Risk factors of middle lobe bronchus kinking following right upper lobectomy

BackgroundThe incidence rate of kinking of the middle lobe bronchus following right upper lobectomy is higher compared to that with residual lung bronchus following other lobectomies. Bronchial kinking was presumed to be caused by the displacement of the residual lung lobes, but its etiology is unclear. Moreover, prevention methods and effective treatments have not yet been established. The purpose of this study was to investigate the risk factors and etiology of middle lobe bronchus kinking and discuss prevention methods.MethodsPatients who underwent right upper lobectomy in our hospital were retrospectively evaluated. Patient clinical characteristics, lung function, and lung lobe volume, surgical procedure were analyzed in association with the incidence of middle lobe bronchus kinking. The association between the displacement of residual lung lobes after operation and the incidence of middle lobe bronchus kinking was analyzed to assess the etiology.ResultsA total of 175 patients were enrolled in the risk analysis. Middle lobe bronchus kinking was observed in 5 patients (2.9%). The low percentage of forced expiratory volume percentage in 1 second (P=0.021), the low volume ratio of the right middle lobe (RML) to the right thoracic cavity (RTC) (P=0.016), and the low volume ratio of RML to right upper lobe (RML/RUL) (P=0.006) were significant risk factors of middle lobe bronchus kinking. In the patients who underwent CT at 6 months after surgery, the degree of the cranial displacement of RML was associated with the incidence of middle lobe bronchus kinking (P=0.025).ConclusionsThe risk of middle lobe bronchus kinking could be assessed preoperatively by calculating the volume ratio of RML/RTC and RML/RUL. The displacement of RML could be associated with the incidence of middle lobe bronchus kinking.     

The epidemiology of non-alcoholic steatohepatitis (NASH) in the United States between 2010-2020: a population-based study

Introduction and objectivesNon-alcoholic steatohepatitis (NASH) is a severe form of non-alcoholic fatty liver disease (NAFLD) that can progress to liver cirrhosis, liver failure and hepatocellular carcinoma. It is the second leading cause of liver transplant in the US. We aim to investigate the prevalence, demographics and risk factors NASH patients in the US.Patients and methodsWe used a large database (Explorys IBM) that aggregates electronic health records from 26 nationwide healthcare systems. We identified adults with NASH between 2010-2020. Demographics including age, gender and race were collected. NASH risk factors including Diabetes Millets (DM), Hyperlipidemia (HLD), Hypertension (HTN) and Obesity were also collected. Cochran-Armitage test was used to assess the statistical significance of year-by-year trend. Univariable and multivariable logistic regression were used to estimate the odds ratio (OR) of risk factors.ResultsNASH annual prevalence rate increased from 1.51% in 2010 to 2.79% in 2020 (p < 0.0001). The proportion of patients with NASH by gender was 54.1% female vs 45.9% male (OR 1.04 [0.91-1.11]). Caucasian had higher odds of NASH than non-Caucasian (OR 1.42 [1.31-1.54]). NASH is strongly associated with DM and obesity (OR 18.61 [17.35-19.94]) and (OR 20.97 [17.87-23.21]), respectively. Other components of metabolic syndrome were associated with NASH to a lesser degree; HTN (OR 3.24 [3.20-3.28]) and HLD (OR 4.93 [4.85-4.01]).ConclusionThe prevalence of NASH has significantly increased in the US in the last decade. This is likely related to the increased prevalence of risk factors as well as increased awareness of the disease.     

NLR在COPD合并CAP与AECOPD患者中诊断与预后的意义

目的对比分析中性粒细胞与淋巴细胞比值(NLR)在慢性阻塞性肺疾病(COPD)合并社区获得性肺炎(CAP)和COPD急性加重期(AECOPD)患者中的鉴别诊断价值及评估预后的潜力。 方法选择2017年1月至2019年12月在哈尔滨医科大学附属第一医院呼吸内科及重症监护病房(ICU病房)符合条件的1 215例COPD患者为对象,依据诊断标准分为COPD合并CAP组783例和AECOPD组432例。使用Spearman相关分析,分析NLR与其他炎性指标(WBC、PCT、CRP、FIB)的相关性;绘制受试者工作特征(ROC)曲线,分析NLR对于COPD合并CAP与AECOPD的诊断价值及评估预后的潜力。 结果Spearman相关分析显示：两组的NLR与其他炎性指标均有明显相关性(P<0.001);ROC曲线分析显示对于3.987的NLR的截断值,鉴别两组的灵敏度为56%,特异性66%,AUC为0.627;NLR在预测ICU入住率、住院期间病死率、有创及无创机械通气使用率方面,COPD合并CAP组AUC分别为：0.79、0.773、0.791、0.726;AECOPD组AUC分别为：0.874、0.915、0.862、0.74。 结论NLR水平在一定程度上可以鉴别COPD合并CAP与AECOPD,但准确性不高;NLR较其他炎性指标在预测ICU入住率、住院期间病死率、机械通气使用率方面表现出显著优势。     

FibroScan行肝脏硬度检测和APRI评估慢性丙型肝炎患者肝纤维化程度价值比较*

目的 探讨使用FibroScan行肝脏硬度检测(LSM)和应用门冬氨酸氨基转移酶/血小板比值(APRI)评估慢性丙型肝炎(CHC)患者肝纤维化程度的价值。方法 2016年5月～2020年5月我院收治的CHC患者133例和同期健康体检者133名,接受FibroScan检查及血生化和血液检查,计算APRI。CHC患者接受肝穿刺活检。结果 CHC患者LSM和APRI分别为(10.3±4.2)和(0.8±0.3),显著高于健康人【分别为(4.3±2.0)和(0.3±0.1),P＜0.05】;52例S₁期患者LSM和APRI分别为(6.5±2.4)和(0.6±0.2),37例S₂期患者LSM和APRI分别为(10.3±2.9)和(0.9±0.3),28例S₃期患者LSM和APRI分别为(14.5±4.1)和(1.2±0.5),16例S₄期患者LSM和APRI分别为(18.4±5.7)和(1.8±0.6),相差显著(P＜0.05);经ROC分析LSM预测CHC患者显著肝纤维化(大于等于S2)的曲线下面积(AUC)为0.891,标准误为0.033,P=0.000,95%可信区间为0.826～0.956,最佳截断点为11.200,其诊断的敏感度为0.625,特异度为0.925,而APRI预测CHC患者显著肝纤维化的AUC为0.776,标准误为0.050,P=0.000,95%可信区间为0.678～0.875,最佳截断点为0.795,其敏感度为0.643,特异度为0.887;经相关性分析发现,LSM和APRI与CHC患者肝纤维化程度呈正相关(P＜0.05)。结论 应用LSM和APRI评估CHC患者肝纤维化程度有一定的临床价值,特别是对于存在显著肝纤维化的患者,可早期作出病情判断,对指导临床处理有很大的帮助,值得进一步研究。     

基于2018年版肝脏影像报告数据系统对比分析CT和MRI诊断肝细胞癌的效能*

目的 比较CT和MRI按照2018年版肝脏影像报告数据系统(LI-RADS)诊断肝细胞癌(HCC)的效能。方法 2017年9月～2020年7月四川省肿瘤医院诊治的有HCC高危因素的72例患者(HCC 53例、非HCC恶性肿瘤10例、良性病变9例),接受CT和MRI检查。根据2018年版LI-RADS定义的影像学征象和分类法则,对所有病灶进行分类,采用kappa一致性检验两种检查方法的分类结果,以组织病理学检查结果为金标准,比较CT和MRI诊断HCC的ROC曲线下面积(AUC),计算LR-5类诊断HCC的敏感性和特异性。结果 CT和MRI的LI-RADS分类结果一致性较好,kappa值为0.693【(95%CI:0.545～0.841),P＜0.001】;两种检查方法诊断HCC的AUC分别为0.827(95%CI: 0.708～0.946)和0.856(95%CI: 0.761～0.952),差异无统计学意义(P＞0.05);以LR-5为阳性,MRI诊断HCC的敏感性为81.1%(43/53),显著高于CT诊断的66.0%(35/53),差异有统计学意义(P＜0.05);CT和MRI诊断HCC的特异性分别为78.9%(15/19)和89.5%(17/19),差异无统计学意义(P＞0.05);在LI-RADS定义的主要征象中,MRI对强化包膜的显示率为40.3%,显著高于CT的5.5%(P＜0.001)。结论 基于肝脏影像报告数据系统,CT和MRI诊断HCC有相当高的效能,而MRI诊断肝细胞癌的敏感性高于CT,特别在显示强化病灶包膜方面优于CT。     

NFATc4和IL-6与非瓣膜性心房颤动的相关性研究

目的：探究活化T细胞核因子4(NFATc4)、炎症细胞因子白介素6（IL-6）水平与房颤的关系及其对房颤的诊断价值,同时分析房颤发病的危险因素。方法：选取2019年06月至2019年12月就诊于蚌埠医学院第一附属医院心内科诊断非瓣膜性心房颤动患者80例作为研究对象,其中阵发性房颤患者40例,持续性房颤患者40例。随机选取同期我院体检健康者（窦性心律）38例作为对照组。收集患者临床资料,采用超声心动图测量受试者左房内径值（LAD）、左室射血分数（LVEF）等值。并于次日清晨抽取受试者空腹状态下静脉血约5ml,采用酶联免疫吸附法（ELISA）检测血清NFATc4、IL-6水平,记录受试者血常规、生化常规中的尿酸值（SUA）、中性粒细胞和淋巴细胞比值（NLR）。比较各组间指标的差异,探究NFATc4、IL-6、SUA、NLR与房颤之间的关系,采用二元logistic回归分析房颤发病的影响因素,采用受试者工作特征（ROC）曲线分析血清NFATc4、IL-6、SUA、NLR水平对房颤发病的预测价值。结果：(1).阵发性房颤组和持续性房颤组中血清NFATc4、IL-6、SUA、NLR值及LAD明显高于对照组,且在阵发性房颤组和持续性房颤组间亦有统计学差异（P<0.05）;(2).房颤患者外周血中NFATc4、IL-6、SUA、NLR表达水平与LAD存在正相关性(r=0.481、0.637、0.331、0.336,P<0.05)。(3).采用二元logistic回归分析NFATc4、IL-6、SUA、NLR是房颤发病的危险因素(P<0.05)。(4). 用受试者工作特征（ROC）曲线发现血清NFATc4、IL-6、SUA、NLR值对房颤发病的具有预测价值（曲线下的面积分别是0.797、0.894、0.701、0.708）。结论：NFATc4、IL-6、SUA、NLR表达水平与房颤的持续时间有关,是房颤发病的危险因素,对房颤发生具有预测价值。     

Does C-reactive protein to serum Albumin Ratio correlate with diabEtic nephropathy in patients with Type 2 dIabetes MEllitus? The CARE TIME study

AimsDiabetic Nephropathy (DN) is a complication of Diabetes Mellitus and is associated with chronic and low-grade inflammatory burden. Novel inflammatory predictors, such as, C-reactive protein to serum albumin ratio (CAR) has been studied various inflammatory conditions, recently. Increased inflammatory burden accompany to both type 2 Diabetes Mellitus (T2DM) and DN, hence we aimed to compare CAR levels of the T2DM subjects with DN to those of without DN.MethodsPatients with T2DM were enrolled to the study. Study population grouped into two according to the presence (group A) or absence (group B) of DN. Characteristics and laboratory data, as well as CAR levels; of the study groups were compared.ResultsMedian CAR levels of the groups A and B were 2.17% (0.02−13.2) and 0.39% (0.02−4.39), respectively (p < 0.001). CAR was found to be an independent risk factor for diabetic nephropathy (adjusted to age, BMI, fasting glucose, HbA1c, and body weight). One unit (0.1%) elevation in CAR increased the risk of nephropathy by 3.5 folds (p < 0.001, 95%CI: 2.24–5.45). CAR levels greater than 0.82% have 79% sensitivity and 78% specificity in predicting DN (AUC: 0.86 [95% CI: 0.80−0.92]; p < 0.001).ConclusionsIn conclusion, elevated CAR levels are higher in type 2 diabetic patients with diabetic nephropathy. According to the ROC curve, a level higher than 0.82% presents the best sensitivity and specificity in the association with the presence of DN.     

Noninherited Factors in Fetal Congenital Heart Diseases Based on Bayesian Network: A Large Multicenter Study

Background: Current studies have confirmed that fetal congenital heart diseases (CHDs) are caused by various factors. However, the quantitative risk of CHD is not clear given the combined effects of multiple factors. Objective: This cross-sectional study aimed to detect associated factors of fetal CHD using a Bayesian network in a large sample and quantitatively analyze relative risk ratios (RRs). Methods: Pregnant women who underwent fetal echocardiography (N = 16,086 including 3,312 with CHD fetuses) were analyzed. Twenty-six maternal and fetal factors were obtained. A Bayesian network is constructed based on all variables through structural learning and parameter learning methods to find the environmental factors that directly and indirectly associated with outcome, and the probability of fetal CHD in the two groups is predicted through a junction tree reasoning algorithm, so as to obtain RR for fetal CHD under different exposure factor combinations. Taking into account the effect of gestational week on the accuracy of model prediction, we conducted sensitivity analysis on gestational week groups. Results: The single-factor analysis showed that the RRs for the numbers of births, spontaneous abortions, and parental smoking were 1.50, 1.38, and 1.11 (P < 0.001), respectively. The risk gradually increased with the synergistic effect of ranging from one to more environmental factors above. The risk was higher among subjects with five synergistic factors, including the number of births, upper respiratory tract infection during early pregnancy, anemia, and mental stress as well as a history of spontaneous abortions or parental smoking, than in those with less than 5 factors (RR = 2.62 or 2.28, P < 0.001). This result was consistent across the participants grouped by GWs. Conclusion: We identified six factors that were directly associated with fetal CHD. A higher number of these factors led to a higher risk of CHD. These findings suggest that it is important to strengthen healthcare and prenatal counseling for women with these factors.