Community-acquired pneumonia in the emergency department: an algorithm to facilitate diagnosis and guide chest CT scan indication. (March 2020)

• Record Type: Journal Article
• Authors: Loubet, P.; Tubiana, S.; Claessens, Y.E.; Epelboin, L.; Ficko, C.; Le Bel, J.; Rammaert, B.; Garin, N.; Prendki, V.; Stirnemann, J.; Leport, C.; Yazdanpanah, Y.; Varon, E.; Duval, X.
• Is Part Of: Clinical microbiology and infection. Volume 26:Number 3(2020)
• Page Start: 382.e1
• Page End: 382.e7
• Publication Date: 2020-03
• Subjects: Chest CT scan -- Community-acquired pneumonia -- Diagnosis -- Diagnostic methods -- Emergency department -- Polymerase chain reaction -- Respiratory viruses; Medical microbiology -- Periodicals; Diagnostic microbiology -- Periodicals; Communicable diseases -- Periodicals; Infection -- Periodicals; 616.01
• DOI: 10.1016/j.cmi.2019.06.026
• Languages: English
• ISSNs: 1198-743X

Abstract

Abstract: 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.