A diagnostic algorithm for early diagnosis and management of acute invasive fungal sinusitis.

Purpose: To define different risk groups of patients suspected of having acute invasive fungal sinusitis (AIFS) and develop a goal-directed diagnostic approach.

Materials And Methods: Forty patients with suspected AIFS biopsied from 2010 to 2020 were included in this study. Patients diagnosed with chronic invasive fungal sinusitis or without biopsy results were excluded. A recursive partitioning analysis (RPA) model was performed to define patient cohorts with the highest risk of having a positive biopsy for AIFS.

Results: There were a total of 26 patients with biopsy-proven AIFS. Patient characteristics significantly associated with an increased likelihood of a positive biopsy for AIFS on bivariate analysis included facial pain (p = 0.047), platelet count <50,000 cells/mm (p = 0.028), and abnormal CT findings, most commonly, bilateral sinus opacification (p = 0.003). The RPA model identified three risk factors for predicting a patient's probability of having a positive biopsy for AIFS, resulting in four-terminal nodes. In the twenty-six patients who had biopsy-proven AIFS, the post-operative 30-day all-cause mortality was 50 % (13/26) and overall mortality was 88.5 % (23/26). Predictors of 30-day all-cause mortality included prolonged interval between biopsy and operative start time (p = 0.042) and earlier initiation of antifungals prior to the operative start time (p = 0.042).

Conclusion: Our findings indicate that patients with a fever of unknown origin, low platelet count, and/or ANC are at an increased risk of being diagnosed with biopsy-proven AIFS. Using these risk factors, we propose a diagnostic approach that may expedite the treatment of patients with AIFS; however, future prospective studies are needed for validation.