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Systematic review finds risk of bias and applicability concerns for models predicting central line-associated bloodstream infection.
Gao, Shan; Albu, Elena; Tuand, Krizia; Cossey, Veerle; Rademakers, Frank; Van Calster, Ben; Wynants, Laure.
Afiliación
  • Gao S; Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
  • Albu E; Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
  • Tuand K; 2Bergen - Learning Centre Désiré Collen, KU Leuven Libraries, KU Leuven, Leuven, Belgium.
  • Cossey V; Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Infection Control and Prevention, University Hospitals Leuven, Leuven, Belgium.
  • Rademakers F; Faculty of Medicine, KU Leuven, Leuven, Belgium.
  • Van Calster B; Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands; EPI-Center, KU Leuven, Leuven, Belgium. Electronic address: ben.vancalster@kuleuven.be.
  • Wynants L; Department of Development and Regeneration, KU Leuven, Leuven, Belgium; EPI-Center, KU Leuven, Leuven, Belgium; Care & Public Health Research Institute, Maastricht University, Maastricht, The Netherlands.
J Clin Epidemiol ; 161: 127-139, 2023 09.
Article en En | MEDLINE | ID: mdl-37536503
OBJECTIVES: To systematically review the risk of bias and applicability of published prediction models for risk of central line-associated bloodstream infection (CLA-BSI) in hospitalized patients. STUDY DESIGN AND SETTING: Systematic review of literature in PubMed, Embase, Web of Science Core Collection, and Scopus up to July 10, 2023. Two authors independently appraised risk models using CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and assessed their risk of bias and applicability using Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS: Sixteen studies were included, describing 37 models. When studies presented multiple algorithms, we focused on the model that was selected as the best by the study authors. Eventually we appraised 19 models, among which 15 were regression models and four machine learning models. All models were at a high risk of bias, primarily due to inappropriate proxy outcomes, predictors that are unavailable at prediction time in clinical practice, inadequate sample size, negligence of missing data, lack of model validation, and absence of calibration assessment. 18 out of 19 models had a high concern for applicability, one model had unclear concern for applicability due to incomplete reporting. CONCLUSION: We did not identify a prediction model of potential clinical use. There is a pressing need to develop an applicable model for CLA-BSI.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sepsis Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Bélgica Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sepsis Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Bélgica Pais de publicación: Estados Unidos