| 崔灿灿,万荣,刘颖楠,等.肺结核病人家庭密切接触者发生结核分枝杆菌感染的风险预测分析[J].安徽医药,2026,30(6):1108-1115. |
| 肺结核病人家庭密切接触者发生结核分枝杆菌感染的风险预测分析 |
| Risk prediction analysis of Mycobacterium tuberculosis infection among close family contacts of tuberculosis patients |
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| DOI：10.3969/j.issn.1009-6469.2026.06.010 |
| 中文关键词: 结核，肺 密切接触者 结核感染 T细胞检测 结核分枝杆菌 风险预测 |
| 英文关键词: Tuberculosis, pulmonary Close contacts T-cell assay for tuberculosis infection Mycobacterium tuberculosis Risk prediction |
| 基金项目:云南省科技厅重大科技专项计划( 202402AA310011) |
| 作者 | 单位 | E-mail | | 崔灿灿 | 昆明市第三人民医院结核二科,云南昆,明650041
大理大学公共卫生学院,云南大理 671000 | | | 万荣 | 昆明市第三人民医院结核二科,云南昆,明650041 | | | 刘颖楠 | 昆明市第三人民医院结核二科,云南昆,明650041 | | | 刘蕾 | 昆明市第三人民医院结核二科,云南昆,明650041 | | | 李明武 | 昆明市第三人民医院结核二科,云南昆,明650041 | ynkmlmw@sina.com |
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| 中文摘要: |
| 目的探究肺结核病人家庭密切接触者发生结核分枝杆菌( MTB)感染的影响因素并建立预测模型。方法横断面研究。选取 2023年 10月至 2024年 7月昆明市第三人民医院诊断为肺结核的 288例病人作为指示病例，并以其 422例家庭密切接触者为研究对象，采用结核感染 T细胞检测 QuantiFERON.-TB Gold Plus(QFT-Plus)检测 MTB感染情况。收集研究对象的人口学信息、生活习惯、肺结核接触史、健康状况以及指示病例的相关信息，采用 χ2检验和二元 logistic回归模型分析 MTB感染的影响因素，最后构建受试者操作特征曲线( ROC曲线)评估各变量的单独及联合预测价值。结果 422例家庭密切接触者中，男性 243例( 57.58%)，女性 179例( 42.42%)； QFT-Plus检测阳性者 139例， MTB感染率为 32.94%(139/422)。单因素分析显示，结核感染阳性者的年龄［(42.99±16.48)岁比( 37.08±15.03)岁］和身体质量指数( BMI)［(24.86±6.35)kg/m2比( 22.72±4.67)kg/m2］均大于结核感染阴性者( P<0.05)，指示病例病原学 1~4组 MTB感染率分别为 45.38%(59/130)、 28.17%(20/71)、 33.00%(33/100)、 22.31%(27/121)(P<0.05)，职业为学生、个体经营者、工人、农民、企事业单位者、专业技术人员、自由职业者、无业人员 MTB感染率分别为 16.00%(8/50)、 38.46%(10/26)、 48.15%(13/27)、 49.23%(32/65)、 24.00%(6/25)、 19.23%(15/78)、 28.07%(16/57)、41.49%(39/94)(P<0.05)，有肺结核相关症状 MTB感染率高于无肺结核相关症状［ 51.92%(81/156)比 21.80%(58/266)，P<0.05］，与肺结核病人确诊前 3个月内开始接触 MTB感染率高于治疗后 14 d内开始接触［38.18%(126/330)比 14.13%(13/92)，P<0.05］；已婚或同居、受教育程度为初中及以下、与病人为夫妻关系、农村居住、无锻炼、有基础疾病、无防护措施、邻居同事有结核病史、与病人共同居住、共用卧室、接触时间 >8 h/d、接触面积 <60 m2通风条件差、没有阳光照射、指示病例咳痰及病原学检查结果为痰细菌学阳性的密切接触者结核感染检出率高( P<0.05)。多因、素分析显示，年龄、 BMI、病原学分组( 1组和 3组)、职业(学生和农民)、有肺结核相关症状、与肺结核病人在确诊前 3个月内开始接触为 MTB感染的危险因素。 ROC曲线结果显示，年龄、 BMI、病原学分组、结核相关症状、开始接触时间的曲线下面积( AUC)依次为 0.60、0.62、0.61、0.66、0.59，这些指标联合预测的 AUC(0.81)显著高于单个预测指标( P<0.05)。结论对于病原学阳性(尤其是痰细菌学阳性和支刷物 \灌洗液阳性)的肺结核病人家庭密切接触者，当年龄 >38.50岁、 BMI>24.06 kg/m2、有肺结核相关症状，且与病人确诊前 3个月内开始接触时， MTB感染的风险显著增加，联合预测价值较高，具有一定的预测准确性。 |
| 英文摘要: |
| Objective To investigate the influencing factors of Mycobacterium tuberculosis (MTB) infection among close family con. tacts of patients with pulmonary tuberculosis and to establish a prediction model.Methods This cross-sectional study included 288patients diagnosed with pulmonary tuberculosis at the Third People 's Hospital of Kunming from October 2023 to July 2024 as indexcases, and their 422 close family contacts were enrolled as study objects. QuantiFERON.-TB Gold Plus (QFT-Plus) was used to detectMTB infection. The demographic information, living habits, tuberculosis contact history, health status and information on the index cas.es were collected. The chi-square test and binary logistic regression model were used to analyze the influencing factors of MTB infec.tion. Receiver operating characteristic curves (ROC curves) were constructed to evaluate the individual and combined predictive valueof each variable.Results Among the 422 close family contacts, 243 (57.58%) were male and 179 (42.42%) were female. A total of 139 tested positive by QFT-Plus, yielding an MTB infection rate of 32.94% (139/422). Univariate analysis showed that those with positiveMTB infection had a higher mean [(42.99±16.48) years vs. (37.08±15.03) years] and body mass index (BMI) [(24.86±6.35) kg/m2 vs. (22.72±4.67) kg/m2] than those with negative tuberculosis MTB infection (P<0.05). The MTB infection rates by etiological group of theindex cases were 45.38% (59/130) for group 1, 28.17% (20/71) for group 2, 33.00% (33/100) for group 3, and 22.31% (27/121) for group4(P<0.05). The MTB infection rates by occupation were as follow: students 16.00% (8/50), self-employed 38.46% (10/26), workers48.15% (13/27), farmers 49.23% (32/65), employees of enterprises/institutions 24.00% (6/25), professional and technical personnel19.23% (15/78), freelancers 28.07% (16/57), and unemployed 41.49% (39/94) (P<0.05). Contacts with tuberculosis-related symptoms had a higher MTB infection rate than those without symptoms [51.92% (81/156) vs. 21.80% (58/266), P<0.05]. In addition, the MTB in.fection rate was higher among contacts who started contact within 3 months before the patient's diagnosis than among those who startedcontact within 14 days after treatment initiation [38.18% (126/330) vs. 14.13% (13/92), P<0.05]. Close contacts who were married or co.habiting, had an education level of junior high school or below, were the spouse of the index case, resided in rural areas, did not exer.cise, had underlying diseases, took no protective measures, had neighbors or colleagues with a history of tuberculosis, lived togetherwith the patient, shared a bedroom, had contact duration >8 h/d, lived in a dwelling with contact area <60 m2, had poor ventilation,lacked sunlight exposure, and whose index case had expectoration and positive sputum bacteriology showed a significantly higher detec.tion rate of tuberculosis infection (P<0.05). Multivariate analysis demonstrated that age, BMI, etiological group (groups 1 and 3), occu.pation (being a student and being a farmer), presence of tuberculosis-related symptoms, and starting contact with the tuberculosis pa.tient within 3 months before diagnosis were risk factors for MTB infection. ROC curve analysis showed that the area under the curve(AUC) values of age, BMI, etiological group, tuberculosis-related symptoms, and time of first contact were 0.60, 0.62, 0.61, 0.66, and0.59, respectively. The AUC for the combined model (0.81) was significantly higher than that of any single predictor (P<0.05).Conclu. sions For close family contacts of patients with bacteriologically positive pulmonary tuberculosis (especially those with sputum bacte. riology-positive and bronchial brushing/lavage fluid-positive results), when age >38.50 years, BMI >24.06 kg/m2, in the presence of tu. berculosis-related symptoms, and contact started within 3 months before the patient's diagnosis, the risk of MTB infection is significant.ly increased. The combined prediction model demonstrates high predictive value with certain predictive accuracy. |
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