中国真菌学杂志

目的 探讨盆腔脓肿女性患者真菌感染危险因素并评估各因素的预测效能。方法 本研究为前瞻性、观察性研究，以2021年2月—2025年1月在武汉市第一医院妇产科住院治疗的255例盆腔脓肿女性患者作为研究对象。入组患者根据治疗过程中是否并发真菌感染被划分为并发真菌感染组和未并发真菌感染组。对比两组患者一般临床资料、盆腔脓肿相关因素、治疗相关因素、常规检验指标的差异。通过多因素logistic回归分析筛选与盆腔脓肿女性患者并发真菌感染风险相关的独立影响因素，绘制受试者工作特征曲线（receiver operating characteristic curve,ROC）分析各影响因素联合预测患者并发真菌感染的价值。结果 入组的盆腔脓肿女性患者年龄范围23～69岁，中位年龄42(37,50)岁，其中并发真菌感染44例，并发真菌感染率为17.3%。并发真菌感染组患者年龄、合并糖尿病占比、营养不良占比、最高体温、脓肿大小、病程、应用糖皮质激素占比、应用性激素药物占比、留置导尿管占比、使用抗生素时间、使用抗生素种类、碳青霉烯类使用占比、治疗前中性粒细胞计数、C反应蛋白、降钙素原、红细胞沉降率均高于未并发真菌感染组患者，白蛋白、IgG均低于未并发真菌感染组患者（P<0.05）。多因素logistic回归分析提示年龄、营养不良、脓肿大小、应用糖皮质激素、使用抗生素时间、使用抗生素种类、中性粒细胞计数、ESR均是盆腔脓肿女性患者并发真菌感染风险的独立危险因素，Ig G是盆腔脓肿女性患者并发真菌感染风险的独立保护因素（P<0.05）。ROC曲线分析表明，上述9种影响因素联合预测盆腔脓肿女性患者并发真菌感染风险的曲线下面积95%CI为0.847(0.785～0.909)，灵敏度为84.53%、特异度为80.25%。结论 年龄、营养不良、脓肿大小、应用糖皮质激素、抗生素使用情况、中性粒细胞计数、红细胞沉降率及IgG均是盆腔脓肿女性患者并发真菌感染的影响因素，基于上述指标可有效预测患者并发真菌感染风险。

Objective To explore the risk factors for fungal infections in female patients with pelvic abscesses and evaluate the predictive efficacy of each factor. Methods This study was a prospective, observational study. A total of 255 female patients with pelvic abscesses who were hospitalized in the Department of Obstetrics and Gynecology of Wuhan No.1 Hospital from February 2021 to January 2025 were enrolled as the research subjects. The enrolled patients were divided into a group with concurrent fungal infection and a group without concurrent fungal infection according to whether they had a concurrent fungal infection during the treatment process. The differences in general clinical data, factors related to pelvic abscesses, treatment-related factors, and routine laboratory test indicators between the two groups of patients were compared. Multivariate logistic regression analysis was used to screen the independent influencing factors related to the risk of concurrent fungal infection in female patients with pelvic abscesses. The receiver operating characteristic(ROC) curve was plotted to analyze the value of the combined prediction of concurrent fungal infection in patients by each influencing factor. Results The age range of the enrolled female patients with pelvic abscesses was 23 to 69 years old, and the median age was 42(37, 50) years old. Among them, 44 cases had concurrent fungal infections, and the incidence of concurrent fungal infections was 17.3%. The age, proportion of patients with diabetes mellitus, proportion of patients with malnutrition, maximum body temperature, abscess size, course of disease, proportion of patients using glucocorticoids, proportion of patients using sex hormone drugs, proportion of patients with indwelling urinary catheters, duration of antibiotic use, types of antibiotics used, proportion of carbapenem use, neutrophil count before treatment, C-reactive protein, procalcitonin and erythrocyte sedimentation rate(ESR) in the group with concurrent fungal infection were all higher than those in the group without concurrent fungal infection, while the albumin(ALB) and IgG were lower than those in the group without concurrent fungal infection(P<0.05). Multivariate logistic regression analysis indicated that age, malnutrition, abscess size, use of glucocorticoids, duration of antibiotic use, types of antibiotics used, neutrophil count, and ESR were all independent risk factors for the risk of concurrent fungal infection in female patients with pelvic abscesses, and IgG was an independent protective factor for the risk of concurrent fungal infection in female patients with pelvic abscesses(P<0.05). ROC curve analysis showed that the area under the curve(95% CI) of the combined prediction of the risk of concurrent fungal infection in female patients with pelvic abscesses by the above 9 influencing factors was 0.847(0.785-0.909), with a sensitivity of 84.53% and a specificity of 80.25%. Conclusion Age, malnutrition, abscess size, use of glucocorticoids, antibiotic use, neutrophil count, ESR, and IgG are all influencing factors for concurrent fungal infection in female patients with pelvic abscesses. Based on the above indicators, the risk of concurrent fungal infection in patients can be effectively predicted.