摘要
目的 分析X-pert联合核苷酸结合寡聚化结构域蛋白2(NOD2)、自噬相关蛋白16样蛋白1(ATG16L1)在活动性肺结核患者疾病转归评估中的应用价值.方法 前瞻性选取2023年4月至2024年4月池州市人民医院收治的110例活动性肺结核患者为研究对象.所有患者均行抗结核治疗、疾病转归评估,将转归患者60例作为转归组,未转归患者50例作为未转归组.收集两组患者的临床资料(年龄、体重指数、性别、吸烟史、贫血、累及肺野数、肺部空洞病变、利福平耐药).治疗前行X-pert、NOD2、ATG16L1检测,比较两组X-pert阳性率及NOD2、ATG16L1表达水平.采用多因素Logistics回归分析分析活动性肺结核患者疾病转归的影响因素,采用受试者操作特征(ROC)曲线分析X-pert、NOD2、ATG16L1对活动性肺结核患者疾病转归的预测价值.结果 两组体重指数、吸烟史、贫血、累及肺野数、利福平耐药比较,差异均无统计学意义(P＞0.05);与转归组相比,未转归组患者年龄较大[(56.15±19.34)vs.(63.18±12.84)岁],男性(71.67vs.90.00)％、肺部空洞病变(11.67 vs.32.00)％比例较高,差异均有统计学意义(P＜0.05).与转归组相比,未转归组患者 X-pert 阳性率(75.00 vs.90.00)％、NOD2[(164.31±15.55)vs.(199.29±24.63)ng/L]、ATG16L1[(8.95±1.1.74)vs.(12.15±2.26)ng/L]表达水平均较高,差异均有统计学意义(P＜0.05).多因素Logistics回归分析结果显示,年龄、性别、肺部空洞病变、X-pert、NOD2、ATG16L1为活动性肺结核患者疾病转归的危险因素(P＜0.05).与X-pert、NOD2、ATG16Ll单项诊断相比,X-pert、NOD2、ATG16L1联合检测对活动性肺结核患者疾病转归的预测价值较高(P＜0.05).结论 疾病未转归活动性肺结核患者X-pert阳性率、NOD2、ATG16L1表达水平均高于转归患者,X-pert、NOD2、ATG16L1为活动性肺结核患者疾病转归的危险因素,X-pert联合NOD2、ATG16L1对活动性肺结核患者疾病转归的预测价值较高,为活动性肺结核患者疾病转归评估提供了有效依据.
Abstract
Objective To analyze the application value of X-pert combined with nucleotide-binding oligomerization domain protein 2(NOD2)and autophagy associated protein 16-like protein 1(ATG16L1)in disease outcome assessment of patients with active pulmonary tuber-culosis.Methods One hundred and ten patients with active pulmonary tuberculosis admitted to Chizhou People's Hospital from April 2023 to A-pril 2024 were prospective selected as the study object.All patients were treated with anti-tuberculosis therapy,disease outcome assessment,60 patients with outcome were selected as outcome group,and 50 patients without outcome were selected as non-outcome group.The clinical data of the two groups of patients(age,body mass index,gender,smoking history,anemia,number of lung fields involved,lung cavity lesions,rifampi-cin resistance)were collected.X-pert,NOD2 and ATG16 L1 were detected before treatment.The positive rate of X-pert and the expression levels of NOD2 and ATG16 L1 were compared between the two groups.The influencing factors of disease outcome in patients with active pulmonary tuberculosis were analyzed by multivariate Logistics regression analysis.The predictive value of X-pert,NOD2 and ATG16L1 for disease outcome in patients with active pulmonary tuberculosis was analyzed by receiver operating characteristic(ROC)curve.Results There were no statistically significant differences in body mass index,smoking history,anemia,number of lung fields involved and rifampicin resistance between the two groups(P＞0.05).Compared with the outcome group,the age of patients in the non-outcome group was older[(56.15±19.34)years vs.(63.18±12.84)years],and the proportion of males(71.67％vs.90.00％)and pulmonary cavity lesions(11.67％vs.32.00％)were higher,the differences were statistically significant(P＜0.05).Compared with the outcome group,the positive rate of X-pert(75.00％vs.90.00)％,NOD2[(164.31±15.55)ng/Lvs.(199.29±24.63)ng/L]and ATG16L1[(8.95±1.74)ng/Lvs.(12.15±2.26)ng/L]were high-er in non-outcome group,the differences were statistically significant(P＜0.05).Multivariate Logistics regression analysis showed that age,gender,pulmonary cavity lesions,X-pert,NOD2,and ATG16L1 were risk factors for disease outcome in patients with active pulmonary tubercu-losis(P＜0.05).Compared with the single diagnosis of X-pert,NOD2 and ATG16L1,the combined detection of X-pert,NOD2 and ATG16L1 had higher predictive value for disease outcome in patients with active pulmonary tuberculosis(P＜0.05).Conclusion The positive rate of X-pert,NOD2 and ATG16L1 in patients with non-metastatic active pulmonary tuberculosis were higher than those in patients with meta-static pulmonary tuberculosis.Age,pulmonary cavity lesions,X-pert,NOD2 and ATG16L1 were the main influencing factors for disease out-come in patients with active pulmonary tuberculosis.X-pert combined with NOD2 and ATG16L1 had high predictive value for disease outcome in patients with active pulmonary tuberculosis.It provides an effective basis for the evaluation of disease outcome in patients with active pulmonary tu-berculosis.
基金项目
2022年度安徽教育厅科研项目(JXYY2022133)
NETL
NSTL
万方数据