Distribution of pathogenic bacteria of bloodstream infection after chemotherapy in patients with acute leukemia and risk factors analysis of the occurrence of adverse events and prediction model construction
10.3760/cma.j.cn115356-20220511-00131
- VernacularTitle:急性白血病患者化疗后血流感染的病原菌分布及不良事件发生的危险因素分析和预测模型构建
- Author:
Wangyang LI
1
;
Yu FU
;
Yanping YANG
;
Hai LIN
;
Hongqiong FAN
;
Qiuju LIU
;
Sujun GAO
;
Yehui TAN
Author Information
1. 吉林大学白求恩第一医院血液科，长春 130021
- Keywords:
Acute leukemia;
Bloodstream infection;
Pathogen distribution;
Risk factors;
Prediction model
- From:
Journal of Leukemia & Lymphoma
2023;32(7):394-399
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the distribution of pathogenic bacteria of bloodstream infection after chemotherapy in patients with acute leukemia (AL), to analyze the risk factors for the occurrence of adverse events and to construct a nomogram model to predict the occurrence of adverse events.Methods:The clinical data of 313 AL patients with bloodstream infection who were admitted to the First Hospital of Jilin University from January 2018 to December 2020 were retrospectively analyzed, and the incidence, fatality and distribution characteristics of pathogenic bacteria after chemotherapy in AL patients were analyzed; the occurrence of adverse events (death or infectious shock) in patients with different clinicopathological characteristics were compared. Unconditional logistic binary regression model multifactor analysis was used to screen independent risk factors for the occurrence of adverse events in AL patients with bloodstream infection after chemotherapy; the nomogram model for predicting the occurrence of adverse events was developed by using R software; the Hosmer-Lemeshow test was used to verify the predictive effect of the model.Results:Of the 313 AL patients, the overall fatality rate was 4.2% (13/313), the all-cause fatality rate of bloodstream infection was 3.5% (11/313). Of the 313 cases, 254 cases (81.1%) were Gram-negative bacteria infection, mainly including 115 cases (45.3%) of Escherichia coli, 80 cases (31.5%) of Klebsiella pneumoniae, and 29 cases (11.4%) of Pseudomonas aeruginosa, and 10 cases (3.9%) died; 51 cases (16.3%) were Gram-positive cocci infection, mainly including 22 cases (43.1%) of Streptococcus spp., 20 cases (39.2%) of Staphylococcus spp., 7 cases (13.7%) of Enterococcus faecalis, and 0 case died; 8 cases (2.6%) were fungal infection, including 4 cases (1.3%) of Candida tropicalis, 2 cases (0.6%) of Candida subsmoothis, 1 case (0.3%) of Candida smooth, 1 case (0.3%) of new Cryptococcus, and 3 cases (37.5%) died. The differences in the occurrence rates of adverse events were statistically significant when comparing different treatment stage, risk stratification, timing of sensitive antibiotic use, total duration of fever, and glucocorticoid use in chemotherapy regimen, infecting bacteria carbapenem resistance, and leukemia remission (all P < 0.05). The results of logistic binary regression analysis showed that the use of glucocorticoid in chemotherapy regimen, the total duration of fever ≥7 d, the timing of sensitive antibiotic use ≥24 h, and carbapenem resistance of the infecting bacteria were independent risk factors for the occurrence of adverse events in AL patients with bloodstream infection after chemotherapy (all P < 0.05). A nomogram prediction model for the occurrence of adverse events in AL patients with bloodstream infection was established, and the nomogram model was calibrated and validated with good calibration and discrimination. Conclusions:The pathogenic bacteria of bloodstream infection after chemotherapy in AL patients is mainly Gram-negative bacteria, and the presence of glucocorticoid in chemotherapy regimen, long total duration of fever, poor timing of sensitive antibiotics, and infecting bacteria carbapenem resistance are risk factors for the occurrence of adverse events in AL patients with bloodstream infection after chemotherapy, and the nomogram prediction model based on these factors has a reliable predictive ability for the occurrence of adverse events.
