A clinical score for identifying active tuberculosis while awaiting microbiological results: Development and validation of a multivariable prediction model in sub-Saharan Africa.
A clinical score for identifying active tuberculosis while awaiting microbiological results: Development and validation of a multivariable prediction model in sub-Saharan Africa.
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在等待微生物学结果时识别活动性结核病的临床评分：撒哈拉以南非洲多变量预测模型的开发和验证
DOI：
10.1371/journal.pmed.1003420
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发表时间：
2020-11
期刊：
影响因子：
15.8
通讯作者：
Dowdy DW
中科院分区：
文献类型：
--
作者：
Baik Y;Rickman HM;Hanrahan CF;Mmolawa L;Kitonsa PJ;Sewelana T;Nalutaaya A;Kendall EA;Lebina L;Martinson N;Katamba A;Dowdy DW
在资源高度有限的情况下，许多诊所缺乏活动性结核病(TB)的当天微生物检测。在这些情况下，治疗前失去随访的风险很高，一个简单、易于使用的临床风险评分可能是有用的。我们分析了南非农村28家初级卫生诊所(2016年7月至2018年1月)接受Xpert MTB/RIF结核病检测的成年人的数据。我们使用最小绝对收缩和选择算子回归来确定与Xpert确认的结核病相关的特征，并将系数转换为简单的分数。我们使用受试者工作特征(ROC)曲线评估辨别力，使用COX线性Logistic回归进行校正，并使用决策曲线评估临床实用性。我们在乌干达城市4家初级卫生诊所(2018年5月至2019年12月)对接受结核病测试的成年人进行了外部验证。在乌干达人群中重复模型开发，以比较临床评分。南非和乌干达的队列分别包括701名和106名结核病检测呈阳性的人，以及686名和281名随机选择的检测为阴性的人。与乌干达队列相比，南非队列年龄更大(41%比19%，年龄在45岁或以上)，生物学性别分类相似(48%比50%女性)，艾滋病毒流行率更高(45%比34%)。最终的预测模型得分从0到10，包括6个特征：年龄、性别、艾滋病毒(2分)、糖尿病、典型结核病症状的数量(咳嗽、发烧、体重减轻和盗汗，各1分)，以及14天症状持续时间。在派生(c-统计量=0.82，95%CI=0.81至0.82)和验证性(c-统计量=0.75，95%CI=0.69至0.80)总体上具有中等区分度。一个测试前有10%结核病概率的患者，测试后概率为4%，评分为3/10，而测试后概率为43%，评分为7/10。新手乌干达模型包含类似的特征，表现同样出色。我们的研究可能会受到光谱偏差的影响，因为我们只包括了每个队列中没有结核病的随机样本。这一评分仅用于指导管理层等待微生物结果，而不是作为基于社区的分诊测试(即确定应接受进一步检测的个人)。在这项研究中，我们观察到，在提交痰进行诊断的患者中，简单的临床风险评分可以合理地区分患有和不患有结核病的患者。根据前瞻性验证，这一评分在诊断资源有限的情况下可能有用，因为在这些环境中，对后续治疗损失的担忧很高。Hannah Rickman和他的同事们得出了一个预测结核病感染的评分，以指导在等待微生物检测结果的同时开始经验性治疗。在高负担环境中，高达40%的确诊结核病(TB)患者失去了从他们最初出现诊断到开始治疗之间的随访时间。因此，启动经验性治疗的简单临床预测规则可能非常有帮助，但现有的结核病预测规则是为在特定环境中使用而设计的(例如，艾滋病毒诊所和接触者调查)，需要在高度资源有限的环境中不太可能获得的数据和/或基础设施(例如，射线照相)，或者已经以对于忙碌的中级临床医生来说难以实施的格式构建，而这些中级临床医生几乎没有时间或访问计算机。我们开发了一个简单的临床风险评分，用于在南非农村初级卫生诊所就诊的成年人中诊断结核病，并在乌干达城市验证了该评分。这一分数(从1到10)很容易手工计算，只需要在资源高度有限的情况下临床医生随时可以获得的信息。这一评分增加了临界点的临床效用，这可能反映了在无法进行当天微生物检测时做出结核病诊断的风险-收益比。如果在其他人群中进行前瞻性验证，这一临床风险评分可能会改善外围卫生环境中经验性结核病诊断的过程，因为在这些环境中，缺乏获得当天微生物检测的机会，失去随访的风险很高。这一临床风险评分可用于高危患者在就诊当天开始4种药物治疗的短期经验性疗程，直到他们的微生物学测试结果出来。
In highly resource-limited settings, many clinics lack same-day microbiological testing for active tuberculosis (TB). In these contexts, risk of pretreatment loss to follow-up is high, and a simple, easy-to-use clinical risk score could be useful. We analyzed data from adults tested for TB with Xpert MTB/RIF across 28 primary health clinics in rural South Africa (between July 2016 and January 2018). We used least absolute shrinkage and selection operator regression to identify characteristics associated with Xpert-confirmed TB and converted coefficients into a simple score. We assessed discrimination using receiver operating characteristic (ROC) curves, calibration using Cox linear logistic regression, and clinical utility using decision curves. We validated the score externally in a population of adults tested for TB across 4 primary health clinics in urban Uganda (between May 2018 and December 2019). Model development was repeated de novo with the Ugandan population to compare clinical scores. The South African and Ugandan cohorts included 701 and 106 individuals who tested positive for TB, respectively, and 686 and 281 randomly selected individuals who tested negative. Compared to the Ugandan cohort, the South African cohort was older (41% versus 19% aged 45 years or older), had similar breakdown of biological sex (48% versus 50% female), and had higher HIV prevalence (45% versus 34%). The final prediction model, scored from 0 to 10, included 6 characteristics: age, sex, HIV (2 points), diabetes, number of classical TB symptoms (cough, fever, weight loss, and night sweats; 1 point each), and >14-day symptom duration. Discrimination was moderate in the derivation (c-statistic = 0.82, 95% CI = 0.81 to 0.82) and validation (c-statistic = 0.75, 95% CI = 0.69 to 0.80) populations. A patient with 10% pretest probability of TB would have a posttest probability of 4% with a score of 3/10 versus 43% with a score of 7/10. The de novo Ugandan model contained similar characteristics and performed equally well. Our study may be subject to spectrum bias as we only included a random sample of people without TB from each cohort. This score is only meant to guide management while awaiting microbiological results, not intended as a community-based triage test (i.e., to identify individuals who should receive further testing). In this study, we observed that a simple clinical risk score reasonably distinguished individuals with and without TB among those submitting sputum for diagnosis. Subject to prospective validation, this score might be useful in settings with constrained diagnostic resources where concern for pretreatment loss to follow-up is high. Hannah Rickman and colleagues derive a score for predicting tuberculosis infection to guide initiation of empiric treatment while waiting for microbiological testing results. In high-burden settings, up to 40% of patients with confirmed tuberculosis (TB) are lost to follow-up between the time they initially present for diagnosis and the time treatment can be initiated. A simple clinical prediction rule for initiation of empiric treatment could therefore be very helpful, but existing prediction rules for TB have been designed for use in specific contexts (e.g., HIV clinics and contact investigation), require data and/or infrastructure (e.g., radiography) that are unlikely to be available in highly resource-limited settings, or have been constructed in a format that is difficult to implement for busy midlevel clinicians with little time or access to computers. We developed a simple clinical risk score for diagnosis of TB among adults presenting to primary health clinics in rural South Africa and validated the score in urban Uganda. This score (ranging from 1 to 10) is easy to calculate by hand and requires only information readily accessible to clinicians in highly resource-limited settings. This score adds clinical utility at cutoffs that likely reflect the risk–benefit ratio of making a TB diagnosis when same-day microbiological testing is unavailable. If prospectively validated in other populations, this clinical risk score could improve the process of empiric TB diagnosis in peripheral health settings where access to same-day microbiological testing is lacking and the risk of loss to follow-up is high. This clinical risk score might be used for patients at high risk to initiate a short-term empirical course of 4-drug TB treatment on the same day of their clinic visit until their microbiological test results are available.
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影响因子：
15.8
作者：
Hanrahan, Colleen F.;Nonyane, Bareng A. S.;Dowdy, David W.
通讯作者：
Dowdy, David W.
影响因子：
3.9
作者：
Pedersen AB;Mikkelsen EM;Cronin-Fenton D;Kristensen NR;Pham TM;Pedersen L;Petersen I
通讯作者：
Petersen I
影响因子：
3.7
作者：
Nakiyingi, Lydia;Bwanika, John Mark;Manabe, Yukari C.
通讯作者：
Manabe, Yukari C.
DOI：
10.1093/cid/ciz1221
发表时间：
2020-11-05
期刊：
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
影响因子：
--
作者：
Li R;Nordio F;Huang CC;Contreras C;Calderon R;Yataco R;Galea JT;Zhang Z;Becerra MC;Lecca L;Murray MB
通讯作者：
Murray MB
影响因子：
3.6
作者：
Basinga, Paulin;Moreira, Juan;Van den Ende, Jef
通讯作者：
Van den Ende, Jef