确定怀疑患有结核病的艾滋病毒阳性患者的临床决策阈值。

Evidence-Based Medicine Pub Date : 2017-08-01 Epub Date: 2017-07-17 DOI:10.1136/ebmed-2017-110718
Tom Boyles, Isabella Locatelli, Nicolas Senn, Mark Ebell
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引用次数: 10

摘要

临床决策阈值可能有助于诊断测试的评估,但很少确定结核病(TB)。我们向临床医生展示了六个基于网络的临床场景,描述了不同地点的艾滋病毒和可能的结核病患者,并具有一系列临床稳定性。发病概率随机变化,临床医生被要求做出治疗决定;计算阈值曲线和治疗阈值。采用贝叶斯定理和Xpert MTB/RIF的诊断准确率计算检验和治疗阈值。我们收到了165份调查回复。治疗阈值取决于临床稳定性和疑似疾病的部位。对于住院患者,不稳定患者的比例为3.4%,稳定患者的比例为79.6%。对于结核性脑膜炎,不稳定患者的发病率从0%到稳定患者的51.4%不等,对于肺结核门诊患者,不稳定患者的发病率从29.1%到稳定患者的74.5%不等。检测阈值和治疗阈值也以类似的方式变化,检测阈值从不稳定的疑似脑膜炎患者的0到稳定的住院患者的8.2%不等。治疗阈值从不稳定的疑似脑膜炎患者的0到稳定的住院患者的97%不等。结核病的治疗阈值可通过向临床医生提供具有随机疾病概率的患者情景来确定,并可使用贝叶斯定理计算检验和治疗阈值。当患者在临床上更不稳定时,当不适当地停止治疗的影响更严重时,阈值较低。这些结果可用于改进诊断试验的使用和评价。
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Determining clinical decision thresholds for HIV-positive patients suspected of having tuberculosis.

Clinical decision thresholds may aid the evaluation of diagnostic tests but have rarely been determined for tuberculosis (TB). We presented clinicians with six web-based clinical scenarios, describing patients with HIV and possible TB at various sites and with a range of clinical stability. The probability of disease was varied randomly and clinicians asked to make treatment decisions; threshold curves and therapeutic thresholds were calculated. Test and treatment thresholds were calculated using Bayes theorem and the diagnostic accuracy of Xpert MTB/RIF. We received 165 replies to our survey. Therapeutic thresholds vary depending on the clinical stability and site of suspected disease. For inpatients, it ranges from 3.4% in unstable to 79.6% in stable patients. For TB meningitis, it ranges from 0% in unstable to 51.4% in stable patients and for pulmonary TB in outpatients it ranges from 29.1% in unstable to 74.5% in the stable patients. Test and treatment thresholds vary in a similar way with test thresholds ranging from 0 in unstable patients with suspected meningitis to 8.2% for stable inpatients. Treatment thresholds vary from 0 for unstable patients with suspected meningitis to 97% for stable inpatients. Therapeutic thresholds for TB can be determined by presenting clinicians with patient scenarios with random probabilities of disease and can be used to calculate test and treatment thresholds using Bayes theorem. Thresholds are lower when patients are more clinically unstable and when the implications of inappropriately withholding therapy are more serious. These results can be used to improve use and evaluation of diagnostic tests.

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