肺隐球菌病抗真菌治疗效果的影响因素及预测模型OA

Objective To explore the factors influencing the efficacy of antifungal therapy for pulmonary cryptococcosis(PC)and to construct a predictive model for ineffective antifungal therapy.Methods A total of 82 PC patients admitted to Jinhua People's Hospital from May 2020 to December 2024 were retrospectively selected and all received antifungal treatment.According to the CT manifestations after treatment,they were divided into the effective group(cured+improved,57 cases)and the ineffective group(ineffective,25 cases).The baseline data and clinical characteristics of the two groups of patients were compared,and binary logistic regression was used to analyze the influencing factors of ineffective PC treatment.A combined model for predicting the ineffectiveness of PC treatment was constructed,and the ROC curve was drawn to evaluate various risk factors and the efficacy of the combined model in predicting the ineffectiveness of PC treatment.The Delong method was used to compare the differences in AUC.Results There were statistically significant differences between the two groups of patients in terms of smoking history,bilateral lung lesions,immune function,combined brain infections,and diagnosis time(all P＜0.05).The results of binary logistic regression showed that having a smoking history(OR=6.400),dual lung lesions(OR=6.725),immune function impairment(OR=6.905),and combined brain infection(OR=12.899)were independent risk factors for ineffective treatment of PC(all P＜0.05).ROC curve analysis showed that the AUCs of immune function status,smoking history,dual lung lesions,combined brain infection and the combined model for predicting the ineffectiveness of PC treatment were 0.696(95％CI:0.561-0.831),0.686(95％CI:0.556-0.816),and 0.654(95％CI:0.514-0.793),0.642(95％CI:0.502-0.783),0.881(95％CI:0.796-0.967),respectively.Delong's comparison showed that the AUC of the combined model for predicting the ineffective treatment of PC was higher than that of each single factor,and the differences were statistically significant(all P＜0.05).When the cut-off value was taken,the sensitivity and specificity of the combined model were 0.720 and 0.895,respectively.Conclusion Immune dysfunction,smoking history,bilateral lung lesions,and brain infection are independent risk factors for the failure of PC treatment.The combined model has a high predictive value,which may be used to evaluate the risk of ineffective treatment of PC and provide data support for clinical diagnosis and treatment.