Abstract Background Invasive fungal sinusitis (IFS) is associated with high mortality rates, chiefly because it is often diagnosed late in the course of disease when infection has spread to the orbit, skull base, or intracranially. We aimed to define early clinical and radiologic indicators of IFS, which can be integrated into a prediction model and clinical management algorithms. Methods Patients with proven or probable IFS were identified in two tertiary level hospitals as part of prospective monitoring of invasive mycoses. Patients with similar underlying medical conditions who underwent sinus CT and nasal endoscopy because of new onset sinus symptoms served as controls. CT images were reviewed by two blinded radiologists and features were scored on structured report forms. CT and clinical data were compared between cases and controls; variables associated with IFS were further assessed in multivariate regression models. Results Twenty-seven patients with IFS were identified and matched 1:1 with control patients. Ocular pain, preauricular or premaxillary fat infiltration on sinus CT and mucosal necrosis identified on nasal endoscopy were independently associated with IFS and were retained in the regression model. Model sensitivity and specificity were 0.77 and 0.96, respectively, with an area under the receiver-operating curve of 0.88. Conclusions Integration of radiologic and clinical features allowed early prediction of IFS, suggesting that these criteria could be used in the context of a clinical diagnostic algorithm.
Chazan et al. (Wed,) studied this question.