ABSTRACT
Objective: Fungal bulb sinusitis (FBS) is mainly caused by fungal infection. Due to its similar clinical symptoms to other sinus diseases such as chronic sinusitis and sinus tumors, it is very easy to have adverse events such as missed diagnosis and misdiagnosis during diagnosis, which further affects patients' negative emotions of quality of life. Therefore, this study investigated the differences between FBS and CRS in Yunnan and western Yunnan, and analyzed the independent risk factors for the diagnosis of FBS, so as to predict the probability of diagnosis of FBS in patients with inflammatory diseases of nasal cavity and sinuses. Methods: A total of 128 FBS patients diagnosed in the First Affiliated Hospital of Dali University from January 2015 to December 2019 were retrospectively selected as the study objects, and 112 FBS patients eligible for this study were selected according to the inclusion and exclusion criteria such as Otolaryngology, Head and Neck Surgery and were set as the study group. And 112 patients with CRS diagnosed in the same period were selected as the control group. Single factor analysis (χ2 test) was applied to screen out the factors with significant differences in the preoperative clinical data of the two diseases, which were incorporated into the multivariate Logistic regression model to find independent risk factors for the diagnosis of FBS, establish the diagnosis prediction equation of the disease, and verify the sensitivity and specificity of the equation by using the collected clinical data. Results: Multifactorial analysis indicated that age, blood in aspirin, calcified spots, unilateral or bilateral lesions, single or multiple sinus tract lesions, and osteophytes were influential as independent risk factors for diagnosing FBS. The O.R.s for unilateral or bilateral lesions, calcified points, single or multiple sinus tract lesions, and blood in aspirin correlated stronger than 10 with the diagnosis of FBS. Based on these results, a logistic regression prediction equation for the diagnosis of FBS was developed: y = -6.879 + 1.295x1 + 2.519x2 + 3.010x3 + 3.605x4 + 2.977x5 + 1.596x6. P = exp(y)/[1 + exp(y)]. Validation revealed that 91.1% of FBS patients had a diagnostic probability of P>0.5 and 79.5% had a diagnostic probability of P > .9. In contrast, only 4.5% of CRS patients had a diagnostic probability of P > .5 and 0 patients had a diagnostic probability of P > .9. Conclusions: FBS remains diagnostic in unilateral or bilateral lesions, calcified spots, single or multiple sinus lesions, and aspirin-containing blood. In addition, the multifactorial regression prediction equation can calculate the probability of a preoperative diagnosis of FBS in patients with inflammatory nasal and sinus diseases, and the prediction efficacy of the established prediction model is good. In addition, the multifactor regression prediction equation has a wide range of applications and can also be used to verify the correlation of other subsequent experiments.
