Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada.
Thorax. 2013 Sep;68(9):860-6. doi: 10.1136/thoraxjnl-2012-203086. Epub 2013 May 14.
Interferon γ release assays (IGRAs) are increasingly used for tuberculosis (TB) infection, but their incremental value beyond patient demographics, clinical signs and conventional tests for active disease has not been evaluated in children.
The incremental value of T-SPOT.TB was assessed in 491 smear-negative children from two hospitals in Cape Town, South Africa. Bayesian model averaging was used to select the optimal set of patient demographics and clinical signs for predicting culture-confirmed TB. The added value of T-SPOT.TB over and above patient characteristics and conventional tests was measured using statistics such as the difference in the area under the receiver operating characteristic curve (AUC), the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI).
Cough longer than 2 weeks, fever longer than 2 weeks, night sweats, malaise, history of household contact and HIV status were the most important predictors of culture-confirmed TB. Binary T-SPOT.TB results did not have incremental value when added to the baseline model with clinical predictors, chest radiography and the tuberculin skin test. The AUC difference was 3% (95% CI 0% to 7%). Using risk cut-offs of <10%, 10-30% and >30%, the NRI was 7% (95% CI -8% to 31%) but the CI included the null value. The IDI was 3% (95% CI 0% to 11%), meaning that the average predicted probability across all possible cut-offs improved marginally by 3%.
In a high-burden setting, the T-SPOT.TB did not have added value beyond clinical data and conventional tests for diagnosis of TB disease in smear-negative children.
干扰素 γ 释放试验(IGRAs)越来越多地用于结核病(TB)感染,但在儿童中,其在患者人口统计学特征、临床症状和活动性疾病的常规检测之外的增值尚未得到评估。
在南非开普敦的两家医院中,对 491 名痰涂片阴性的儿童进行了 T-SPOT.TB 的增值评估。贝叶斯模型平均法用于选择最佳的患者人口统计学特征和临床症状组合,以预测培养确诊的 TB。使用曲线下面积(AUC)差值、净重新分类改善(NRI)和综合判别改善(IDI)等统计数据,衡量 T-SPOT.TB 超过患者特征和常规检测的附加价值。
咳嗽时间超过 2 周、发热时间超过 2 周、夜间盗汗、不适、有家庭接触史和 HIV 状态是培养确诊 TB 的最重要预测因素。当将 T-SPOT.TB 的二元结果添加到具有临床预测因素、胸部 X 线和结核菌素皮肤试验的基线模型中时,其并没有增加价值。AUC 差值为 3%(95%CI 0%至 7%)。使用<10%、10-30%和>30%的风险切点,NRI 为 7%(95%CI-8%至 31%),但 CI 包含了零值。IDI 为 3%(95%CI 0%至 11%),这意味着所有可能切点的平均预测概率略有提高 3%。
在高负担环境中,T-SPOT.TB 在痰涂片阴性儿童的 TB 疾病诊断中,除了临床数据和常规检测之外,并没有额外的价值。