INSERM, IAME, UMR 1137, Paris, France; AP-HP, Hôpital Bichat-Claude Bernard, Service de Maladies Infectieuses et Tropicales, Paris, France.
INSERM, IAME, UMR 1137, Paris, France.
Clin Microbiol Infect. 2020 Mar;26(3):382.e1-382.e7. doi: 10.1016/j.cmi.2019.06.026. Epub 2019 Jul 5.
The aim was to create and validate a community-acquired pneumonia (CAP) diagnostic algorithm to facilitate diagnosis and guide chest computed tomography (CT) scan indication in patients with CAP suspicion in Emergency Departments (ED).
We performed an analysis of CAP suspected patients enrolled in the ESCAPED study who had undergone chest CT scan and detection of respiratory pathogens through nasopharyngeal PCRs. An adjudication committee assigned the final CAP probability (reference standard). Variables associated with confirmed CAP were used to create weighted CAP diagnostic scores. We estimated the score values for which CT scans helped correctly identify CAP, therefore creating a CAP diagnosis algorithm. Algorithms were externally validated in an independent cohort of 200 patients consecutively admitted in a Swiss hospital for CAP suspicion.
Among the 319 patients included, 51% (163/319) were classified as confirmed CAP and 49% (156/319) as excluded CAP. Cough (weight = 1), chest pain (1), fever (1), positive PCR (except for rhinovirus) (1), C-reactive protein ≥50 mg/L (2) and chest X-ray parenchymal infiltrate (2) were associated with CAP. Patients with a score below 3 had a low probability of CAP (17%, 14/84), whereas those above 5 had a high probability (88%, 51/58). The algorithm (score calculation + CT scan in patients with score between 3 and 5) showed sensitivity 73% (95% CI 66-80), specificity 89% (95% CI 83-94), positive predictive value (PPV) 88% (95% CI 81-93), negative predictive value (NPV) 76% (95% CI 69-82) and area under the curve (AUC) 0.81 (95% CI 0.77-0.85). The algorithm displayed similar performance in the validation cohort (sensitivity 88% (95% CI 81-92), specificity 72% (95% CI 60-81), PPV 86% (95% CI 79-91), NPV 75% (95% CI 63-84) and AUC 0.80 (95% CI 0.73-0.87).
Our CAP diagnostic algorithm may help reduce CAP misdiagnosis and optimize the use of chest CT scan.
旨在创建和验证社区获得性肺炎（CAP）的诊断算法，以方便疑似 CAP 患者的诊断，并指导其进行胸部计算机断层扫描（CT）检查。
我们对 ESCAPED 研究中接受过胸部 CT 扫描并通过鼻咽 PCR 检测呼吸道病原体的疑似 CAP 患者进行了分析。一个裁决委员会确定了最终的 CAP 可能性（参考标准）。与确诊 CAP 相关的变量被用来创建加权 CAP 诊断评分。我们估计了 CT 扫描有助于正确识别 CAP 的评分值，从而创建了 CAP 诊断算法。该算法在瑞士一家医院连续收治的 200 名 CAP 疑似患者的独立队列中进行了外部验证。
在 319 名患者中，51%（163/319）被归类为确诊 CAP，49%（156/319）被排除为 CAP。咳嗽（权重=1）、胸痛（1）、发热（1）、PCR 阳性（除鼻病毒外）（1）、C 反应蛋白≥50mg/L（2）和胸部 X 线片实质浸润（2）与 CAP 相关。评分低于 3 分的患者 CAP 可能性较低（17%，14/84），而评分高于 5 分的患者 CAP 可能性较高（88%，51/58）。该算法（评分计算+CT 扫描，评分在 3 至 5 分之间的患者）显示出 73%的敏感性（95%置信区间 66-80）、89%的特异性（95%置信区间 83-94）、88%的阳性预测值（95%置信区间 81-93）、76%的阴性预测值（95%置信区间 69-82）和 0.81 的曲线下面积（95%置信区间 0.77-0.85）。该算法在验证队列中表现相似（敏感性 88%（95%置信区间 81-92）、特异性 72%（95%置信区间 60-81）、阳性预测值 86%（95%置信区间 79-91）、阴性预测值 75%（95%置信区间 63-84）和 0.80 的曲线下面积（95%置信区间 0.73-0.87）。
我们的 CAP 诊断算法可以帮助减少 CAP 的误诊，并优化胸部 CT 扫描的使用。
