مجله زنان مامائی و نازائی ایران
سال هفدهم شماره 123 (هفته اول آذر 1393)

Preeclampsia is one of the most common complications of pregnancy and oneof the main causes of maternal and neonatal mortality and morbidity. Excretion of protein in urine is the most important diagnostic sign of diagnosis of the patients with preeclampsia. Current gold standard test for quantitative evaluation of proteinuria is 24 hours urine collection, but, this is a time-consuming and cumbersome method, and using a method which can show urine protein as soon as possible could be effective in proceeding the treatment process. This study was performed with the aim to predict the proteinuria in patients suspected to preeclampsia with using the protein to creatinine ratio in random urine sample and determining the best cut off point. This cross-sectional, analytical study was performed on 103 pregnant women with gestational age >20 weeks referred to Emam Reza hospital complaining of BP≥140/90 mmHg or severe edema from 2013-2014. To measure the protein/creatinine ratio, a random urine sample was taken and then 24 hours urine collection was performed for determination of protein excretion. Data analysis was performed by SPSS software (version 16), and Chi-square, T-test, Man-Whitney and logistic regression. Among 103 pregnant women, protein excretion in 24-h urine was > 300mg in 53 cases and ≤ 300mg in 50 cases. The best cut off point for protein/creatinine ratio of random urine sample was obtained 0.27 with 96% sensitivity and 54% specificity and 70% positive predictive value and 96% negative predictive value (P<0.0001). Protein/creatinine ratio of random urine sample could be used as a simple and rapid test to rule out proteinuria in 93% of patients suspected to preeclampsia.

Breast cancer is the most common malignancy in women. Monthly breast self-examination is one of the easy and cost-effective methods that does not require specialized personnel and equipment and has been introduced as the best method of screening. Among these, this question is suggested that whether nurses and midwives women have complete knowledge about it so they can teach others? Therefore, this study was performed with the aim to assess the knowledge and attitudes of nursing and midwifery personnel about Breast Self-Examination (BSE). This descriptive-analytic study was performed on 89 cases of nursing and midwifery personnel of Borojen Valiasr hospital in 2012. For data collection, the researcher made questionnaire of examining knowledge and attitudes about BSE and demographic information were used. Data analysis was performed using SPSS software (version 21), independent t-test and spearman test. Mean level of knowledge about BSE in nurses was 9.48 ± 2.84 and in midwives 11 ± 1.52 that wit maximum score (20) was not in acceptable level. Mean level of attitude in nurses was 55.56 ± 5.09 and in midwives 55.23 ± 7.65. There were significant difference between mean levels of knowledge and attitude among participants (r=0.244, P=0.021). Nurses and midwives as the most populous community of health teams, not only do not have enough knowledge about BSE, but also do not have a correct attitude toward it. Therefore, it seems that it is necessary that educational planners and administrators of educating hospital staff should train this important health behavior to women staff.

Preeclampsia is a serious disorder during pregnancy that even can lead to termination of pregnancy and fetus abortion. This study was performed with the aim to generate an efficient predictive model to accurately predict the effectiveness of preeclampsia medications. This is a descriptive, cross-sectional study performed on 774 patients in Neyshabour Hakim Hospital from 2011-2013. Three algorithms, C5.0, C & R Tree and CHAID were applied on data of preeclampsia, and then the accuracy of generated models was obtained. In this study, Clementine software (version 12.0) was used for data analysis and implementation of data mining algorithms. The obtained values for accuracy of generated models by implementation of C5.0, C&R Tree and CHAID algorithms on train and test dataset were 99.63 and 99.14, respectively. The high accuracy of models showed good performance of these algorithms. The sensitivity of 100% and precision of 99.5% confirmed that C&R Tree is superior to other algorithms. Obtained results of clustering on better tree showed that when the effectiveness of medicine has been increased, the patient's mean age was increased, and it was due to direct relation between preeclampsia and increasing age. The obtained results show that the probability of preeclampsia is increased with increasing age and the medications with high effectiveness should be used for treatment. Therefore, more attention should be taken for selecting the used medicine with different effectiveness. Prediction of the effectiveness of medicine is performed for achieving these targets: help to the doctor for increasing the accuracy of diagnosis and prevention of incorrect diagnosis in dose of medicine consumption for patient, diagnosing the preeclampsia severity and inhibition of dangerous effects of extra consumption of medication by patient, prediction of the adequate medicine storage and prevention of medicine lack side-effects.