A clinical algorithm to diagnose invasive pulmonary aspergillosis in critically ill patients.

RATIONALE The clinical relevance of Aspergillus-positive endotracheal aspirates in critically ill patients is difficult to assess. OBJECTIVES We externally validate a clinical algorithm to discriminate Aspergillus colonization from putative invasive pulmonary aspergillosis in this patient group. METHODS We performed a multicenter (n = 30) observational study including critically ill patients with one or more Aspergillus-positive endotracheal aspirate cultures (n = 524). The diagnostic accuracy of this algorithm was evaluated using 115 patients with histopathologic data, considered the gold standard. Subsequently, the diagnostic workout of the algorithm was compared on the total cohort (n = 524), with the categorization based on the diagnostic criteria of the European Organization for the Research and Treatment of Cancer/Mycoses Study Group. MEASUREMENTS AND MAIN RESULTS Among 115 histopathology-controlled patients, 79 had proven aspergillosis. The algorithm judged 86 of 115 cases to have putative aspergillosis. This diagnosis was confirmed in 72 and rejected in 14 patients. The algorithm judged 29 patients to have Aspergillus colonization. This was confirmed in 22 and rejected in 7 patients. The algorithm had a specificity of 61% and a sensitivity of 92%. The positive and negative predictive values were 61 and 92%, respectively. In the total cohort (n = 524), 79 patients had proven invasive pulmonary aspergillosis (15.1%). According to the European Organization for the Research and Treatment of Cancer/Mycoses Study Group criteria, 32 patients had probable aspergillosis (6.1%) and 413 patients were not classifiable (78.8%). The algorithm judged 199 patients to have putative aspergillosis (38.0%) and 246 to have Aspergillus colonization (46.9%). CONCLUSIONS The algorithm demonstrated favorable operating characteristics to discriminate Aspergillus respiratory tract colonization from invasive pulmonary aspergillosis in critically ill patients.