RESUMO
BACKGROUND: Prediction models help target patients at risk of multidrug-resistant organism (MDRO) colonisation or infection and could serve as tools informing clinical practices to prevent MDRO transmission and inappropriate empiric antibiotic therapy. However, limited evidence identifies 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 colonisation 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 colonisation or infection in adults. ASSESSMENT OF RISK OF BIAS: The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Evidence certainty was assessed using the GRADE approach. METHODS OF DATA SYNTHESIS: Meta-analyses were conducted to summarise 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 colonisation 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 hospitalisation. 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 colonisation or infection. We cannot recommend which models are ready for application due to 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.
