Department of Microbiology, Oslo University Hospital, Oslo, Norway.
Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
BMC Infect Dis. 2023 Apr 18;23(1):247. doi: 10.1186/s12879-023-08182-3.
Infections are major causes of disease in cancer patients and pose a major obstacle to the success of cancer care. The global rise of antimicrobial resistance threatens to make these obstacles even greater and hinder continuing progress in cancer care. To prevent and handle such infections, better models of clinical outcomes building on current knowledge are needed. This internally funded systematic review (PROSPERO registration: CRD42021282769) aimed to review multivariable models of resistant infections/colonisations and corresponding mortality, what risk factors have been investigated, and with what methodological approaches.
We employed two broad searches of antimicrobial resistance in cancer patients, using terms associated with antimicrobial resistance, in MEDLINE and Embase through Ovid, in addition to Cinahl through EBSCOhost and Web of Science Core Collection. Primary, observational studies in English from January 2015 to November 2021 on human cancer patients that explicitly modelled infection/colonisation or mortality associated with antimicrobial resistance in a multivariable model were included. We extracted data on the study populations and their malignancies, risk factors, microbial aetiology, and methods for variable selection, and assessed the risk of bias using the NHLBI Study Quality Assessment Tools.
Two searches yielded a total of 27,151 unique records, of which 144 studies were included after screening and reading. Of the outcomes studied, mortality was the most common (68/144, 47%). Forty-five per cent (65/144) of the studies focused on haemato-oncological patients, and 27% (39/144) studied several bacteria or fungi. Studies included a median of 200 patients and 46 events. One-hundred-and-three (72%) studies used a p-value-based variable selection. Studies included a median of seven variables in the final (and largest) model, which yielded a median of 7 events per variable. An in-depth example of vancomycin-resistant enterococci was reported.
We found the current research to be heterogeneous in the approaches to studying this topic. Methodological choices resulting in very diverse models made it difficult or even impossible to draw statistical inferences and summarise what risk factors were of clinical relevance. The development and adherence to more standardised protocols that build on existing literature are urgent.
感染是癌症患者疾病的主要原因,也是癌症治疗成功的主要障碍。全球范围内抗菌药物耐药性的上升威胁着使这些障碍变得更大,并阻碍癌症治疗的持续进展。为了预防和处理这些感染,需要在现有知识的基础上建立更好的临床结果模型。这项内部资助的系统评价(PROSPERO 注册:CRD42021282769)旨在综述耐药感染/定植和相应死亡率的多变量模型,调查了哪些危险因素,以及采用了哪些方法学方法。
我们使用与抗菌药物耐药性相关的术语,在 Ovid 平台上对 MEDLINE 和 Embase 进行了两次广泛的癌症患者抗菌药物耐药性搜索,此外还通过 EBSCOhost 对 Cinahl 和 Web of Science Core Collection 进行了搜索。纳入了 2015 年 1 月至 2021 年 11 月期间发表的、以人类癌症患者为对象的、明确建立多变量模型来模拟与抗菌药物耐药性相关的感染/定植或死亡率的原始、观察性研究。我们提取了有关研究人群及其恶性肿瘤、危险因素、微生物病因和变量选择方法的数据,并使用 NHLBI 研究质量评估工具评估了偏倚风险。
两次搜索共产生了 27151 条独特的记录,经过筛选和阅读后,共有 144 项研究被纳入。在研究的结果中,死亡率是最常见的(68/144,47%)。45%(65/144)的研究侧重于血液肿瘤患者,27%(39/144)研究了几种细菌或真菌。研究纳入的患者中位数为 200 例,事件中位数为 46 例。103 项(72%)研究使用基于 p 值的变量选择。最终(也是最大的)模型中纳入了中位数为 7 个变量,每个变量产生了中位数为 7 个事件。报告了一个关于耐万古霉素肠球菌的深入实例。
我们发现目前的研究在研究这个课题的方法上存在很大的差异。导致非常不同模型的方法选择使得很难甚至不可能进行统计推断,并总结出哪些危险因素具有临床相关性。迫切需要制定和遵守基于现有文献的更标准化的方案。