肺结核与糖尿病共病患者就诊延迟风险预测模型的构建及验证

doi:10.19871/j.cnki.xfcrbzz.2026.01.011

摘要/Abstract

摘要： 目的构建肺结核与糖尿病（pulmonary tuberculosis-diabetes mellitus,PTB-DM）共病患者就诊延迟风险预测模型并验证,为临床早期识别就诊延迟高风险人群、制定针对性干预策略提供参考。方法采用便利抽样法收集2023年1月至2024年12月入住南京中医药大学附属南京医院（南京市第二医院）结核科的344例PTB-DM共病患者的临床资料,按照7:3比例将患者分为建模组和验证组;采用多因素Logistic回归模型系统性筛选并分析PTB-DM共病患者就诊延迟的独立危险因素,使用R软件绘制列线图模型并进行分组验证。结果本研究中建模组和验证组分别为241例和103例患者,就诊延迟发生率分别为59.34%和56.31%,两组患者临床资料差异无统计学意义（P>0.05）。多因素Logistic分析结果显示,就诊距离（OR=4.553,95%CI：1.765~19.347）、体检频率（OR=13.907,95%CI：1.324~27.582）、查尔森共病指数(charlson comorbidity, CCI)得分（OR=1.181,95%CI：1.059~1.320）、简易疾病感知问卷(brief illness perception questionnaire, BIPQ)得分（OR=0.729,95%CI：0.634~0.926）、社会支持评定量表(social support rating scale, SSRS)得分（OR=0.903,95%CI：0.871~0.986）、就医决策障碍感知量表(Chinese-perceived barriers to health care-seeking decision, PBHSD-C)得分（OR=1.439,95%CI：1.187~4.548）是PTB-DM共病患者就诊延迟的影响因素。其中,就诊距离、体检频率及CCI、PBHSD-C得分与就诊延迟风险呈正相关（OR>1）,BIPQ、SSRS得分与就诊延迟风险呈负相关（OR<1）。基于上述变量构建列线图预测模型,接受者操作特性（receiver operating characteristic,ROC）曲线分析显示,建模组ROC曲线下面积（area under curve,AUC）为85.3%,敏感度为84.15%,特异度为76.42%;校准曲线显示,P值为0.306,提示模型预测值与实际值一致性良好。验证组验证结果显示,该模型的敏感度为74.1%,特异度为91.1%,阳性预测值为91.5%,阴性预测值为73.2%,预测正确率为81.6%。结论本研究开发的PTB-DM共病患者就诊延迟风险预测模型通过验证证实具有良好的区分度和校准度,能够有效识别就诊延迟的高危患者,可为疾病预防控制部门及医疗机构实施针对性干预措施提供循证依据,优化资源分配,从而降低疾病传播和就诊延迟风险。

关键词: 肺结核, 糖尿病, 就诊延迟, 风险预测, 列线图, 验证

Abstract: Objective A risk prediction model for healthcare-seeking delay in patients with pulmonary tuberculosis-diabetes mellitus (PTB-DM) was developed and validated in this study, providing a reference for the clinical early identification of high-risk groups for healthcare-seeking delay and the formulation of targeted intervention strategies. Method Clinical data of 344 patients with PTB-DM admitted to the Tuberculosis Department of Nanjing Second Hospital from January 2023 to December 2024 were collected via convenience sampling. These patients were stratified into a modeling group and a validation group at a ratio of 7:3. Multivariate Logistic regression was applied to systematically screen and analyze the independent risk factors for treatment delay of patients with PTB-DM. Using R software, the nomogram model was plotted and stratified validation was performed. Result In this study, 241 patients were enrolled in the modeling group and 103 in the validation group. The incidence of treatment delay was 59.34% and 56.31% in the two groups, respectively. No statistically significant difference was found in clinical data between the two groups (P>0.05). Multivariate analysis of clinical data from patients in the modeling group demonstrated that,distance to medical facilities (OR=4.553, 95%CI:1.765-19.347), frequency of physical examinations (OR=13.907, 95%CI:1.324-27.582), charlson comorbidity (CCI) score (OR=1.181, 95%CI:1.059-1.320), brief illness perception questionnaire (BIPQ) score (OR=0.729, 95%CI:0.634-0.926), social support rating scale (SSRS) score (OR=0.903, 95%CI:0.871-0.986), and chinese-perceived barriers to health care-seeking decision (PBHSD-C) score (OR=1.439, 95%CI:1.187-4.548) are the influencing factors of treatment delay of patients with PTB-DM. A nomogram prediction model was constructed based on the aforementioned variables. ROC curve analysis showed that the AUC of the modeling group was 85.3%, with a sensitivity of 84.15% and a specificity of 76.42%; calibration curve analysis revealed a P-value of 0.306, indicating good consistency between the predicted values and actual values of the model. Validation results in the validation group showed that the model had a sensitivity of 74.1%, a specificity of 91.1%, a positive predictive value of 91.5%, a negative predictive value of 73.2%, and an overall prediction accuracy of 81.6%. Conclusion The risk prediction model for treatment delay of patients with PTB-DM developed in this study has been verified to possess good discriminative ability and calibration. It can effectively identify high-risk patients for treatment delay, provide an evidence-based foundation for disease prevention and control departments and medical institutions to implement targeted intervention measures, optimize resource allocation, and thereby reduce both the risk of disease transmission and the incidence of treatment delay.

Key words: Pulmonary tuberculosis, Diabetes mellitus, Treatment delay, Risk prediction, Nomograms, Validation

中图分类号:

R521
R587.1

刘玲, 祝成凤, 王进, 刘晓玲, 宋艳, 刘艳, 曾谊, 张丞, 林霏申. 肺结核与糖尿病共病患者就诊延迟风险预测模型的构建及验证[J]. 新发传染病电子杂志, 2026, 11(1): 67-73.

Liu Ling, Zhu Chengfeng, Wang Jin, Liu Xiaoling, Song Yan, Liu Yan, Zeng Yi, Zhang Cheng, Lin Feishen. Construction and validation of a risk predictive model for treatment delay of patients with pulmonary tuberculosis-diabetes mellitus comorbidity[J]. Electronic Journal of Emerging Infectious Diseases, 2026, 11(1): 67-73.

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