Background. Household contacts of patients with tuberculosis (TB) are at great risk of TB infection. The aim of this study was to develop a predictive model of TB transmission among household contacts. Method. This was a secondary analysis of data from a prospective cohort study, in which a total of 700 TB patients and 3417 household contacts were enrolled between 2010 and 2013 at two study sites in Peru. The incidence of secondary TB cases among household contacts of index cases was recorded. The LASSO regression method was used to reduce the data dimension and to filter variables. Multivariate logistic regression analysis was applied to develop the predictive model, and internal validation was performed. A nomogram was constructed to display the model, and the AUC was calculated. The calibration curve and decision curve analysis (DCA) were also evaluated. Results. The incidence of TB disease among the contacts of index cases was 4.4% (149/3417). Ten variables (gender, age, TB history, diabetes, HIV, index patient’s drug resistance, socioeconomic status, spoligotypes, and the index-contact share sleeping room status) filtered through the LASSO regression technique were finally included in the predictive model. The model showed good discriminatory ability, with an AUC value of 0.761 (95% CI, 0.723–0.800) for the derivation and 0.759 (95% CI, 0.717–0.796) for the internal validation. The predictive model showed good calibration, and the DCA demonstrated that the model was clinically useful. Conclusion. A predictive model was developed that incorporates characteristics of both the index patients and the contacts, which may be of great value for the individualized prediction of TB transmission among household contacts.

中文翻译：

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家庭接触者之间结核病传播预测模型的开发。

背景。结核病 (TB) 患者的家庭接触者感染结核病的风险很大。本研究的目的是建立一个家庭接触者之间结核病传播的预测模型。方法. 这是对一项前瞻性队列研究数据的二次分析，该研究在 2010 年至 2013 年期间在秘鲁的两个研究地点共招募了 700 名结核病患者和 3417 名家庭接触者。记录指示病例的家庭接触者中继发性结核病例的发生率。LASSO回归方法用于减少数据维度和过滤变量。应用多元逻辑回归分析建立预测模型，并进行内部验证。构建列线图以显示模型，并计算 AUC。还评估了校准曲线和决策曲线分析 (DCA)。结果. 指示病例接触者中结核病发病率为4.4%（149/3417）。通过 LASSO 回归技术过滤的十个变量（性别、年龄、结核病史、糖尿病、艾滋病毒、指标患者的耐药性、社会经济地位、spoligotypes 和指标接触共享卧室状态）最终被纳入预测模型。该模型显示出良好的判别能力，推导的 AUC 值为 0.761（95% CI，0.723-0.800），内部验证的 AUC 值为 0.759（95% CI，0.717-0.796）。预测模型显示出良好的校准，DCA 证明该模型在临床上是有用的。结论. 开发了一个预测模型，该模型结合了索引患者和接触者的特征，这可能对家庭接触者之间结核病传播的个体化预测具有重要价值。