摘要
目的了解恶性血液病并发侵袭性真菌感染者的相关临床信息,以提高疾病的诊疗水平。方法选取某院2010年-2015年收治的恶性血液病并发侵袭性真菌感染的病例,对入选患者进行回顾性分析。结果恶性血液病并发侵袭性真菌感染病例数逐年增加,以急性白血病为最多,约占52.83%,感染部位以肺部感染33例(62.26%)为最多;抗真菌药物的使用,氟康唑22例,伏立康唑31例,近年来伏立康唑使用明显增多。使用糖皮质激素、外周血白细胞计数≦1.0E+9/L、抗生素使用种类≧3种、抗生素使用超过7天均可使真菌感染率明显增加(P<0.05),Logistic回归分析显示:使用糖皮质激素、外周血白细胞计数≦1.0E+9/L、抗生素使用种类≥3种为恶性血液病并发侵袭性真菌感染独立危险因素(OR值分别为130.58、58.5、33.69)。结论对于恶性血液病存在影响因素特别是独立危险因素的患者及时进行抗真菌的二级预防,且尽可能选用广谱抗真菌药。
Objectives To improve the diagnosis and treatment level in our hospital by studying the clinical information of the hospitalized patients of invasive fungal infection on hematological malignancies.Methods To conduct a retrospectively analysis on the hospitalized patients of invasive fungal infection concurrent with hematological malignancies admitted in our hospital from 2010 to 2015.Results The cases of invasive fungal infection with hematological malignancies were increasing year by year.The patients with acute leukemia rank first,making up 52.83%.And 33 cases had pulmonary infection,accounting for 62.26%.22 patients were treated by Fluconazole,and 31 patients were treated by Voriconazole.In recent years,the use of Voriconazole has increased markedly.Factors that significandy increase the rate of fungal infection were as follows:using glucocorticoids;the number of white blood cells in peripheral blood being no more than 1.0E+9/L;being treated with antibiotics for more than seven days,the risk factors were as follows:using glucocorticoids;the number of white blood cells in peripheral blood being no more than 1.0E+9/L;.being treated more than three antibiotics.The OR values of above factors were 130.58,58.5 and 33.69 respectively.Conclusions We should take immediate secondary prevention measures to invasive fungal infection on the patients who existing risk factors especially as independent risk factors,and choose broad-spectrum antifungals if possible.
作者
陈炜
徐燕红
Chen Wei Xu Yanhong
出处
《中国病案》
2017年第1期101-103,共3页
Chinese Medical Record
关键词
恶性血液病
侵袭性真菌感染
住院
统计分析
Hematological malignancies
Invasive fungal infection
Hospitalization
Statistical analysis