Diagnosis, Prognosticators, and Management of Acute Invasive Fungal Rhinosinusitis: Multidisciplinary Consensus Statement and Evidence‐Based Review with Recommendations

Acute invasive fungal sinusitis (AIFS) is an aggressive disease that requires prompt diagnosis and multidisciplinary treatment given its rapid progression. However, there is currently no consensus on diagnosis, prognosis, and management strategies for AIFS, with multiple modalities routinely employed. The purpose of this multi-institutional and multidisciplinary evidence-based review with recommendations (EBRR) is to thoroughly review the literature on AIFS, summarize the existing evidence, and provide recommendations on the management of AIFS.The PubMed, EMBASE, and Cochrane databases were systematically reviewed from inception through January 2022. Studies evaluating management for orbital, non-sinonasal head and neck, and intracranial manifestations of AIFS were included. An iterative review process was utilized in accordance with EBRR guidelines. Levels of evidence and recommendations on management principles for AIFS were generated.A review and evaluation of published literature was performed on 12 topics surrounding AIFS (signs and symptoms, laboratory and microbiology diagnostics, endoscopy, imaging, pathology, surgery, medical therapy, management of extrasinus extension, reversing immunosuppression, and outcomes and survival). The aggregate quality of evidence was varied across reviewed domains.Based on the currently available evidence, judicious utilization of a combination of history and physical examination, laboratory and histopathologic techniques, and endoscopy provide the cornerstone for accurate diagnosis of AIFS. In addition, AIFS is optimally managed by a multidisciplinary team via a combination of surgery (including resection whenever possible), antifungal therapy, and correcting sources of immunosuppression. Higher quality (i.e., prospective) studies are needed to better define the roles of each modality and determine diagnosis and treatment algorithms.