Diagnostic accuracy of first and early second trimester multiple biomarkers for prediction of gestational diabetes mellitus: a multivariate longitudinal approach.

BACKGROUND: Gestational Diabetes Mellitus (GDM) is an underlying cause of maternal and newborn morbidity and mortality all around the world. Timely diagnosis of GDM plays an important role in reducing its adverse consequences and burden. This study aimed to determine diagnostic accuracy of multiple indicators in complete blood count (CBC) test for early prediction of GDM. METHODS: In this prospective cohort study, the data from 600 pregnant women was analyzed. In the study sample, the two-step approach was utilized for the diagnosis of GDM at 24-28 weeks of gestation. We also used the repeated measures of hemoglobin (Hb), hematocrit (Hct), fasting blood sugar (FBS) and red blood cell count (RBC) in the first and early second trimesters of pregnancy as the longitudinal multiple indicators for early diagnosis of GDM. The classification of pregnant women to GDM and non-GDM groups was performed using a statistical technique based on the random-effects modeling framework. RESULTS: Among the sample, 49 women (8.2%) were diagnosed with GDM. In the first and early second trimester of pregnancy, the mean HcT, Hb and FBS of women with GDM was significantly higher than non-GDMs (P < 0.001). The concurrent use of multiple longitudinal data from HcT, Hb, RBC and FBS in the first and early second trimester of pregnancy resulted in a sensitivity, specificity and area under the curve (AUC) of 87%, 70% and 83%, respectively, for early prediction of GDM. CONCLUSIONS: In general, our findings showed that the concurrent use of repeated measures data on Hct, Hb, FBS and RBC in the first and early second trimester of pregnancy might be utilized as an acceptable tool to predict GDM earlier in pregnancy.

Longitudinal discriminant analysis of hemoglobin level for predicting preeclampsia.

BACKGROUND: Preeclampsia is one of the most serious complications during pregnancy with important effects on health of mother and fetus that causes maternal and fetal morbidity and mortality. This study was performed to evaluate whether high levels of hemoglobin may increase the risk of preeclampsia. OBJECTIVES: The present study aimed to predict preeclampsia by the hemoglobin profiles through longitudinal discriminant analysis and comparing the error rate of discrimination in longitudinal and cross sectional data. PATIENTS AND METHODS: In a prospective cohort study from October 2010 to July 2011, 650 pregnant women referred to the prenatal clinic of Milad Hospital in Tehran were evaluated in 3 stages. The hemoglobin level of each woman was measured in the first, second, and third trimester of pregnancy by an expert technician. The subjects were followed up to delivery and preeclampsia was the main outcome under study. The covariance pattern and linear-mixed effects models are common methods that were applied for discriminant analysis of longitudinal data. Also Student t, Mann-Whitney U, and chi-square tests were used for comparing the demographic and clinical characteristics between two groups. Statistical analyses were performed using the SAS software version 9.1. RESULTS: The prevalence rate of preeclampsia was 7.2% (47 women). The women with preeclampsia had a higher mean of hemoglobin values and the difference was 0.46 g/dL (P = 0.003). Also the mean of hemoglobin in the first trimester was higher than that of the second trimester, and was lower than that of the third trimester and the differences were significant (P = 0.015 and P < 0.001, respectively). The sensitivity for longitudinal data and cross-sectional data in three trimesters was 90%, 67%, 72%, and 54% and the specificity was 88%, 55%, 63%, and 50%, respectively. CONCLUSIONS: The levels of hemoglobin can be used to predict preeclampsia and monitoring the pregnant women and its regular measure in 3 trimesters help us to identify women at risk for preeclampsia.

Generalization of the receiver-operating characteristic curve to determine the normal hemoglobin range cutoff points in pregnant women.

BACKGROUND: Identification of a normal range for biomarkers, based on pregnancy outcomes (caused by their high or low values) is of special importance in clinical studies. As some pregnancy outcomes can happen in both high and low levels of biomarkers, the receiver-operating characteristic (ROC) curve is unsuitable for identifying these levels separately; rather, a statistical method is preferable which identifies both levels simultaneously. OBJECTIVES: To this effect, our research introduces a generalization of ROC curve (by using a number of related consequences) to identify a normal range for the biomarker. Practically, the study intends to identify a normal range of hemoglobin in the first trimester of pregnancy to prevent adverse outcomes that can be caused by high and low levels of hemoglobin. PATIENTS AND METHODS: The current article introduces an ROC generalization curve to determine a normal range for biomarkers based on a number of pregnancy outcomes, which may occur in high and low levels of biomarkers. Simulated data were also used to compare the current method with the ROC curve method. Our data collected from a cohort study carried out on 600 pregnant women referring to Milad Hospital in Tehran, Iran in 2010. The data comprised hemoglobin level in the first trimester of pregnancy as well as pregnancy outcomes such as preterm delivery, low birth weight, preeclampsia, and gestational diabetes. We calculated an estimation of the normal range of hemoglobin for the study population. Statistical analysis was carried out by R software, version 3.0.2. RESULTS: Results from the simulation study indicated that, the new method was better than the methods which used two ROC curves separately with regard to sensitivity and specificity. In this method, the level of normal hemoglobin in the first trimester ranged from 10 to 12.4 with sensitivity and specificity levels of 76.2% and 48% respectively, which is higher than previous studies. CONCLUSIONS: With regard to the normal range of biomarkers, our method yielded greater sensitivity and specificity levels than methods using the ROC curve, which separately analyzes the data, particularly in occasions with common consequences in high and low levels of the biomarker.