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Baker T, Gerdin M. The clinical usefulness of prognostic prediction models in critical illness. Eur J Intern Med. 2017;45:37-40doi: 10.1016/j.ejim.2017.09.012.
Baker, T., & Gerdin, M. (2017). The clinical usefulness of prognostic prediction models in critical illness. European journal of internal medicine, 4537-40. https://doi.org/10.1016/j.ejim.2017.09.012
Baker, Tim, and Gerdin, Martin. "The clinical usefulness of prognostic prediction models in critical illness." European journal of internal medicine vol. 45 (2017): 37-40. doi: https://doi.org/10.1016/j.ejim.2017.09.012
Baker T, Gerdin M. The clinical usefulness of prognostic prediction models in critical illness. Eur J Intern Med. 2017 Nov;45:37-40. doi: 10.1016/j.ejim.2017.09.012. Epub 2017 Sep 19. PMID: 28935477.
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Eur J Intern Med. 2017 Nov;45:37-40. doi: 10.1016/j.ejim.2017.09.012. Epub 2017 Sep 19.

The clinical usefulness of prognostic prediction models in critical illness.

European journal of internal medicine

Tim Baker, Martin Gerdin

Affiliations

  1. 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. Electronic address: [email protected].
  2. 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.

PMID: 28935477 DOI: 10.1016/j.ejim.2017.09.012

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.

Copyright © 2017 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Keywords: Critical care; Critical illness; Decision support techniques; Emergency medical services; Hospital rapid response team; Prognosis

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