OBJECTIVE 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). METHODS 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. RESULTS 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). CONCLUSION Our CAP diagnostic algorithm may help reduce CAP misdiagnosis and optimize the use of chest CT scan.

中文翻译：

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急诊科社区获得性肺炎：一种有助于诊断并指导胸部CT扫描指示的算法。

目的目的是创建和验证社区获得性肺炎（CAP）诊断算法，以帮助诊断和指导急诊科（ED）患有CAP的患者进行胸部计算机断层扫描（CT）扫描指示。方法我们对入选ESCAPED研究的CAP疑似患者进行了分析，这些患者接受了胸部CT扫描并通过鼻咽PCR进行了呼吸道病原体的检测。裁决委员会分配了最终的CAP概率（参考标准）。与确诊的CAP相关的变量用于创建加权CAP诊断评分。我们估算了CT扫描可帮助正确识别CAP的得分值，因此创建了CAP诊断算法。在200例因CAP怀疑而在瑞士医院连续入院的患者的独立队列中，对算法进行了外部验证。结果在包括的319例患者中，有51％（163/319）被归为确诊CAP，有49％（156/319）被归为排除的CAP。咳嗽（体重= 1），胸痛（1），发烧（1），PCR阳性（鼻病毒除外）（1），C反应蛋白≥50mg / L（2）和胸部X线实质浸润（2） ）与CAP相关联。得分低于3的患者发生CAP的可能性较低（17％，14/84），而得分高于5的患者则具有较高的CAP可能性（88％，51/58）。该算法（得分在3到5之间的患者进行评分计算+ CT扫描）显示出敏感性73％（95％CI 66-80），特异性89％（95％CI 83-94），阳性预测值（PPV）88％ （95％CI 81-93），阴性预测值（NPV）76％（95％CI 69-82），曲线下面积（AUC）0.81（95％CI 0.77-0.85）。该算法在验证队列中显示出相似的性能（灵敏度88％（95％CI 81-92），特异性72％（95％CI 60-81），PPV 86％（95％CI 79-91），NPV 75％（结论：95％CI 63-84）和AUC 0.80（95％CI 0.73-0.87）结论我们的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。