真菌性鼻鼻窦炎发病相关因素及临床特征分析

目的:探究影响真菌性鼻鼻窦炎发病的有关因素,并分析其临床特征。方法:选取在我院就诊的150例真菌性鼻鼻窦炎患者及150例慢性鼻鼻窦炎患者,回顾性分析2组患者的临床资料,将两者进行对照研究,通过多因素Logistic回归分析和检验的方法,对患者的术前资料进行分析,进而探讨影响真菌性鼻鼻窦炎发病的因素及其临床特征。通过真菌特异性六胺银染色的方法,对150例真菌性鼻鼻窦炎患者进行分型。结果:以年龄(x_1)、病程(x_2)、涕血(x_3)、头痛(x_4)、单侧或双侧病变(x_5)和钙化斑(x_6)为变量,获得了真菌性鼻鼻窦炎发病的Logistic回归预测方程:y=-8.714+1.201 x_1+0.497 x_2+4.576 x_3+1.188 x_4+2.697x_5+4.118 x_6,P=exp(y)/[1+exp(y)]。与慢性鼻鼻窦炎的发病情况对比,发现真菌性鼻鼻窦炎的发生多位女性患者,年龄在40岁以上,病程在3年以内,主要症状为头痛和涕血,影像学检查有单侧病变,且有钙化斑出现(P0.05)。在150例真菌性鼻鼻窦炎患者中,慢性侵袭性46例,非侵袭性104例。结论:真菌性鼻鼻窦炎的发病可以通过其相关因素的Logistic回归预测方程进行预测,其临床表现有明显的特征性。

Study on Correlated Factors and Clinical Features of Fungal Rhinosinusitis

ABSTRACT Objective: To investigate the correlated factors of fungal rhinosinusitis and to analyze its clinical features. Methods: A group of 150 patients with fungal rhinosinusitis treated by surgery and another group of 150 patients with chronic rhinosinusitis in our hospital were selected and their clinical data were analyzed and compared retrospectively. The multiple factor Logistic regression analy- sis and square test were used to analyze the clinical data of all the patients. The correlated factors and the clinical features of fungal rhi- nosinusitis were investigated. Gomori methenamine silver staining which was special for fungi was used to classify the pathological types of 150 patients with fungal rhinosinusitis. Results: Age (x₁), the course of the disease (x₂), haem-nasal discharge (x₃), headache (x₄), uni- lateral/bilateral sinus lesion (x₅) and calcified plaque in CT scan (x6) were set as the concomitant variables respectively, and then we es- tablished the Logistic regression predictive equation for fungal rhinosinusitis, which was y=-8.714+1.201 x₁+0.497 x₂+4.576 x₃+1.188 x₄+2.697x₅+4.118 x₆, P=exp(y)/[1+exp(y)]. The P value meant the probability of suffering fungal rhinosinusitis. Compared with chronic rhinosinusitis, the clinical features of fungal rhinosinusitis were female, over 40 years old, the course of disease <3 years, headache, haem-nasal discharge, unilateral sinus lesion and calcified plaque in CT scan (P<0.05). Among the 150 patients with fungal rhinosinusi- tis, 46 cases were chronic invasive and 104 were non-invasive. Conclusion: The clinical features of fungal rhinosinusitis can be predicted by using the suitable logistic predictable equation and they are significant for the diagnosis of fungal rhinosinusitis.