Application of correlation analysis to identify factors from a woman's anamnesis influencing the results of pregnancy obtained by INF

This article discusses the search for a statistical relationship between diseases of the genitourinary system, chronic diseases, surgical interventions and other data on the anamnesis, a woman’s heredity and pregnancy outcome obtained using assisted reproductive technologies (IVF). The study is conducted with the aim of developing a mathematical model for predicting pregnancy and assessing the health of a child conceived using ART (IVF) at the stage of planning. The conclusions are based on data on 338 women and the diagnoses of their 402 children at the stage of the maternity hospital. A research was made for the effect of 56 binary signs on the outcome of pregnancy, described by 38 characteristics. To identify significant factors, a correlation analysis was performed using Fisher's exact test, Chi-square test, and using interval estimates of the shares, and the Z-criterion for the difference of two shares. As the outcomes, the terms and methods of delivery available to the patient group under consideration, as well as the diagnoses of children at the stage of the maternity hospital were selected. To assess the strength of the relationship, Cramer's V is applied. The result of the analysis is the identification of 56 significant factors and 35 significant correlations, which will be taken into account in the future for the development of the regression model.

1. Amirova A.A. Prognozirovanie iskhodov EHKO i EHKO/IKSI u bespolodnykh supruzheskikh par pri nekotorykh formakh besplodiya [dissertation]. Moscow; 2011. (In Russ) Available at: https://www.dissercat.com/content/prognozirovanie-iskhodov-eko-iekoiksi-u-bespolodnykh-supruzheskikh-par-pri-nekotorykh-forma (accessed 17.02.2020).

2. Satvinder Purewal, Sarah C.E. Chapman, Olga B.A. van den Akker. A systematic review and meta-analysis of lifestyle and body mass index predictors of successful assisted reproductive technologies. Journal of Psychosomatic Obstetrics & Gynecology. 2019;40(1):2-18 DOI:10.1080/0167482X.2017.1403418

3. L.L. van Loendersloot, M. van Wely, S. Repping, P.M.M. Bossuyt, F. van der Veen. Individualized decision-making in IVF: calculating the chances of pregnancy. Human Reproduction. 2013;28(11):2972-2980 DOI: 10.1093/humrep/det315

4. Dhillon R.K., McLernon D.J, Smith P.P, Fishel S., Dowell K., Deeks J.J., Bhattacharya S., Coomarasamy A. Predicting the chance of live birth for women undergoing IVF: a novel pretreatment counselling tool. Human Reproduction. 2016;31(1):84-92 DOI: 10.1093/humrep/dev268

5. David J McLernon, Ewout W Steyerberg, Egbert R te Velde, Amanda J Lee, Siladitya Bhattacharya. 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 113 873 women. BMJ. 2016;355(8082) DOI: 10.1136/bmj.i5735

6. Porcu, G., Lehert, P., Colella, C, Giorgetti C. Predicting live birth chances for women with multiple consecutive failing IVF cycles: a simple and accurate prediction for routine medical practice. Reproductive Biology and Endocrinology. 2013;11(1) DOI: 10.1186/1477-7827-11-1

7. Katarina Kebbon Vaegter, Tatevik Ghukasyan Lakic, Matts Olovsson, Lars Berglund, Thomas Brodin, Jan Holte Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers. Fertility and Sterility. 2017;107(3):641-648.e2 DOI: 10.1016/j.fertnstert.2016.12.005

8. Gadzhimuradova N.D. Sostoyanie zdorov'ya i prognozirovanie ego narushenii u detei pervogo goda zhizni, rodivshikhsya ot odnoplodnoi beremennosti posle ehkstrakorporal'nogo oplodotvoreniya [dissertation]. Perm; 2017. (In Russ) Available at: http://www.dslib.net/pediatria/sostojanie-zdorovja-i-prognozirovanie-ego-narushenij-udetej-pervogo-goda-zhizni.html (accessed 18.02.2020).

9. Anaconda - solutions for Data Science Practitioners and Enterprise Machine Learning. Available at: https://www.anaconda.com (accessed 18.02.2020)

10. Morphological analyzer pymorphy2. Available at: https://pymorphy2.readthedocs.io/en/latest/ (accessed 18.02.2020).

11. Pedregosa et al. Scikit-learn: Machine Learning in Python. JMLR. 2011;12:2825-2830.

12. Wilson E. B. Probable inference, the law of succession, and statistical inference. Journal of American Statistical Association. 1927;22:209-212.

13. Grjibovski А.М. Confidence intervals for proportions. Human Ecology. 2008;5:57-60. (In Russ)

14. Kolbaya T.T. Beremennost' i rody u zhenshchin razlichnykh vozrastnykh grupp [dissertation]. Moscow; 2011. (In Russ) Available at: http://medicaldiss.com/medicina/beremennost-i-rody-u-zhenschin-razlichnyh-vozrastnyh-grupp (accessed 18.02.2020)

15. Grjibovski А.М. Analysis of nominal data (independent observations). Human Ecology. 2008;6:58-68. (In Russ)

16. Glantz S. Primer of biostatistics. М.: Practica;1998. (In Russ)