• Prediction of the ineffectiveness of assisted reproductive technologies in women with infertility and liver disease

Prediction of the ineffectiveness of assisted reproductive technologies in women with infertility and liver disease

HEALTH OF WOMAN. 2016.4(110):171–174; doi 10.15574/HW.2016.110.171 
 

Prediction of the ineffectiveness of assisted reproductive technologies in women with infertility and liver disease


Boychuk O. G.

National Medical Academy of Postgraduate Education P. L. Shupyk, Kiev


The objective: to evaluate the risk factors and develop an algorithm for predicting the ineffectiveness of ART programs in women with liver disease.


Patients and methods. To build a prediction algorithm comprehensively examined 200 patients with infertility and non-alcoholic fatty liver disease (NAFLD): 1 group – 55 women who became pregnant (the efficacy of ART programs), 2 group – 145 women who use assisted reproduction has been ineffective. Building a prediction algorithm was based on the recognition pathometrically procedure developed E.V.Gubler.


Results. The results of comprehensive studies have identified 23 risk factors that can be used in predicting the ineffectiveness of ART in women with liver disease. For each group calculated the frequency of occurrence of each characteristic and the formula calculated divergence Kullback informativeness of each factor. In accordance with the procedure designed for prognostic factors identified 15 of the most informative prognostic indicators and built a table that is an information basis for the work of forecasting algorithm. As a result of the experimental prediction empirical thresholds set at a1=12 and a2=-12.


Conclusions. Designed prediction algorithm allows high accuracy (95.0%) are women to be at risk of inefficiency ART, which will allow time to conduct the proper therapy and increase the effectiveness of ART programs in women with NAFLD. The proposed algorithm can be recommended for use in the practice of reproduction.


Key words: infertility, assisted reproductive technologies, liver pathology, prognosis.


REFERENCES

1. Gubler EV. 1990. Informatics in pathology, clinical medicine and pediatrics. 176.

2. The Russian Association of Human Reproduction. ART register. The report for 2012.(2014).36.

3. Iuzko OM. 2013. Assisted reproductive technology in Ukraine: pregnancy, childbirth, newborn. Zhinochyy likar. 4:52-3

4. Brinsden PR. 2009. Thirty years of IVF: the legacy of Patrick Steptoe and Robert Edwards. Hum Fertil (Camb);12(3):137–43. http://dx.doi.org/10.1080/14647270903176773; PMid:19925325

5. Cai QF, Wan F, Huang R, Zhang HW. 2011. Factors predicting the cumulative outcome of IVF/ICSI treatment: a multivariable analysis of 2450 patients. Hum. Reprod. 26(9): 2352-540. http://dx.doi.org/10.1093/humrep/der228

6. van Loendersloot LL, van Wely M, Repping S, Bossuyt PM, van der Veen F. 2013. Individualized decision-making in IVF: calculating the chances of pregnancy. Hum. Reprod. 28(11):2972-80. http://dx.doi.org/10.1093/humrep/det315; PMid:23925394

7. Yeh JS, Steward RG, Dude AM, Shah AA, Goldfarb JM, Muasher SJ. 2014. Pregnancy rates in donor oocyte cycles compared to similar autologous in vitro fertilization cycles: an analysis of 26,457 fresh cycles from the Society for Assisted Reproductive Technology. Fertil Steril. 102(2):399–404.http://dx.doi.org/10.1016/j.fertnstert.2014.04.027