Objective:To analyze the correlation of glycosylated hemoglobin (HbA 1c) level in the late pregnancy gestational diabetes mellitus (GDM) patients and fetal weights, neonatal Apgar scores, maternal and infant adverse outcomes. Methods:One hundred and eighty-seven pregnant women who were diagnosed with GDM from January 2015 to July 2019 and delivered in Yixing People′s Hospital after standard diagnosis and treatment were divided into four groups (A group: HbA 1c<6.0%, 65 cases; B group: HbA 1c: 6.0% - 6.5%, 49 cases; C group: HbA 1c 6.6%-7.0%, 39 cases; D group: HbA 1c>7.0%, 34 cases) according to the HbA 1c examination results at 28 to 32 weeks of gestation. General data, fetal weights, neonatal Apgar scores and maternal and infant adverse outcomes were compared among the four groups. The correlation between GDM HbA 1c and fetal weights, neonatal Apgar scores and maternal and infant adverse outcomes were analyzed by unconditional Logistic regression. Results:In general data of GDM pregnant women with different HbA 1c levels, only oral glucose tolerance test (OGTT) fasting blood glucose: (4.68 ± 0.60), (4.89 ± 0.69), (5.23 ± 0.90), (6.48 ± 2.17) mmol/L; postprandial 1 h blood glucose: (9.84 ± 1.56), (10.09 ± 1.84), (10.6 ± 2.01), (12.74 ± 4.12) mmol/L; postprandial 2 h blood glucose: (8.65 ± 1.49), (8.86 ± 1.76), (9.28 ± 2.15), (11.56 ± 4.93) mmol/L, showed statistically significant differences ( P<0.05). Among the newborns of GDM pregnant women with different HbA 1c levels, there were statistically significant differences in the macrosomic infant rates: 1.54%(1/65), 10.20%(5/49), 12.82%(5/39), 17.65%(6/34); rates of neonatal Apgar scores<7 points:13.85%(9/65), 16.33%(8/49), 25.64%(10/39), 44.12%(15/34); the proportion of maternal and infant adverse outcomes: 24.62%(16/65), 24.49%(12/49), 28.21%(11/39), 50.00%(17/34), showed statistically significant differences ( P<0.05). After adjusting OGTT by unconditional Logistic regression analysis, HbA 1c (6.6%-7.0% and>7.0%) was independent risk factor for macrosomic infants: OR = 1.430, 95% CI = 1.035-1.977, P = 0.030; OR = 2.042, 95% CI = 1.311-3.180, P = 0.001; maternal and infant adverse outcomes: OR = 1.774, 95% CI = 1.130-2.874, P = 0.010; OR = 3.387, 95% CI = 1.608-7.133, P = 0.001. HbA 1c>7.0% was independent risk factors for neonatal Apgar scores<7 points: OR = 1.848 95% CI = 1.086-3.143, P = 0.023. Conclusions:There was a significant correlation between HbA 1c in GDM pregnant women in the late pregnancy and macrosomic infants, neonatal Apgar scores, and maternal and infant adverse outcomes. In particular, GDM pregnant women with HbA 1c>7.0% should be alert to the risk of macrosomic infants, neonatal Apgar score<7 points, and maternal and infant adverse outcomes.

Objective:To investigate the value of pericoronary adipose tissue histogram parameters based on coronary CT angiography (CTA) images for the differentiation of acute coronary syndrome and stable coronary artery disease.Methods:The clinical data and CTA images of 93 patients with coronary CTA examination in Suzhou Kowloon Hospital from 2013 to 2018 were analyzed retrospectively. There were 39 patients with acute coronary syndrome (acute coronary syndrome group) and 54 patients with stable coronary artery disease (stable coronary artery disease group). A region of interest (ROI) was drawn around the stenosis of the coronary arteries, with CT attenuation ranging from-190 to -30 HU to exclude non-adipose tissue. The CT attenuation of ROI excluding non-adipose were measured and histogram analysis was performed. The obtained parameters included the mean value, median value and the 5th, 10th, 45th, 55th, 70th and 95th percentiles. The differences in histogram parameters between the two groups were compared, and then the value of each parameter in differentiating acute coronary syndrome and stable coronary artery disease was evaluated based on receiver operating characteristic (ROC) analysis. The stepwise regression of multivariate logistic regression analysis was used to identify the useful features and establish the final prediction model. The ROC curve of the final model was calculated and its value was analyzed.Results:The mean, median and the 5th, 10th, 45th, 55th,70th and 95th percentile differences between the acute coronary syndrome group and the stable coronary artery disease group were statistically significant (all P<0.05). The ROC curve for the median and the 95th percentile had the same area under curve (AUC) of 0.73. The sensitivity, specificity and AUC of the diagnostic model established by multiple logistic regression were 82.1%, 89.1% and 0.90 respectively. Conclusion:CT attenuation histogram of pericoronary adipose tissue is of high value in differentiating acute coronary syndrome from stable coronary artery disease.