【摘要】 目的 分析慢性乙肝患者血清生化、血常规、血清病毒载量及乙型肝炎标志物与肝组织炎症分级、纤维化分期的相关性,以找到有较好相关性的临床指标；通过肝活检证实临床诊断与病理诊断的符合情况,探讨肝活检的重要性及价值。方法 对2007年6月—2009年8月在传染科行肝穿刺活检的359例慢性乙型肝炎患者的血清丙氨酸氨基转移酶(ALT)、门冬氨酸氨基转移酶(AST)、总胆红素(TB)、白蛋白(ALB)、球蛋白(GLB)等指标,白细胞(WBC)、血小板(PLT)等指标,凝血酶原时间(PT),血清HBV DNA定量及乙肝标志物的不同状态与肝穿病理分级、分期的相关性进行分析；统计慢性乙肝患者临床诊断与病理诊断的符合情况。结果 肝组织炎症分级及纤维化分期之间有一定相关性(Plt;0.05)；血清ALT、AST、ALB、GLB、PT有助于判断肝组织炎症程度(Plt;0.05)；ALB、GLB、WBC、PLT、PT对肝组织纤维化程度的评估有意义(Plt;0.05)；HBV DNA复制水平与肝组织炎症及纤维化无关(Pgt;0.05),但存在负相关的趋势；纤维化程度高的患者HBeAg阴性组较HBeAg阳性组更多(Plt;0.05)。慢性乙型肝炎患者临床与病理诊断总符合率为56.3％。结论 动态监测慢性乙肝患者肝功能、血常规、凝血常规在一定程度上有助于判断疾病的程度,但要确诊肝组织炎症分级及纤维化分期,肝组织病理活检是必需的。
目的：了解正常妊娠妇女常用血常规指标的变化，建立其参考值范围。方法:分别在早孕（孕10～14周）、中孕（孕20～24周）、晚孕（孕30～34周）及产后（产后12周）四个时期序贯性测定120例正常妊娠妇女血常规指标:红细胞（RBC）、血红蛋白（HB）、红细胞压积（HCT）、血小板（PLT）、白细胞（WBC），并建立参考值范围。同期选取53例健康体检非孕者为对照。结果：RBC、HCT早、中、晚孕相对于正常对照均降低（Plt;0.01）。HB在孕期降低，中、晚孕降低较明显，相对于正常对照差异有统计学意义（Plt;0.01）。PLT早、中孕降低不明显（Pgt;0.05），到晚孕期相对于正常对照降低差异有统计学意义（Plt;0.05）。WBC在妊娠四期相对于正常对照均升高（Plt;0.01）。结论：血常规各项指标随孕期发展均产生不同程度的变化，从该序惯性研究中得出了正常妊娠妇女的5个血常规指标水平的参考值范围，该参考值范围可以用于评估正常妊娠妇女的孕期健康水平。
ObjectiveTo identify differences in blood routine indicators between lung cancer patients and healthy controls, and between different subgroups of lung cancer patients, so as to improve the early detection of lung cancer prognosis, and provide a basis for risk stratification and prognostic judgment for patients with lung cancer.MethodsThis study enrolled 1 227 patients pathologically diagnosed with lung cancer from December 2008 to December 2013 and 2 454 healthy controls 1∶2 matched by sex and age. The blood routine data of lung cancer patients were collected when they were first diagnosed with lung cancer. Gender and age stratified analysis of blood routine indicators between lung cancer patients and controls were conducted. Comparisons of blood routine indicators among lung cancer patients with different pathological types, stages, and prognosis were performed, followed by Cox regression survival analysis. Normally distributed quantitative variables were presented as mean ± standard deviation and non-normally distributed quantitative variables as medium (lower quartile, upper quartile).ResultsCompared to healthy controls, the counts of platelet [(206.84±80.47) vs. (175.27±55.74)×109/L], white blood cells [(7.04±2.29) vs. (6.08±1.40)×109/L], neutrophil [(4.90±2.08) vs. (3.61±1.07)×109/L], monocyte [0.42 (0.30, 0.54) vs. 0.33 (0.26, 0.42)×109/L], and eosinophil [0.14 (0.07, 0.24) vs. 0.12 (0.07, 0.19)×109/L], as the well as neutrophil-lymphocytes ratio (3.91±2.82 vs. 2.03±0.89) and platelet-lymphocyte ratio (160.35±96.06 vs. 96.93±38.02) in lung cancer patients increased significantly, while the counts of red blood cells [(4.41±0.58) vs. (4.85±0.51)×1012/L] and lymphocyte [(1.49±0.60) vs. (1.93±0.59)×109/L] in lung cancer patients decreased, and the differences were statistically significant (P<0.05). The counts of platelet, red blood cells, white blood cells, neutrophil, and monocyte differed among patients with different pathological types, tumor stages, and prognosis (P<0.05). Neutrophil-lymphocytes ratio and platelet-lymphocyte ratio were higher in squamous cell carcinoma patients than those in other pathological patients, higher in advanced lung cancer patients than those in early stage patients, and higher in dead lung cancer patients than those in survival patients (P<0.05). Neutrophil-lymphocyte ratio was an independent factor affecting the prognosis of lung cancer [hazard ratio=1.077, 95% confidence interval (1.051, 1.103), P<0.001].ConclusionsThe inflammatory index of blood routine indicators are higher in lung cancer patients than those in healthy controls, which indicates that lung cancer is closely related to chronic inflammation. There are significant differences in blood routine inflammation index among lung cancer patients with different pathological types, stages, and prognosis, which reflects the heterogeneity and complexity of lung cancer. Neutrophil-lymphocytes ratio inverse correlates with the prognosis of lung cancer.
Objective To establish and verify the early prediction model of critical illness patients with influenza. Methods Critical illness patients with influenza who diagnosed with influenza in the emergency departments from West China Hospital of Sichuan University, Shangjin Hospital of West China Hospital of Sichuan University, and Panzhihua Central Hospital between January 1, 2017 and June 30, 2020 were selected. According to K-fold cross validation method, 70% of patients were randomly assigned to the model group, and 30% of patients were assigned to the model verification group. The patients in the model group and the model verification group were divided into the critical illness group and the non-critical illness group, respectively. Based on the modified National Early Warning Score (MEWS) and the Simplified British Thoracic Society Score (confusion, uremia, respiratory, BP, age 65 years, CRB-65 score), a critical illness influenza early prediction model was constructed and its accuracy was evaluated. Results A total of 612 patients were included. Among them, there were 427 cases in the model group and 185 cases in the model verification group. In the model group, there were 304 cases of non-critical illness and 123 cases of critical illness. In the model verification group, there were 152 cases of non-critical illness and 33 cases of critical illness. The results of binary logistic regression analysis showed that age, hypertension, the number of days between the onset of symptoms and presentation at the emergency department, consciousness state, white blood cell count, and lymphocyte count, oxygen saturation of blood were the independent risk factors for critical illness influenza. Based on these 7 risk factors, an early prediction model for critical illness influenza was established. The correct percentages of the model for non-critical illness and critical illness patients were 95.4% and 77.2%, respectively, with an overall correct prediction percentage of 90.2%. The results of the receiver operator characteristic curve showed that the sensitivity and specificity of the early prediction model for critical illness influenza in predicting critical illness patients were 0.909, 0.921, and the area under the curve and its 95% confidence interval were 0.931 (0.860, 0.999). The sensitivity, specificity, and area under the curve (0.935, 0.865, 0.942) of the early prediction model for critical illness influenza were higher than those of MEWS (0.642, 0.595, 0.536) and CRB-65 (0.628, 0.862, 0.703). Conclusions The conclusion is that age, hypertension, the number of days between the onset of symptoms and presentation at the emergency department, consciousness, oxygen saturation, white blood cell count, and absolute lymphocyte count are independent risk factors for predicting severe influenza cases. The early prediction model for critical illness patients with influenza has high accuracy in predicting severe influenza cases, and its predictive value and accuracy are superior to those of the MEWS score and CRB-65 score.