摘要
目的探寻影响恶性血液病伴碳青霉烯类耐药菌感染病人死亡风险因素、建立死亡风险预测模型,并对模型进行评估和验证。方法收集2018年11月至2021年2月郑州大学第一附属医院158例碳青霉烯类耐药菌感染的恶性血液病病人资料(建模组121例,验证组37例),建模组,根据其离院时存活与否,分为58例的存活亚组、63例的死亡亚组。纳入病人基本资料、耐碳青霉烯类抗菌药物细菌感染前各种抗生素应用情况、血液病种、血常规及生化、侵入性操作或者手术、造血干细胞移植术(HSCT)、入住重症监护室(ICU)以及基础疾病或合并症等因素,进行单因素分析。logistic回归分析方程中纳入那些P<0.20的指标,以其作为自变量,应用方程所输出的风险因子以及因子的相关系数,建立死亡风险回归方程。进而纳入验证组37例病人资料对模型进行验证。结果建模组存活亚组和死亡亚组在耐药菌感染前住院时间[(13.40±10.02)d比(23.35±15.52)d]、抗生素使用时间[(11.03±9.33)d比(19.56±15.43)d]、抗生素使用种类[(2.81±1.87)种比(4.00±1.86)种]、最多几种抗生素联合应用[(1.67±0.925)种比(2.10±0.59)种]、使用过碳青霉烯类抗菌药物与否(40/18比58/5)、使用过其余特殊使用级抗生素与否(24/34比45/18)、中性粒细胞计数[0.55(0.05,3.64)×10^(9)/L比0.06(0.03,0.22)×10^(9)/L]、粒缺持续时间[3.0(0,13.0)d比12.0(8.0,19.0)d]、是否有心脑血管疾病病史(10/48比21/42)以及血液病谱(22/13/6/5/12比34/6/10/10/3)等方面差异有统计学意义(P<0.05)。是否接受过化疗(51/7与61/2)及是否接受过HSCT(8/50比4/59)在单因素分析结果中0.05<P<0.20。P<0.20的单因素分析变量纳入二元logistic回归方程进行建立模型,共输出4个风险因素,得到建模方程:logistic(P)=0.061×a(感染前住院天数)+1.868×b(感染前应用过其余的特殊使用级性抗生素与否)-0.412×c(感染前中性粒细胞计数)+1.345×d(患
Objective To explore the risk factors affecting the mortality of patients with malignant hematologic diseases with carbape‑nem-resistant bacterial infections to establish a mortality risk prediction model,and to evaluate and validate the model.Methods Data from 158 malignant hematologic patients with carbapenem-resistant bacterial infections in the First Affiliated Hospital of Zhengzhou University were collected from November 2018 to February 2021(121 in the modeling group and 37 in the validation group),and they were divided into a survival subgroup of 58 cases and a death subgroup of 63 cases according to whether they were alive or not at the time of their discharge from the hospital.Basic information,various antibiotic applications before carbapenem-resistant antimicrobial bacterial infections,types of hematological diseases,routine blood and biochemistry,invasive operations or surgeries,hematopoietic stem cell transplantation(HSCT),admission to the intensive care unit(ICU),and underlying diseases or comorbidities of the patients were included in the one-way analysis.Logistic regression analysis equations incorporated those indicators with P<0.20 as indepen-dent variables and applied the risk factors output from the equations as well as the correlation coefficients of the factors to create a re-gression equation for the risk of death.The model was further validated by including the data of 37 patients in the validation group.Results The survival and death subgroups of the modeling group were characterized by the duration of hospitalization before infection with resistant bacteria[(13.40±10.02)d vs.(23.35±15.52)d],the duration of antibiotic use[(11.03±9.33)d vs.(19.56±15.43)d],the types of antibiotics used[(2.81±1.87)species vs.(4.00±1.86)species],the most types of antibiotics in combination[(1.67±0.925)spe-cies vs.(2.10±0.59)species],use of carbapenem antimicrobials or not(40/18 vs.58/5),use of the remaining special-use class of antibiot-ics or not(24/34 vs.45/18),neutrophil counts[0.55(0.05,3.64)×1
作者
徐新童
李俏俏
梁春艳
张聪丽
邢海洲
XU Xintong;LI Qiaoqiao;LIANG Chunyan;ZHANG Congli;XING Haizhou(Department of Hematology,The First Affiliated Hospital of Zhengzhou University,Zhengzhou,He'nan 450052,China)
出处
《安徽医药》
CAS
2024年第1期118-123,共6页
Anhui Medical and Pharmaceutical Journal
关键词
血液肿瘤
碳青霉烯类
抗药性
细菌
耐药性
死亡
危险性评估
预测模型
Hematologic neoplasms
Carbapenems
Drug resistance,bacterial
Drug resistance
Death
Risk assessment
Predictive modeling
