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The clinical usefulness of prognostic prediction models in critical illness

  • Tim Baker
    Correspondence
    Corresponding author at: Global Health—Health Systems & Policy, Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
    Affiliations
    Global Health—Health Systems & Policy, Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden

    Perioperative Medicine & Intensive Care, Karolinska University Hospital, Stockholm, Sweden

    Department of Anaesthesia & Intensive Care, Queen Elizabeth Central Hospital, Blantyre, Malawi
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  • Martin Gerdin
    Affiliations
    Global Health—Health Systems & Policy, Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden

    Department of Clinical Science, Innovation, and Technology, Karolinska Institutet, 171 77 Stockholm, Sweden
    Search for articles by this author
Published:September 19, 2017DOI:https://doi.org/10.1016/j.ejim.2017.09.012

      Highlights

      • Prognostic prediction models are widespread within acute and critical care.
      • To be clinically useful they should have a good predictive performance, be user-friendly and guide important decisions.
      • Intervention research is required to evaluate their impact on patient outcomes.

      Abstract

      Critical illness is any immediately life-threatening disease or trauma and results in several million deaths globally every year. Responsive hospital systems for managing critical illness include quick and accurate identification of the critically ill patients. Prognostic prediction models are widely used for this aim. To be clinically useful, a model should have good predictive performance, often measured using discrimination and calibration. This is not sufficient though: a model also needs to be tested in the setting where it will be used, it should be user-friendly and should guide decision making and actions. The clinical usefulness and impact on patient outcomes of prediction models has not been greatly studied. The focus of research should shift from attempts to optimise the precision of models to real-world intervention studies to compare the performance of models and their impacts on outcomes.

      Keywords

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