Prediction models for diagnosis and prognosis of the colonization or infection of multidrug-resistant organisms in adults: a systematic review, critical appraisal, and meta-analysis

被引:0
|
作者
Liu, Xu [1 ,2 ]
Liu, Xi [1 ,2 ]
Jin, Chenyue [1 ]
Luo, Yuting [1 ,3 ]
Yang, Lianping [4 ]
Ning, Xinjiao [1 ]
Zhuo, Chao [5 ]
Xiao, Fei [1 ,2 ,6 ,7 ,8 ]
机构
[1] Sun Yat Sen Univ, Affiliated Hosp 5, Dept Infect Dis, 52 MeiHua East Rd, Zhuhai 519000, Peoples R China
[2] Sun Yat Sen Univ, Guangdong Hong Kong Macao Univ Joint Lab Intervent, Affiliated Hosp 5, Hong Kong, Guangdong, Peoples R China
[3] Liuzhou Peoples Hosp, Dept Infect Dis, Liuzhou, Peoples R China
[4] Sun Yat Sen Univ, Sch Publ Hlth, Guangzhou, Peoples R China
[5] Guangzhou Med Univ, Affiliated Hosp 1, State Key Lab Resp Dis, Guangzhou, Peoples R China
[6] Sun Yat Sen Univ, Affiliated Hosp 5, Guangdong Prov Engn Res Ctr Mol Imaging, Zhuhai, Peoples R China
[7] First Peoples Hosp Kashi, Kashi Guangdong Inst Sci & Technol, Kashi, Peoples R China
[8] Sun Yat Sen Univ, Sch Pharmaceut Sci, State Key Lab Antiinfect Drug Dev, Guangzhou, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Bacterial; Clinical decision rules; Clinical prediction model; Drug resistance; Multidrug-resistant organism; Multiple; Systematic review; STAPHYLOCOCCUS-AUREUS; HOSPITAL ADMISSION; RISK; APPLICABILITY; PROBAST; BIAS; TOOL;
D O I
10.1016/j.cmi.2024.07.005
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
Background: Prediction models help to target patients at risk of multidrug-resistant organism (MDRO) colonization or infection and could serve as tools informing clinical practices to prevent MDRO transmission and inappropriate empiric antibiotic therapy. However, there is limited evidence to identify which among the available models are of low risk of bias and suitable for clinical application. Objectives: To identify, describe, appraise, and summarise the performance of all prognostic and diagnostic models developed or validated for predicting MDRO colonization or infection. Data sources: Six electronic literature databases and clinical registration databases were searched until April 2022. Study eligibility criteria: Development and validation studies of any multivariable prognostic and diagnostic models to predict MDRO colonization or infection in adults. Participants: Adults (>= 18 years old) without MDRO colonization or infection (in prognostic models) or with unknown or suspected MDRO colonization or infection (in diagnostic models). Assessment of risk of bias: The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias. Evidence certainty was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. Methods of data synthesis: Meta-analyses were conducted to summarize the discrimination and calibration of the models' external validations conducted in at least two non-overlapping datasets. Results: We included 162 models (108 studies) developed for diagnosing (n = 135) and predicting (n = 27) MDRO colonization or infection. Models exhibited a high-risk of bias, especially in statistical analysis. Highfrequency predictors were age, recent invasive procedures, antibiotic usage, and prior hospitalization. Less than 25% of the models underwent external validations, with only seven by independent teams. Metaanalyses for one diagnostic and two prognostic models only produced very low to low certainty of evidence. Conclusions: The review comprehensively described the models for identifying patients at risk of MDRO colonization or infection. We cannot recommend which models are ready for application because of the high-risk of bias, limited validations, and low certainty of evidence from meta-analyses, indicating a clear need to improve the conducting and reporting of model development and external validation studies to facilitate clinical application. Xu Liu, Clin Microbiol Infect 2024;30:1364 (c) 2024 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:1364 / 1373
页数:10
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