A Predictive Model for Diagnosis of Acute Invasive Fungal Rhinosinusitis Among High-Risk Patients

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Abstract
Background: Acute invasive fungal rhinosinusitis (AIFR) is a life-threatening disease mainly affecting immunocompromised patients. Early detection is therefore key to improving patient survival. To date, there are still no standard clinical criteria for AIFR diagnosis. Objective: This study develops a predictive model that utilizes clinical presentation and computed tomography (CT) findings to diagnose AIFR. Methods: A retrospective cohort study was conducted on patients with high risk for AIFR at King Chulalongkorn Memorial Hospital over the past 15 years (2008-2022). We constructed several multivariate logistic regression models for AIFR diagnosis based on different subsets of variables from 3 categories: signs/symptoms, endoscopy, and CT imaging. Results: There were 67 AIFR-positive patients and 68 AIFR-negative patients. Combining variables from 3 categories, a 6-variable model (fever, visual loss, mucosal discoloration, crusting, mucosal loss of contrast, retroantral fat stranding) achieved the highest area under the receiver operating characteristic curve of 0.8900 (74.63% sensitivity, 89.71% specificity). Conclusions: We proposed predictive models for AIFR diagnosis in high-risk patients using clinical variables. The models can be used to guide the decision for further management such as biopsy or surgical intervention.

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