P Loubet1, S Tubiana2, Y E Claessens3, L Epelboin4, C Ficko5, J Le Bel6, B Rammaert7, N Garin8, V Prendki9, J Stirnemann8, C Leport10, Y Yazdanpanah11, E Varon12, X Duval13. 1. INSERM, IAME, UMR 1137, Paris, France; AP-HP, Hôpital Bichat-Claude Bernard, Service de Maladies Infectieuses et Tropicales, Paris, France. Electronic address: paul.loubet@aphp.fr. 2. INSERM, IAME, UMR 1137, Paris, France. 3. Service des urgences, Hôpital Princesse Grace, Monaco. 4. Unité des Maladies Infectieuses et Tropicales, Centre Hospitalier Andrée Rosemon, Cayenne, French Guiana; Ecosystèmes Amazoniens et Pathologie Tropicale (EPaT) EA3593, Université de la Guyane, Cayenne, French Guiana; Service des Maladies Infectieuses et Tropicales, Groupe Hospitalier Pitié-Salpêtrière, 47-83 bd de l'hôpital, Paris, France. 5. Service de Maladies Infectieuses et Tropicales, Hôpital Inter-armées de Bégin, Saint-Mandé, France. 6. INSERM, IAME, UMR 1137, Paris, France; Département de Médecine Générale, Université Paris Diderot, Sorbonne Paris Cité, Paris, France. 7. Service de Maladies Infectieuses et Tropicales, CHU de Poitiers, Poitiers, France; Université de Poitiers, Poitiers, France; Inserm U1070, Poitiers, France. 8. Service de Médecine Interne Générale, Hôpitaux Universitaires de Genève, Genève, Switzerland. 9. Service de Médecine Interne de l'âgé, Hôpitaux Universitaires de Genève, Genève, Switzerland. 10. INSERM, IAME, UMR 1137, Paris, France; Université Paris-Diderot, Paris, France; AP-HP, Unité de Coordination du Risque Épidémique et biologique, Paris, France. 11. INSERM, IAME, UMR 1137, Paris, France; AP-HP, Hôpital Bichat-Claude Bernard, Service de Maladies Infectieuses et Tropicales, Paris, France. 12. Centre National de Référence des Pneumocoques, Centre Hospitalier Intercommunal de Créteil, Créteil, France. 13. INSERM, IAME, UMR 1137, Paris, France; Université Paris-Diderot, Paris, France; Inserm CIC 1425, Hôpital Bichat-Claude Bernard, Paris, France.
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.
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.