Post-Doctoral Research Center, Chongqing Public Health Medical Center, Chongqing, China.
Emergency Department, Chongqing Public Health Medical Center, Chongqing, China.
BMJ Open Respir Res. 2023 Nov;10(1). doi: 10.1136/bmjresp-2023-001781.
Tracheobronchial tuberculosis (TBTB), a specific subtype of pulmonary tuberculosis (PTB), can lead to bronchial stenosis or bronchial occlusion if not identified early. However, there is currently no available means for predicting the risk of associated TBTB in PTB patients. The objective of this study was to establish a risk prediction nomogram model for estimating the associated TBTB risk in every PTB patient.
A retrospective cohort study was conducted with 2153 PTB patients. Optimised characteristics were selected using least absolute shrinkage and selection operator regression. Multivariate logistic regression was applied to build a predictive nomogram model. Discrimination, calibration and clinical usefulness of the prediction model were assessed using C-statistics, receiver operator characteristic curves, calibration plots and decision analysis. The developed model was validated both internally and externally.
Among all PTB patients who underwent bronchoscopies (n=2153), 40.36% (n=869) were diagnosed with TBTB. A nomogram model incorporating 11 predictors was developed and displayed good discrimination with a C-statistics of 0.782, a sensitivity of 0.661 and a specificity of 0.762 and good calibration with a calibration-in-the-large of 0.052 and a calibration slope of 0.957. Model's discrimination was favourable in both internal (C-statistics, 0.782) and external (C-statistics, 0.806) validation. External validation showed satisfactory accuracy (sensitivity, 0.690; specificity, 0.804) in independent cohort. Decision curve analysis showed that the model was clinically useful when intervention was decided on at the exacerbation possibility threshold of 2.3%-99.2%. A clinical impact curve demonstrated that our model predicted high-risk estimates and true positives.
We developed a novel and convenient risk prediction nomogram model that enhances the risk assessment of associated TBTB in PTB patients. This nomogram can help identify high-risk PTB patients who may benefit from early bronchoscopy and aggressive treatment to prevent disease progression.
气管支气管结核（TBTB）是肺结核（PTB）的一种特殊亚型，如果不能早期发现，可能导致支气管狭窄或阻塞。然而，目前尚无预测 PTB 患者发生 TBTB 风险的方法。本研究旨在建立一种风险预测列线图模型，以评估每位 PTB 患者发生相关 TBTB 的风险。
采用回顾性队列研究，纳入 2153 例 PTB 患者。采用最小绝对收缩和选择算子回归选择优化特征。应用多变量逻辑回归构建预测列线图模型。采用 C 统计量、受试者工作特征曲线、校准图和决策分析评估预测模型的区分度、校准度和临床实用性。对开发的模型进行内部和外部验证。
在所有接受支气管镜检查的 PTB 患者中（n=2153），40.36%（n=869）诊断为 TBTB。建立了一个包含 11 个预测因子的列线图模型，该模型具有良好的区分度，C 统计量为 0.782，灵敏度为 0.661，特异度为 0.762，校准度良好，大校准值为 0.052，校准斜率为 0.957。内部验证（C 统计量为 0.782）和外部验证（C 统计量为 0.806）均显示该模型具有良好的区分度。外部验证在独立队列中显示出较好的准确性（灵敏度为 0.690，特异度为 0.804）。决策曲线分析显示，当干预可能性阈值为 2.3%-99.2%时，该模型具有临床实用性。临床影响曲线表明，我们的模型预测了高危估计值和真阳性。
我们开发了一种新颖、便捷的风险预测列线图模型，可增强对 PTB 患者相关 TBTB 的风险评估。该列线图可帮助识别高危 PTB 患者，这些患者可能受益于早期支气管镜检查和积极治疗，以预防疾病进展。
