Goodman Katherine E, Lessler Justin, Cosgrove Sara E, Harris Anthony D, Lautenbach Ebbing, Han Jennifer H, Milstone Aaron M, Massey Colin J, Tamma Pranita D
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health.
Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine.
Clin Infect Dis. 2016 Oct 1;63(7):896-903. doi: 10.1093/cid/ciw425. Epub 2016 Jun 28.
Timely identification of extended-spectrum β-lactamase (ESBL) bacteremia can improve clinical outcomes while minimizing unnecessary use of broad-spectrum antibiotics, including carbapenems. However, most clinical microbiology laboratories currently require at least 24 additional hours from the time of microbial genus and species identification to confirm ESBL production. Our objective was to develop a user-friendly decision tree to predict which organisms are ESBL producing, to guide appropriate antibiotic therapy.
We included patients ≥18 years of age with bacteremia due to Escherichia coli or Klebsiella species from October 2008 to March 2015 at Johns Hopkins Hospital. Isolates with ceftriaxone minimum inhibitory concentrations ≥2 µg/mL underwent ESBL confirmatory testing. Recursive partitioning was used to generate a decision tree to determine the likelihood that a bacteremic patient was infected with an ESBL producer. Discrimination of the original and cross-validated models was evaluated using receiver operating characteristic curves and by calculation of C-statistics.
A total of 1288 patients with bacteremia met eligibility criteria. For 194 patients (15%), bacteremia was due to a confirmed ESBL producer. The final classification tree for predicting ESBL-positive bacteremia included 5 predictors: history of ESBL colonization/infection, chronic indwelling vascular hardware, age ≥43 years, recent hospitalization in an ESBL high-burden region, and ≥6 days of antibiotic exposure in the prior 6 months. The decision tree's positive and negative predictive values were 90.8% and 91.9%, respectively.
Our findings suggest that a clinical decision tree can be used to estimate a bacteremic patient's likelihood of infection with ESBL-producing bacteria. Recursive partitioning offers a practical, user-friendly approach for addressing important diagnostic questions.
及时识别产超广谱β-内酰胺酶（ESBL）菌血症可改善临床结局，同时尽量减少包括碳青霉烯类在内的广谱抗生素的不必要使用。然而，目前大多数临床微生物实验室从鉴定微生物属和种到确认ESBL产生至少还需要额外24小时。我们的目标是开发一种用户友好的决策树，以预测哪些微生物产ESBL，从而指导适当的抗生素治疗。
我们纳入了2008年10月至2015年3月在约翰霍普金斯医院因大肠杆菌或克雷伯菌属导致菌血症的18岁及以上患者。头孢曲松最低抑菌浓度≥2µg/mL的分离株进行ESBL确证试验。使用递归划分生成决策树，以确定菌血症患者感染产ESBL菌的可能性。使用受试者工作特征曲线并通过计算C统计量来评估原始模型和交叉验证模型的辨别力。
共有1288例菌血症患者符合纳入标准。194例患者（15%）的菌血症是由确诊的产ESBL菌引起的。预测ESBL阳性菌血症的最终分类树包括5个预测因素：ESBL定植/感染史、慢性留置血管装置、年龄≥43岁、近期在ESBL高负担地区住院以及在过去6个月内接受抗生素治疗≥6天。该决策树的阳性和阴性预测值分别为90.8%和91.9%。
我们的研究结果表明，临床决策树可用于估计菌血症患者感染产ESBL菌的可能性。递归划分提供了一种实用、用户友好的方法来解决重要的诊断问题。
