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Related Experiment Videos

Can we predict prognosis using mortality probability model IIo?

Yaseen Arabi1, Radoslow Goraj, Abdullah Al-Shimemeri

  • 1Intensive Care Department, MC 1425, King Fahad National Guard Hospital, PO Box 22490, Riyadh 11426, Kingdom of Saudi Arabia. arabi@ngha.med.sa

Saudi Medical Journal
|October 9, 2002
PubMed
Summary

The Mortality Probability Model (MPM) IIo accurately predicts death in intensive care unit patients with 95% certainty. While highly specific, its sensitivity is low for identifying all critically ill patients who may not benefit from ICU care.

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Area of Science:

  • Critical Care Medicine
  • Prognostic Modeling
  • Health Outcomes Research

Background:

  • Predicting prognosis in intensive care unit (ICU) admissions is crucial for resource allocation and clinical decision-making.
  • The Mortality Probability Model (MPM) IIo is a tool designed to estimate mortality risk in critically ill patients.
  • Evaluating the accuracy of prognostic models in diverse populations is essential for their reliable application.

Purpose of the Study:

  • To assess the effectiveness of the Mortality Probability Model (MPM) IIo in predicting very poor prognosis among intensive care unit (ICU) patients.
  • To determine the sensitivity, specificity, and predictive values of MPM IIo at different cutoff points (90% and 95%).

Main Methods:

  • A prospective observational study was conducted in a medical-surgical ICU at a tertiary care teaching hospital in Riyadh, Saudi Arabia.

Related Experiment Videos

  • Data for MPM IIo calculation was collected from 557 patients admitted to the ICU between March 1999 and February 2000.
  • The study calculated sensitivity, specificity, positive predictive value, and negative predictive value for MPM IIo using 90% and 95% mortality prediction cutoffs.

    • Main Results:

    • At a 95% predicted mortality cutoff, MPM IIo demonstrated 100% specificity and 100% positive predictive value, correctly identifying all non-survivors in this high-risk group.
    • However, the sensitivity at the 95% cutoff was only 18%, indicating it missed a significant proportion of patients who ultimately died.
    • Using a 90% predicted mortality cutoff, specificity remained high at 99.7% and positive predictive value at 97.7%, with sensitivity at 25%.

    Conclusions:

    • The Mortality Probability Model (MPM) IIo, particularly at a 95% predicted mortality threshold, exhibits high specificity for identifying patients with a very poor prognosis in the ICU.
    • The model's low sensitivity suggests it may not be suitable for identifying all patients unlikely to benefit from ICU admission.
    • MPM IIo can serve as a valuable adjunct to clinical judgment for managing critically ill patients with extremely poor predicted outcomes.