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Archives of Iranian Medicine. 2011; 14 (6): 412-415
in English | IMEMR | ID: emr-137336

ABSTRACT

This study evaluated some risk factors for pre-eclampsia, which is one of the most problematic complications of pregnancy. This was a retrospective case control study conducted on 318 pre-eclamptic women [case group] and 318 women who were normotensive at the time of delivery as the control group. Evaluated factors were: maternal age, gestational age, nuliparity, mother's educational status, maternal body mass index [BMI], maternal hemoglobin and blood Rh, familial history of pre-eclampsia, history of pre-eclampsia in a previous pregnancy, marital relations, urinary infection [UTI] during the present pregnancy, season of delivery, and method of contraception. Risk factors for pre-eclampsia were: UTI [P=0.04]; history of pre-eclampsia during previous pregnancy [P=0.003], and winter season [P=0.001]. Maternal age of more than 20 years, high educational status of mother, parity more than one, and oral contraceptive pills were protective for pre-eclampsia. After adjusting for all possible confounding factors using multiple logistic regression, only preexisting pre-eclampsia [P=0.004] was a risk factor, whereas parity more than three [P=0.007] and anemia [P=0.01] were protective for pre-eclampsia. The rate of cesarean delivery was more common in the pre-eclamptic group [245 cases, 77%] than the control group [85 cases, 26.7%]. The one minute Apgar scored neonates less than 8 was more common in the case group [28.6% vs. 47.4%, P<0.001]. Gestational age at the time of delivery was lower in the case group [36.48 +/- 3.4 weeks vs. 37.12 +/- 3.3 weeks, P<0.001]. Awareness of risk factors of pre-eclampsia can help to monitor patients, ensure earlier diagnosis and predict which patients are more likely to develop pre-eclampsia


Subject(s)
Humans , Female , Pre-Eclampsia/epidemiology , Pregnancy , Retrospective Studies , Risk Factors , Statistics, Nonparametric , Apgar Score , Case-Control Studies , Cesarean Section/statistics & numerical data , Chi-Square Distribution , Logistic Models
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