3.8

CiteScore

2.4

Impact Factor
  • ISSN 1674-8301
  • CN 32-1810/R
Wu Fang, Liu Feng, Guan Yichun, Du Jiangbo, Tan Jichun, Lv Hong, Lu Qun, Tao Shiyao, Huang Lei, Zhou Kun, Xia Yankai, Wang Xinru, Shen Hongbing, Ling Xiufeng, Diao Feiyang, Hu Zhibin, Jin Guangfu. A nomogram predicting clinical pregnancy in the first fresh embryo transfer for women undergoing in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments[J]. The Journal of Biomedical Research, 2019, 33(6): 422-429. DOI: 10.7555/JBR.33.20190065
Citation: Wu Fang, Liu Feng, Guan Yichun, Du Jiangbo, Tan Jichun, Lv Hong, Lu Qun, Tao Shiyao, Huang Lei, Zhou Kun, Xia Yankai, Wang Xinru, Shen Hongbing, Ling Xiufeng, Diao Feiyang, Hu Zhibin, Jin Guangfu. A nomogram predicting clinical pregnancy in the first fresh embryo transfer for women undergoing in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments[J]. The Journal of Biomedical Research, 2019, 33(6): 422-429. DOI: 10.7555/JBR.33.20190065

A nomogram predicting clinical pregnancy in the first fresh embryo transfer for women undergoing in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments

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  • Corresponding author:

    Guangfu Jin and Zhibin Hu, Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China

    Tel/Fax: +86-25-8686-8437/+86-25-8686-8499 and +86-25-8686-8437, E-mails:guangfujin@njmu.edu.cn and zhibin_hu@njmu.edu.cn

  • Received Date: April 27, 2019
  • Revised Date: June 19, 2019
  • Accepted Date: June 20, 2019
  • Available Online: August 29, 2019
  • The extent to which factors affect the probability of clinical pregnancy in the first fresh embryo transfer after assisted conception is unknown. In order to examine the predictors of clinical pregnancy, a retrospective cohort study was launched between January 1, 2013 and December 31, 2016 in four infertility clinics including 19 837 in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) fresh cycles with known outcomes and relevant records. A multivariable logistic regression was used to select the most significant predictors in the final nomogram for predicting clinical pregnancy. Furthermore, the model was validated by an independent validation set and the performance of the model was evaluated by the receiver operating characteristic (ROC) curves along with the area under the ROC curve (AUC) and calibration plots. In a training set including 17 854 participants, we identified that female age, tubal factor, number of embryos transferred, endometrial thickness and number of good-quality embryos were independent predictors for clinical pregnancy. We developed a nomogram using these five factors and the predictive ability was 0.66 for AUC (95% CI 0.64–0.68), which was independently validated in the validation set (AUC=0.66, 95% CI 0.65–0.68). Our results show that some specific factors can be used to provide infertile couples with an accurate assessment of clinical pregnancy following assisted conception and facilitate to guide couples and clinicians.
  • [1]
    Qiao J, Feng HL. Assisted reproductive technology in China: compliance and non-compliance[J]. Transl Pediatr, 2014, 3(2): 91–97.
    [2]
    Zhou Z, Zheng D, Wu H, et al. Epidemiology of infertility in China: a population-based study[J]. BJOG, 2018, 125(4): 432–441. doi: 10.1111/1471-0528.14966
    [3]
    Zarinara A, Zeraati H, Kamali K, et al. Models predicting success of infertility treatment: a systematic review[J]. J Reprod Infertil, 2016, 17(2): 68–81.
    [4]
    Nelson SM, Lawlor DA. Predicting live birth, preterm delivery, and low birth weight in infants born from in vitro fertilisation: a prospective study of 144,018 treatment cycles[J]. PLoS Med, 2011, 8(1): e1000386. doi: 10.1371/journal.pmed.1000386
    [5]
    De Neubourg D, Gerris J, Mangelschots K, et al. Single top quality embryo transfer as a model for prediction of early pregnancy outcome[J]. Hum Reprod, 2004, 19(6): 1476–1479. doi: 10.1093/humrep/deh283
    [6]
    De Mouzon J, Goossens V, Bhattacharya S, et al. Assisted reproductive technology in Europe, 2006: results generated from European registers by ESHRE[J]. Hum Reprod, 2010, 25(8): 1851–1862. doi: 10.1093/humrep/deq124
    [7]
    McLernon DJ, Steyerberg EW, Te Velde ER, et al. Predicting the chances of a live birth after one or more complete cycles of in vitro fertilisation: population based study of linked cycle data from 113873 women[J]. BMJ, 2016, 355: i5735.
    [8]
    Clarke JF, van Rumste MME, Farquhar CM, et al. Measuring outcomes in fertility trials: can we rely on clinical pregnancy rates?[J]. Fertil Steril, 2010, 94(5): 1647–1651. doi: 10.1016/j.fertnstert.2009.11.018
    [9]
    Schieve LA, Peterson HB, Meikle SF, et al. Live-birth rates and multiple-birth risk using in vitro fertilization[J]. JAMA, 1999, 282(19): 1832–1888. doi: 10.1001/jama.282.19.1832
    [10]
    Wang AC, Wang Y, Wu FX, et al. Assessing predictors for the success of GnRH antagonist protocol in reproductive women in IVF/ICSI - in fresh cycles[J]. Biomed Rep, 2017, 7(5): 482–486. doi: 10.3892/br.2017.984
    [11]
    Cai QF, Wan F, Huang R, et al. Factors predicting the cumulative outcome of IVF/ICSI treatment: a multivariable analysis of 2450 patients[J]. Hum Reprod, 2011, 26(9): 2532–2540. doi: 10.1093/humrep/der228
    [12]
    Verberg MFG, Eijkemans MJC, Macklon NS, et al. Predictors of ongoing pregnancy after single-embryo transfer following mild ovarian stimulation for IVF[J]. Fertil Steril, 2008, 89(5): 1159–1165. doi: 10.1016/j.fertnstert.2007.05.020
    [13]
    Blank C, Wildeboer RR, DeCroo I, et al. Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective[J]. Fertil Steril, 2019, 111(2): 318–326. doi: 10.1016/j.fertnstert.2018.10.030
    [14]
    Lintsen AME, Eijkemans MJC, Hunault CC, et al. Predicting ongoing pregnancy chances after IVF and ICSI: a national prospective study[J]. Hum Reprod, 2007, 22(9): 2455–2462. doi: 10.1093/humrep/dem183
    [15]
    Borque Á, Esteban LM, Sanz G, et al. Usefulness of clinical nomograms and predictive models for PCA. Predictive clinical factors of tumor agressiveness[J]. Arch Esp Urol (in Spanish), 2015, 68(3): 267–281.
    [16]
    Ashmita J, Vikas S, Swati G. The impact of progesterone level on day of hCG injection in IVF cycles on clinical pregnancy rate[J]. J Hum Reprod Sci, 2017, 10(4): 265–270. doi: 10.4103/0974-1208.223278
    [17]
    Dhillon RK, McLernon DJ, Smith PP, et al. Predicting the chance of live birth for women undergoing IVF: a novel pretreatment counselling tool[J]. Hum Reprod, 2016, 31(1): 84–92. doi: 10.1093/humrep/dev268
    [18]
    Hansen KR, He ALW, Styer AK, et al. Predictors of pregnancy and live-birth in couples with unexplained infertility after ovarian stimulation-intrauterine insemination[J]. Fertil Steril, 2016, 105(6): 1575–1583.e2. doi: 10.1016/j.fertnstert.2016.02.020
    [19]
    Broekmans FJ, Knauff EAH, te Velde ER, et al. Female reproductive ageing: current knowledge and future trends[J]. Trends Endocrinol Metab, 2007, 18(2): 58–65. doi: 10.1016/j.tem.2007.01.004
    [20]
    van Loendersloot LL, van Wely M, Repping S, et al. Individualized decision-making in IVF: calculating the chances of pregnancy[J]. Hum Reprod, 2013, 28(11): 2972–2980. doi: 10.1093/humrep/det315
    [21]
    Ulug U, Ben-Shlomo I, Turan E, et al. Conception rates following assisted reproduction in poor responder patients: a retrospective study in 300 consecutive cycles[J]. Reprod Biomed Online, 2003, 6(4): 439–443. doi: 10.1016/S1472-6483(10)62164-5
    [22]
    Kawwass JF, Crawford S, Kissin DM, et al. Tubal factor infertility and perinatal risk after assisted reproductive technology[J]. Obstet Gynecol, 2013, 121(6): 1263–1271. doi: 10.1097/AOG.0b013e31829006d9
    [23]
    Luke B, Brown MB, Stern JE, et al. Using the Society for Assisted Reproductive Technology Clinic Outcome System morphological measures to predict live birth after assisted reproductive technology[J]. Fertil Steril, 2014, 102(5): 1338–1344. doi: 10.1016/j.fertnstert.2014.07.1242
    [24]
    Rozen S. Defending male fertility[J]. Sci Transl Med, 2011, 3(92): 92ps31. doi: 10.1126/scitranslmed.3002743
    [25]
    Palermo G, Joris H, Devroey P, et al. Pregnancies after intracytoplasmic injection of single spermatozoon into an oocyte[J]. Lancet, 1992, 340(8810): 17–18. doi: 10.1016/0140-6736(92)92425-F
    [26]
    Baker VL, Luke B, Brown MB, et al. Multivariate analysis of factors affecting probability of pregnancy and live birth with in vitro fertilization: an analysis of the society for assisted reproductive technology clinic outcomes reporting system[J]. Fertil Steril, 2010, 94(4): 1410–1416. doi: 10.1016/j.fertnstert.2009.07.986
    [27]
    Huang Y, Li JY, Zhang F, et al. Factors affecting the live-birth rate in women with diminished ovarian reserve undergoing IVF-ET[J]. Arch Gynecol Obstet, 2018, 298(5): 1017–1027. doi: 10.1007/s00404-018-4884-4
    [28]
    Pandian Z, Marjoribanks J, Ozturk O, et al. Number of embryos for transfer following in vitro fertilisation or intra-cytoplasmic sperm injection[J]. Cochrane Database Syst Rev, 2013, (7): CD003416.
    [29]
    Li HWR, Lee VCY, Lau EYL, et al. Role of baseline antral follicle count and anti-Mullerian hormone in prediction of cumulative live birth in the first in vitro fertilisation cycle: a retrospective cohort analysis[J]. PLoS One, 2013, 8(4): e61095. doi: 10.1371/journal.pone.0061095
    [30]
    Roberts SA, Hirst WM, Brison DR, et al. Embryo and uterine influences on IVF outcomes: an analysis of a UK multi-centre cohort[J]. Hum Reprod, 2010, 25(11): 2792–2802. doi: 10.1093/humrep/deq213
    [31]
    Dechanet C, Brunet C, Anahory T, et al. Effects of cigarette smoking on female reproduction: from oocyte to embryo (Part I)[J]. Gynecol Obstet Fertil (in French), 2011, 39(10): 559–566. doi: 10.1016/j.gyobfe.2011.07.033
    [32]
    Rockliff HE, Lightman SL, Rhidian E, et al. A systematic review of psychosocial factors associated with emotional adjustment in in vitro fertilization patients[J]. Hum Reprod Update, 2014, 20(4): 594–613. doi: 10.1093/humupd/dmu010
    [33]
    Yang XK, Li Y, Li CD, et al. Current overview of pregnancy complications and live-birth outcome of assisted reproductive technology in mainland China[J]. Fertil Steril, 2014, 101(2): 385–391.e2. doi: 10.1016/j.fertnstert.2013.10.017
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    Corresponding author: Jin Guangfu, guangfujin@njmu.edu.cn

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