Abstract: Objective: To establish a predictive model and evaluate the predictive efficacy of the model based on the influencing factors of patients with pulmonary fungal infection in ICU.Methods: A total of 198 inpatients in the ICU of our hospital from February 2018 to June 2020 were analyzed, and clinical data of all patients were collected, including fungal type, clinical symptoms, age, gender, hormone use time, broad-spectrum antibiotics type, antibiotic use time, basic diseases, mechanical ventilation, serum albumin, renal function, indwelling catheter, right heart failure, deep venous catheterization and level of granulocytes. The patients were divided into infection group(102 cases) and non-infected group(96 cases) according to whether it was secondary pulmonary fungal infection, and conducted single-factor and multi-factor analysis, and established a risk prediction model.Results: A total of 46 cases of Candida albicans were detected, accounting for 45.10%. Univariate analysis results showed that the infection group and the non-infectious group had statistically significant differences in hormone use time, broad-spectrum antibiotic use time, mechanical ventilation, serum albumin, deep venous catheterization, and diabetes(t=7.098-40.030, χ²=7.540-19.251, all P<0.05). Prediction model was P=1/{1+EXP[-(2.177+0.166×hormone use time+0.829×mechanical ventilation+0.067×antibacterial drug use time-1.150×serum albumin+0.439×deep venous catheter+0.610×diabetes) ]}. The difference between the predictive model receiver operating curve area(AUC)=0.857(95%CI: 0.801-0.913) and the random area 0.500 was statistically significant. Taking the prediction probability of 0.511(the largest Youden index) as the cutting point, the sensitivity, specificity, and accuracy of the prediction model were 82.35%, 83.33%, and 82.83%, respectively. When the cutting point was 0.500, the sensitivity, specificity and accuracy of the prediction model were 83.33%, 78.13%, and 80.81%, respectively.Conclusion: The risk prediction model established by hormone use time, broad-spectrum antibiotic use time, mechanical ventilation, serum albumin, deep venous catheterization, and diabetes can provide references for the early diagnosis of pulmonary fungal infection in ICU patients.