初始胸部CT特征对非耐药肺结核患者抗结核疗效的预测价值OA
Predictive value of baseline chest CT features for anti-tuberculosis treatment outcomes in patients with non-drug-resistant pulmonary tuberculosis
目的:评估治疗初期胸部CT特征预测非耐药肺结核患者不良治疗结局的效能.方法:采用回顾性、病例-对照研究设计,纳入2017年7月至2024年4月期间天津大学海河医院结核病区收治的非耐药肺结核患者.根据治疗转归纳入预后不良组(118例)并匹配预后良好组(183例).通过单因素分析及多因素logistic回归分析,系统比较两组患者初始胸部CT特征及临床指标差异,并构建疗效预测模型.采用受试者工作特征曲线(ROC)评估模型的预测效能.结果:多因素logistic回归分析显示,复治肺结核(OR=6.257,95％CI:2.977～13.153,P＜0.001)、初始 CT 显示空洞(OR=1.994,95％CI:1.116～3.564,P=0.020)、支气管扩张(OR=3.610,95％CI:1.395～9.341,P=0.008)、纤维索条影(OR=1.889,95％CI:1.073～3.328,P=0.028)、受累肺叶数＞3 个(OR=2.293,95％CI:1.241～4.237,P=0.008)以及血肌酐降低(OR=3.074,95％CI:1.539～6.137,P=0.001)是治疗预后不良的独立危险因素.最终建立的预测模型公式为:logit(p)=-2.327+1.834×复治肺结核+0.690 ×空洞+1.284×支气管扩张+0.636×纤维索条影+0.830×受累肺叶数＞3+1.123×血肌酐降低,模型AUC达0.808(95％CI:0.759～0.857).结论:基于治疗初始胸部CT影像特征联合临床指标构建的预测模型,对非耐药肺结核患者的治疗转归具有良好的预测价值.
Objective:To investigate the predictive value of baseline chest CT imaging features for predicting treatment outcomes in patients with non-drug-resistant pulmonary tuberculosis during the treatment phase.Methods:A retrospective case-control study design was employed.Patients with non-drug-resistant pulmonary tuberculosis admitted at Tianjin University Haihe Hospital between July 2017 and April 2024 were enrolled.Patients were categorized into a poor prognosis group(n=118)based on treatment outcomes and matched with a good prognosis group(n=183).Differences in baseline chest CT features and clinical indicators between the two groups were systematically compared using univariate analysis and logistic regression analysis.An efficacy prediction model was subsequently constructed.The predictive performance of the model was evaluated using the receiver operating characteristic(ROC)curve.Results:Multivariate logistic regression analysis identified the following as independent risk factors for poor treatment prognosis:retreated tuberculosis(OR=6.257,95％CI:2.977-13.153,P＜0.001),baseline CT showing cavity(OR=1.994,95％CI:1.116-3.564,P=0.020),bronchiectasis(OR=3.610,95％CI:1.395-9.341,P=0.008),fibrous stripe(OR=1.889,95％CI:1.073-3.328,P=0.028),involvement of more than three lung lobes(OR=2.293,95％CI:1.241-4.237,P=0.008),and decreased serum creatinine(OR=3.074,95％CI:1.539-6.137,P=0.001).The final prediction model formula was established as:logit(p)=-2.327+1.834 ×(Retreated tuberculosis)+0.690 ×(Cavity)+1.284 ×(Bronchiectasis)+0.636 ×(Fibrous stripe)+0.830 ×(Involved lung lobes＞3)+1.123 ×(Decreased serum creatinine).The model achieved an area under the curve(AUC)of 0.808(95％CI:0.759-0.857).Conclusion:The prediction model constructed based on baseline chest CT imaging features combined with clinical indicators showed good predictive value for the treatment outcomes of patients with non-drug-resistant pulmonary tuberculosis.
赵小婷;韩晶;侯志丽;丁文龙;张宇洋;张硕;邢志珩
天津大学海河医院/天津市海河医院放射科/天津市呼吸疾病研究所/国家中医药管理局中医药防治传染病重点研究室,天津 300350天津大学海河医院/天津市海河医院医务科/天津市呼吸疾病研究所/国家中医药管理局中医药防治传染病重点研究室,天津 300350天津大学海河医院/天津市海河医院结核科/天津市呼吸疾病研究所/国家中医药管理局中医药防治传染病重点研究室,天津 300350天津大学海河医院/天津市海河医院放射科/天津市呼吸疾病研究所/国家中医药管理局中医药防治传染病重点研究室,天津 300350天津医科大学海河临床学院,天津 300350天津大学海河医院/天津市海河医院放射科/天津市呼吸疾病研究所/国家中医药管理局中医药防治传染病重点研究室,天津 300350天津大学海河医院/天津市海河医院放射科/天津市呼吸疾病研究所/国家中医药管理局中医药防治传染病重点研究室,天津 300350
医药卫生
结核,肺预后因素分析,统计学危险因素体层摄影术,X线计算机
Tuberculosis,pulmonaryPrognosisFactor analysis,statisticalRisk factorsTomography,X-ray computed
《中国防痨杂志》 2026 (5)
670-676,7
天津市教委科研计划项目(2024ZX009) Tianjin Municipal Education Commission Scientific Research Plan Project(2024ZX009)
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