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Related Experiment Videos

Prognosis in critical care.

Lucila Ohno-Machado1, Frederic S Resnic, Michael E Matheny

  • 1Decision Systems Group, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.

Annual Review of Biomedical Engineering
|July 13, 2006
PubMed
Summary
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This review compares intensive care unit (ICU) mortality prediction models like APACHE and SAPS, highlighting the importance of model calibration and the statistical methods used. These models aid healthcare providers in treatment decisions and outcome comparisons.

Area of Science:

  • Critical Care Medicine
  • Health Services Research

Background:

  • Prognostic risk prediction models have been utilized in intensive care units (ICUs) since the 1980s.
  • These models are crucial for informing treatment decisions, prognosis, and comparing institutional outcomes.
  • Critical care prognostic models are among the most widely tested and used predictive tools in healthcare.

Purpose of the Study:

  • To review and compare key mortality prediction models used in critical care.
  • To emphasize the significance of model calibration in prognostic modeling.
  • To provide an overview of the statistical methodology, specifically multiple logistic regression, underpinning these models.

Main Methods:

  • Literature review and comparison of established mortality prediction models.

Related Experiment Videos

  • Analysis of models including APACHE, SAPS, MPM, and their subsequent iterations (e.g., APACHE-II, SAPS-II, MPM-II).
  • Discussion of the statistical underpinnings, focusing on multiple logistic regression.

    • Main Results:

    • Identified and compared several widely used prognostic models developed between 1981 and 1993.
    • Highlighted the critical role of model calibration for reliable risk prediction.
    • Reviewed the foundational statistical technique of multiple logistic regression.

    Conclusions:

    • Mortality prediction models in the ICU are essential tools for clinical decision-making and performance evaluation.
    • Accurate calibration is paramount for the clinical utility of these prognostic models.
    • Multiple logistic regression remains a cornerstone statistical method for developing critical care risk prediction models.