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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.
Liu, Xu; Liu, Xi; Jin, Chenyue; Luo, Yuting; Yang, Lianping; Ning, Xinjiao; Zhuo, Chao; Xiao, Fei.
Afiliación
  • Liu X; Department of Infectious Diseases, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China; Guangdong-Hong Kong-Macao University Joint Laboratory of Interventional Medicine, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China.
  • Liu X; Department of Infectious Diseases, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China; Guangdong-Hong Kong-Macao University Joint Laboratory of Interventional Medicine, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China.
  • Jin C; Department of Infectious Diseases, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China.
  • Luo Y; Department of Infectious Diseases, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China; Department of Infectious Diseases, Liuzhou People's Hospital, Liuzhou, China.
  • Yang L; School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Ning X; Department of Infectious Diseases, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China.
  • Zhuo C; State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. Electronic address: chaosheep@sina.com.
  • Xiao F; Department of Infectious Diseases, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China; Guangdong-Hong Kong-Macao University Joint Laboratory of Interventional Medicine, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China; Guangdong Provincial Engineering Research C
Clin Microbiol Infect ; 30(11): 1364-1373, 2024 Nov.
Article en En | MEDLINE | ID: mdl-38992430
ABSTRACT

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. High-frequency 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. Meta-analyses 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.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Infecciones Bacterianas / Farmacorresistencia Bacteriana Múltiple Idioma: En Revista: Clin Microbiol Infect Asunto de la revista: DOENCAS TRANSMISSIVEIS / MICROBIOLOGIA Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Infecciones Bacterianas / Farmacorresistencia Bacteriana Múltiple Idioma: En Revista: Clin Microbiol Infect Asunto de la revista: DOENCAS TRANSMISSIVEIS / MICROBIOLOGIA Año: 2024 Tipo del documento: Article