infertility. Bull World Health Organ 2010;88:881-2. doi: 10.2471/BLT.10.011210.
Practice Committee of American Society for Reproductive Medicine. Definitions of infertility and recurrent pregnancy loss: A committee opinion. Fertil Steril 2013;99:63. doi: 10.1016/j.fertnstert.2012.09.023.
Zegers-Hochschild F, Adamson GD, de Mouzon J, Ishihara O, Mansour R, Nygren K, et al.
The international committee for monitoring assisted reproductive technology (ICMART) and the World Health Organization (WHO) revised glossary on ART terminology, 2009. Hum Reprod 2009;24:2683-7. doi: 10.1093/humrep/dep343.
Slade P, O'Neill C, Simpson AJ, Lashen H. The relationship between perceived stigma, disclosure patterns, support and distress in new attendees at an infertility clinic. Hum Reprod 2007;22:2309-17. doi: 10.1093/humrep/dem115.
Bak CW, Seok HH, Song SH, Kim ES, Her YS, Yoon TK. Hormonal imbalances and psychological scars left behind in infertile men. J Androl 2012;33:181-9. doi: 10.2164/jandrol.110.012351.
Wu AK, Elliott P, Katz PP, Smith JF. Time costs of fertility care: The hidden hardship of building a family. Fertil Steril 2013;99:2025-30.
Sharlip ID, Jarow JP, Belker AM, Lipshultz LI, Sigman M, Thomas AJ, et al.
Best practice policies for male infertility. Fertil Steril 2002;77:873-82. doi: 10.1016/S0022-5347(05)65109-9.
Philippov OS, Radionchenko AA, Bolotova VP, Voronovskaya NI, Potemkina TV. Estimation of the prevalence and causes of infertility in Western Siberia. Bull World Health Organ 1998;76:183-7. doi: 10.1146/annurev.publhealth.19.1.527.
Gunnell DJ, Ewings P. Infertility prevalence, needs assessment and purchasing. J Public Health Med 1994;16:29-35. doi: 10.1093/oxfordjournals.pubmed.a042931.
Mascarenhas MN, Flaxman SR, Boerma T, Vanderpoel S, Stevens GA. National, regional, and global trends in infertility prevalence since 1990: A systematic analysis of 277 health surveys. PLoS Med 2012;9:e1001356. doi: 10.1371/journal.pmed.1001356.
McLernon DJ, Steyerberg EW, Te Velde ER, Lee AJ, Bhattacharya S. Predicting the chances of a live birth after one or more complete cycles ofin vitro
fertilisation: Population based study of linked cycle data from 113 873 women. BMJ 2016;355:i5735. doi: 10.1136/bmj.i5735.
Fu AZ, Cantor SB, Kattan MW. Use of nomograms for personalized decision-analytic recommendations. Med Decis Making 2010;30:267-74. doi: 10.1177/0272989X09342278.
Templeton A, Morris JK, Parslow W. Factors that affect outcome ofin vitro
fertilisation treatment. Lancet 1996;348:1402-6. doi: 10.1016/S0140-6736(96)05291-9.
Nelson SM, Lawlor DA. Predicting live birth, preterm delivery, and low birth weight in infants born fromin vitro
fertilisation: A prospective study of 144,018 treatment cycles. PLoS Med 2011;8:e1000386. doi: 10.1371/journal.pmed.1000386.
Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, et al.
Guidelines for developing and reporting machine learning predictive models in biomedical research: A multidisciplinary view. J Med Internet Res 2016;18:e323. doi: 10.2196/jmir.5870.
Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 2008;26:1364-70. doi: 10.1200/JCO.2007.12.9791.
Walz J, Gallina A, Saad F, Montorsi F, Perrotte P, Shariat SF, et al.
A nomogram predicting 10-year life expectancy in candidates for radical prostatectomy or radiotherapy for prostate cancer. J Clin Oncol 2007;25:3576-81. doi: 10.1200/JCO.2006.10.3820.
Centers for Disease Control and Prevention, American Society for Reproductive Medicine, Society for Assisted Reproductive Technology. 2015 Assisted Reproductive Technology National Summary Report. Atlanta (GA): US Dept of Health and Human Services; 2017.
Abuzeid MI, Bolonduro O, La Chance J, Abozaid T, Urich M, Ullah K, et al.
Cumulative live birth rate and assisted reproduction: Impact of female age and transfer day. Facts Views Vis Obgyn 2014;6:145-9.
Smith AD, Tilling K, Lawlor DA, Nelson SM. External validation and calibration of IVFpredict: A national prospective cohort study of 130,960in vitro
fertilisation cycles. PLoS One 2015;10:e0121357. doi: 10.1371/journal.pone.0121357.
Dhillon RK, McLernon DJ, Smith PP, Fishel S, Dowell K, Deeks JJ, et al.
Predicting the chance of live birth for women undergoing IVF: A novel pretreatment counselling tool. Hum Reprod 2016;31:84-92. doi: 10.1093/humrep/dev268.
Malizia BA, Hacker MR, Penzias AS. Cumulative live-birth rates afterin vitro
Engl J Med 2009;360:236-43. doi: 10.1056/NEJMoa0803072.
Broekmans FJ, Kwee J, Hendriks DJ, Mol BW, Lambalk CB. A systematic review of tests predicting ovarian reserve and IVF outcome. Hum Reprod Update 2006;12:685-718. doi: 10.1093/humupd/dml034.
Jayaprakasan K, Campbell B, Hopkisson J, Johnson I, Raine-Fenning N. A prospective, comparative analysis of anti-müllerian hormone, inhibin-B, and three-dimensional ultrasound determinants of ovarian reserve in the prediction of poor response to controlled ovarian stimulation. Fertil Steril 2010;93:855-64. doi: 10.1016/j.fertnstert.2008.10.042.
Iliodromiti S, Anderson RA, Nelson SM. Technical and performance characteristics of anti-Müllerian hormone and antral follicle count as biomarkers of ovarian response. Hum Reprod Update 2015;21:698-710. doi: 10.1093/humupd/dmu062.
Broer SL, Mol BW, Hendriks D, Broekmans FJ. The role of antimullerian hormone in prediction of outcome after IVF: Comparison with the antral follicle count. Fertil Steril 2009;91:705-14. doi: 10.1016/j.fertnstert.2007.12.013.
Holte J, Brodin T, Berglund L, Hadziosmanovic N, Olovsson M, Bergh T. Antral follicle counts are strongly associated with live-birth rates after assisted reproduction, with superior treatment outcome in women with polycystic ovaries. Fertil Steril 2011;96:594-9. doi: 10.1016/j.fertnstert.2011.06.071.
Iliodromiti S, Kelsey TW, Wu O, Anderson RA, Nelson SM. The predictive accuracy of anti-Müllerian hormone for live birth after assisted conception: A systematic review and meta-analysis of the literature. Hum Reprod Update 2014;20:560-70. doi: 10.1093/humupd/dmu003.
Jayaprakasan K, Chan Y, Islam R, Haoula Z, Hopkisson J, Coomarasamy A, et al.
Prediction ofin vitro
fertilization outcome at different antral follicle count thresholds in a prospective cohort of 1,012 women. Fertil Steril 2012;98:657-63. doi: 10.1016/j.fertnstert.2012.05.042.
Pinborg A, Gaarslev C, Hougaard CO, Nyboe Andersen A, Andersen PK, Boivin J, et al.
Influence of female bodyweight on IVF outcome: A longitudinal multicentre cohort study of 487 infertile couples. Reprod Biomed Online 2011;23:490-9. doi: 10.1016/j.rbmo.2011.06.010.
Luke B. Adverse effects of female obesity and interaction with race on reproductive potential. Fertil Steril 2017;107:868-77. doi: 10.1016/j.fertnstert.2017.02.114.
Cai X, Song R, Long M, Wang SF, Ma YR, Li X, et al.
A cross-sectional study on the current status of female infertility in three counties of Xinjiang Uygur autonomous region. Zhonghua Yi Xue Za Zhi 2011;91:3182-5. doi: 10.3760/cma.j.issn.0376-2491.2011.45.005.
Jiao Y, Song X, Cai X. A cross-sectional study of infertility prevalence and influencing factors in Uygur and Kazak women, Xinjiang Uygur autonomous region. Zhonghua Liu Xing Bing Xue Za Zhi 2015;36:945-8. doi: 10.3760/cma.j.issn.0254-6450.2015.09.008.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3]