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@inproceedings{569828,
author = {Hudeček, Robert and Huser, Martin and Ventruba, Pavel and Chovanec, Josef and Šarmanová, Jana and Šarman, Zdenk},
address = {Athens},
booktitle = {Book of abstracts XVIII th congres of EBCOG},
edition = {1;2004},
keywords = {Ovarian hyperstimulation syndrome; risk factors; data mining},
language = {eng},
location = {Athens},
pages = {215-216},
publisher = {EBCOG},
title = {Ovarian hyperstimulation syndrome analysis of risk factors using data mining methods},
year = {2004}
}
TY - JOUR
ID - 569828
AU - Hudeček, Robert - Huser, Martin - Ventruba, Pavel - Chovanec, Josef - Šarmanová, Jana - Šarman, Zdenk
PY - 2004
TI - Ovarian hyperstimulation syndrome analysis of risk factors using data mining methods
PB - EBCOG
CY - Athens
KW - Ovarian hyperstimulation syndrome
KW - risk factors
KW - data mining
N2 - OVARIAN HYPERSTIMULATION SYNDROME - ANALYSIS OF RISK FACTORS USING DATA MINING METHODS Hudeek, R.1), Huser, M.1), Ventruba, P.1), Chovanec, J. 1), Šarmanová, J.2), Šarman, Z.2): 1) Dept.of Gynecology and Obstetrics, Masaryk University, Brno, Czech Republic, 2) Dept.of Electrical Engineering and Computer Science, Technical University, Ostrava, Czech, Republic. Introduction: Development of explorative computer data analysis of ovarian stimulation phase of assisted reproduction (AR) cycle using the system for data mining SHLUK. The study focuses on ovarian hyperstimulation risk factors analysis of the clinical data using data mining method. Materials and method: Analyzed file included data about 12 527 AR cycles in Dept. of Gynecology and Obstetrics, Masaryk University, Brno. Cycles leaded to development of ovarian hyperstimulation syndrom (OHSS) were analyzed (2456 cases, 19,6 % of cycles). Both the OHSS complicated cases and cases without ovarian hyperstimulation was tested by data mining ACETN method which is designed to find statistically significant differences among input attributes of ovarian stimulation phase of AR cycle. The observed differences between input attributes were statistically tested and the value of statistical significance was calculated. Results: The frequency of ovarian hyperstimulation according to clinical and laboratory relevance was counted as follows: grade I. OHSS in 592 cycles (4,7%), grade II. OHSS in 762 cycles (6,1%), grade III. OHSS in 614 cycles (4,9%) and grade IV. OHSS in 488 cases (3,9%). Significantly higher incidence of OHSS was observed among patients under 30 years old, patients with OHSS in previous cycle and among couples with imunology and andrology factor of infertility. Ovarian stimulation with recombinant FSH combined with GnRH agonists or antagonists significantly raised the frequency of OHSS development in comparison with protocols using urinary FSH and clomifen citrate. Conclusions: We have proved the SHLUK data mining system applicable in the multivariable analysis of assisted reproduction cycles inputs and outcomes. Method ACETN makes possible to define statistically significant relations between individual attributes of the ovarian stimulation stage of AR cycle. Supported by IGA, No.: 7696-3, Ministry of Health of Czech Rep.
ER -
HUDEČEK, Robert, Martin HUSER, Pavel VENTRUBA, Josef CHOVANEC, Jana ŠARMANOVÁ a Zdenk ŠARMAN. Ovarian hyperstimulation syndrome analysis of risk factors using data mining methods. In \textit{Book of abstracts XVIII th congres of EBCOG}. 1;2004. Athens: EBCOG, 2004, s.~215-216.
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