结核与肺部疾病杂志 ›› 2024, Vol. 5 ›› Issue (6): 552-559.doi: 10.19983/j.issn.2096-8493.2024104

• 论著 • 上一篇    下一篇

新疆生产建设兵团2016—2022年肺结核患者不良转归列线图预测模型的构建与验证

马晓玲, 赵永年, 段丽丽, 刘新文()   

  1. 新疆生产建设兵团疾病预防控制中心,乌鲁木齐 830002
  • 收稿日期:2024-06-21 出版日期:2024-12-20 发布日期:2024-12-11
  • 通信作者: 刘新文 E-mail:lxwsss@sina.com
  • 基金资助:
    新疆生产建设兵团重点人群肺结核主动筛查对结核病疫情影响因素的研究(BTCDKY202202)

Construction and validation of a nomogram model for predicting adverse outcomes of pulmonary tuberculosis patients in 2016—2022 in Xinjiang Production and Construction Corps

Ma Xiaoling, Zhao Yongnian, Duan Lili, Liu Xinwen()   

  1. Xinjiang Production and Construction Corps Center for Disease Control and Prevention,Urumqi 830002,China
  • Received:2024-06-21 Online:2024-12-20 Published:2024-12-11
  • Contact: Liu Xinwen E-mail:lxwsss@sina.com
  • Supported by:
    Study on the Influencing Factors of Tuberculosis Outbreaks through Active Screening of Key Populations for Tuberculosis in the Xinjiang Production and Construction Corps(BTCDKY202202)

摘要:

目的: 构建新疆生产建设兵团(以下简称“兵团”)2016—2022年肺结核患者不良转归的列线图预测模型,评价模型的预测效果和应用价值。方法: 回顾性分析兵团2016—2022年肺结核患者的治疗转归情况。通过单因素log-rank检验及多因素Cox回归分析筛选变量并构建肺结核患者不良转归的预测模型,采用列线图对预测模型进行展示。采用区分度、校准度以及临床效益性评价模型的预测能力,采用Boostrap(B=1000)自抽样验证法对模型进行内部验证。结果: 2016—2022年兵团肺结核患者不良转归率为5.07%(405/7993)。多因素Cox回归分析结果显示,少数民族(HR=1.382,95%CI:1.106~1.725),年龄在30~60岁(HR=1.535,95%CI:1.097~2.148)、>60岁(HR=2.895,95%CI:2.088~4.013),合并糖尿病患者(HR=1.753,95%CI:1.255~2.450),复治(HR=1.846,95%CI:1.400~2.434),现地址类型为本省其他地区(HR=1.430,95%CI:1.129~1.810)、外省(HR=1.596,95%CI:1.186~2.147),无基层管理(HR=1.385,95%CI:1.132~1.694),现管理地区为南疆(HR=1.276,95%CI:1.017~1.600)是影响肺结核患者治疗转归的危险因素。据此构建兵团肺结核患者发生不良转归的列线图预测模型,模型的受试者工作特征曲线下面积为0.697(95%CI:0.633~0.761),校正曲线与理想曲线总体走势较为一致。结论: 本研究构建的列线图预测模型具有良好的预测价值,能够帮助临床决策者快速识别患者中高危人群并且进行个性化管理,及时规避风险从而提高患者的治疗成功率。

关键词: 结核,肺, 治疗失败, 因素分析, 统计学, 列线图

Abstract:

Objective: To construct a nomogram model for predicting adverse outcomes of tuberculosis patients in Xinjiang Production and Construction Corps (hereafter referred as “the Corps”) from 2016 to 2022, and to evaluate the predictive effectiveness and application value of the model. Methods: A retrospective analysis of treatment outcomes of TB patients in the Corps from 2016 to 2022 was conducted. Variables were selected and a prediction model for adverse outcomes in TB patients was constructed through univariable log-rank tests and multivariable Cox regression analysis, with the model presented with a nomogram. The model’s predictive ability was assessed with discrimination, calibration, and clinical utility. Internal validation was performed using the Bootstrap method (B=1000). Results: The average adverse outcome rate of tuberculosis patients treated in the Corps in 2016—2022 was 5.07% (405/7993). Multivariable Cox regression analysis identified several risk factors: ethnic minorities (HR=1.382, 95%CI: 1.106-1.725), 30-60 years old (HR=1.535, 95%CI: 1.097-2.148), >60 years (HR=2.895, 95%CI: 2.088-4.013), comorbid diabetes (HR=1.753, 95%CI: 1.255-2.450), retreatment (HR=1.846, 95%CI: 1.400-2.434), current address being other regions of the province (HR=1.430, 95%CI: 1.129-1.810) or outside of the province (HR=1.596, 95%CI: 1.186-2.147), lack of primary care center management (HR=1.385, 95%CI: 1.132-1.694), and treatment management being performed in southern Xinjiang (HR=1.276, 95%CI: 1.017-1.600). Based on these factors, a nomogram prediction model for adverse outcomes in TB patients was constructed. The area under the receiver operating characteristic curve of the model was 0.697 (95%CI: 0.633-0.761). Conclusion: The nomogram prediction model developed in this study shows good predictive value, could assist clinical decision-makers quickly identifying high-risk patients for personalized management, thereby mitigating risks and improving treatment success rate.

Key words: Tuberculosis, pulmonary, Treatment failure, Factor analysis, statistical, Nomogram

中图分类号: