Analysis of correlation factors and pregnancy outcomes of hypertensive disorders of pregnancy - a secondary analysis of a random sampling in Beijing, China.

OBJECTIVE: We aimed to assess the prevalence and risk factors for hypertensive disorders and to study the main pregnancy outcomes in the Beijing area of China. STUDY DESIGN: This study randomly sampled 15 hospitals in Beijing from Jun 2013 to Nov 2013 and evaluated 15 194 deliveries. Logistic regression analysis was used to study the association between risk factors and hypertensive disorders. Pregnancy outcomes included preterm birth, cesarean delivery and small for gestational age (SGA). RESULTS: The prevalence of hypertensive disorders, preeclampsia (PE) and severe PE was 4.4, 2.7 and 1.8%, respectively. The risk factors for hypertensive disorders and severe PE were maternal body mass index before pregnancy, gestational weight gain (GWG), gestational diabetes and pre-gestational diabetes, and third trimester cholesterol (CHOL) levels. First trimester high-density lipoprotein was a protective factor for severe PE. The incidence of hypertensive disorders increased with maternal age. Preterm delivery, cesarean delivery and small infant size for gestational age were more prevalent in the severe PE group compared with the non-hypertensive group. CONCLUSIONS: In the Beijing area of China, maternal body mass index before pregnancy, GWG, maternal complications of gestational diabetes and pre-gestational diabetes, and third trimester CHOL levels are risk factors for both hypertensive disorders of pregnancy and severe PE. First trimester high-density lipoprotein is a protective factor for severe PE. Severe preeclampsia leads to a higher incidence of preterm delivery, cesarean delivery and SGA infants.

Risk of adverse pregnancy outcomes stratified for pre-pregnancy body mass index.

OBJECTIVE: To estimate the risk of adverse maternal and perinatal outcomes in women with different pre-pregnancy body mass index (BMI). METHODS: We conducted a cohort study with 14 451 singleton pregnancies in 15 medical centers in Beijing between 20 June 2013 and 30 November 2013 using cluster random sampling. We divided participants into four groups based on pre-pregnancy BMI: Group A (underweight): BMI < 18.5 kg/m(2), Group B (normal): 18.5-23.9 kg/m(2), Group C (overweight): 24-27.9 kg/m(2), Group D (obesity): ≥28 kg/m(2). We used multivariate analysis to evaluate the association of the risk of adverse pregnancy outcomes and pre-pregnancy BMI. RESULTS: The prevalence of maternal overweight and obesity was 14.82% (2142/14 451) and 4.71% (680/14 451) in the study population, respectively. Higher pre-pregnancy BMI is associated with higher prevalence of gestational diabetes (GDM), macrosomia, Cesarean section (C-section), preeclampsia and postpartum hemorrhage. Pre-pregnancy overweight or obesity increases the risk of adverse pregnancy outcomes, regardless of GDM status. CONCLUSIONS: Pre-pregnancy overweight or obesity is associated with increased risk of adverse pregnancy outcomes. Nutrition counseling is recommended before pregnancy in women who have overweight or obesity.

Fast and accurate registration techniques for affine and nonrigid alignment of MR brain images.

Registration of magnetic resonance brain images is a geometric operation that determines point-wise correspondences between two brains. It remains a difficult task due to the highly convoluted structure of the brain. This paper presents novel methods, Brain Image Registration Tools (BIRT), that can rapidly and accurately register brain images by utilizing the brain structure information estimated from image derivatives. Source and target image spaces are related by affine transformation and non-rigid deformation. The deformation field is modeled by a set of Wendland's radial basis functions hierarchically deployed near the salient brain structures. In general, nonlinear optimization is heavily engaged in the parameter estimation for affine/non-rigid transformation and good initial estimates are thus essential to registration performance. In this work, the affine registration is initialized by a rigid transformation, which can robustly estimate the orientation and position differences of brain images. The parameters of the affine/non-rigid transformation are then hierarchically estimated in a coarse-to-fine manner by maximizing an image similarity measure, the correlation ratio, between the involved images. T1-weighted brain magnetic resonance images were utilized for performance evaluation. Our experimental results using four 3-D image sets demonstrated that BIRT can efficiently align images with high accuracy compared to several other algorithms, and thus is adequate to the applications which apply registration process intensively. Moreover, a voxel-based morphometric study quantitatively indicated that accurate registration can improve both the sensitivity and specificity of the statistical inference results.

Differences in white matter abnormalities between bipolar I and II disorders.

BACKGROUND: Although patients with bipolar I and II disorders exhibit heterogeneous clinical presentations and cognitive functions, it remains unclear whether these two subtypes have distinct neural substrates. This study aimed to differentiate the fiber abnormalities between bipolar I and II patients using diffusion tensor images. METHOD: Fourteen bipolar I patients, thirteen bipolar II patients, and twenty-one healthy subjects were recruited. Fractional anisotropy (FA) values calculated from diffusion tensor images were compared among groups using two-sample t-test analysis in a voxel-wise manner. Correlations between the mean FA value of each survived area and the clinical characteristics as well as the scores of neuropsychological tests were further analyzed. RESULTS: Patients of both subtypes manifested fiber impairments in the thalamus, anterior cingulate, and inferior frontal areas, whereas the bipolar II patients showed more fiber alterations in the temporal and inferior prefrontal regions. The FA values of the subgenual anterior cingulate cortices for both subtypes correlated with the performance of working memory. The FA values of the right inferior frontal area of bipolar I and the left middle temporal area of bipolar II both correlated with executive function. For bipolar II patients, the left middle temporal and inferior prefrontal FA values correlated with the scores of YMRS and hypomanic episodes, respectively. CONCLUSIONS: Our findings suggest distinct neuropathological substrates between bipolar I and II subtypes. The fiber alterations observed in the bipolar I patients were majorly associated with cognitive dysfunction, whereas those in the bipolar II patients were related to both cognitive and emotional processing.

Accurate and robust extraction of brain regions using a deformable model based on radial basis functions.

Brain extraction from head magnetic resonance (MR) images is a classification problem of segmenting image volumes into brain and non-brain regions. It is a difficult task due to the convoluted brain surface and the inapparent brain/non-brain boundaries in images. This paper presents an automated, robust, and accurate brain extraction method which utilizes a new implicit deformable model to well represent brain contours and to segment brain regions from MR images. This model is described by a set of Wendland's radial basis functions (RBFs) and has the advantages of compact support property and low computational complexity. Driven by the internal force for imposing the smoothness constraint and the external force for considering the intensity contrast across boundaries, the deformable model of a brain contour can efficiently evolve from its initial state toward its target by iteratively updating the RBF locations. In the proposed method, brain contours are separately determined on 2D coronal and sagittal slices. The results from these two views are generally complementary and are thus integrated to obtain a complete 3D brain volume. The proposed method was compared to four existing methods, Brain Surface Extractor, Brain Extraction Tool, Hybrid Watershed Algorithm, and Model-based Level Set, by using two sets of MR images as well as manual segmentation results obtained from the Internet Brain Segmentation Repository. Our experimental results demonstrated that the proposed approach outperformed these four methods when jointly considering extraction accuracy and robustness.