Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
Clin Infect Dis. 2022 Mar 23;74(6):973-982. doi: 10.1093/cid/ciab598.
Despite widespread availability of curative therapy, tuberculosis (TB) treatment outcomes remain suboptimal. Clinical prediction models can inform treatment strategies to improve outcomes. Using baseline clinical data, we developed a prediction model for unsuccessful TB treatment outcome and evaluated the incremental value of human immunodeficiency virus (HIV)-related severity and isoniazid acetylator status.
Data originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly diagnosed TB patients in Brazil from 2015 through 2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary TB who started first-line anti-TB therapy and had ≥12 months of follow-up. The end point was unsuccessful TB treatment: composite of death, treatment failure, regimen switch, incomplete treatment, or not evaluated. Missing predictors were imputed. Predictors were chosen via bootstrapped backward selection. Discrimination and calibration were evaluated with c-statistics and calibration plots, respectively. Bootstrap internal validation estimated overfitting, and a shrinkage factor was applied to improve out-of-sample prediction. Incremental value was evaluated with likelihood ratio-based measures.
Of 944 participants, 191 (20%) had unsuccessful treatment outcomes. The final model included 7 baseline predictors: hemoglobin, HIV infection, drug use, diabetes, age, education, and tobacco use. The model demonstrated good discrimination (c-statistic = 0.77; 95% confidence interval, .73-.80) and was well calibrated (optimism-corrected intercept and slope, -0.12 and 0.89, respectively). HIV-related factors and isoniazid acetylation status did not improve prediction of the final model.
Using information readily available at treatment initiation, the prediction model performed well in this population. The findings may guide future work to allocate resources or inform targeted interventions for high-risk patients.
尽管有广泛的治愈性治疗方法，但结核病（TB）的治疗效果仍然不理想。临床预测模型可以为治疗策略提供信息，以改善治疗结果。本研究使用基线临床数据，开发了一种用于预测治疗失败的 TB 治疗结果的预测模型，并评估了人类免疫缺陷病毒（HIV）相关严重程度和异烟肼乙酰化状态的增量价值。
数据来源于巴西区域前瞻性观察性结核病研究（Regional Prospective Observational Research for Tuberculosis Brazil，RePORT-Brazil）队列，该队列纳入了 2015 年至 2019 年期间巴西新诊断的结核病患者。本分析纳入了培养阳性、药物敏感的肺结核患者，这些患者接受了一线抗结核治疗，并进行了≥12 个月的随访。终点是治疗失败：死亡、治疗失败、方案改变、治疗不完整或未评估的复合终点。缺失的预测因子通过 bootstrap 后向选择进行选择。通过 bootstrapping 内部验证来评估过拟合，应用收缩因子来改善样本外预测。
在 944 名参与者中，有 191 名（20%）出现治疗失败的结果。最终模型包括 7 个基线预测因子：血红蛋白、HIV 感染、药物使用、糖尿病、年龄、教育和吸烟。该模型具有良好的区分能力（c 统计量为 0.77；95%置信区间，0.73-0.80），校准良好（校正后的截距和斜率分别为-0.12 和 0.89）。HIV 相关因素和异烟肼乙酰化状态并不能改善最终模型的预测。
本研究使用治疗开始时可获得的信息，该预测模型在该人群中表现良好。这些发现可能有助于未来的工作，以分配资源或为高风险患者提供有针对性的干预措施。
