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A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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Health is a condition of the body, mind, and spirit where an individual remains free from illness. Similarly, wellness is an active state, including living a lifestyle that promotes physical, mental, and emotional health. Physical health is critical for the overall well-being and can be affected by lifestyle, activity level, diet, and behavior. The highest attainable standard of health is a fundamental and universal human right. Consider Lisa, a fifteen-year-old born with congenital...
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An automated computerized critical illness severity scoring system derived from APACHE III: modified APACHE.

Spyridon Fortis1, Amy M J O'Shea2, Brice F Beck3

  • 1Center for Comprehensive Access & Delivery Research & Evaluation (CADRE), Iowa City VA Health Care System, Iowa City, IA, USA; Department of Internal Medicine, Division of Pulmonary, Critical Care and Occupation Medicine, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, USA.

Journal of Critical Care
|September 23, 2018
PubMed
Summary
This summary is machine-generated.

A new automated scoring system, modified APACHE (mAPACHE), demonstrates adequate performance in predicting intensive care unit (ICU) and 30-day mortality. This tool, derived from APACHE III, offers a reliable method for assessing patient severity.

Keywords:
APACHEComputerizedCritical care outcomesMedical records systemsMortalitySeverity of illness index

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Area of Science:

  • Critical Care Medicine
  • Health Informatics
  • Biostatistics

Background:

  • Accurate prediction of patient mortality in intensive care units (ICUs) is crucial for resource allocation and clinical decision-making.
  • Existing severity scoring systems, like APACHE III, require manual data input, which can be time-consuming and prone to errors.
  • Automated systems can streamline the process of severity scoring, potentially improving efficiency and accuracy.

Purpose of the Study:

  • To evaluate the performance of a novel automated computerized ICU severity scoring system, termed modified APACHE (mAPACHE).
  • To assess the accuracy of mAPACHE in predicting both ICU and 30-day mortality.
  • To compare the performance of mAPACHE against established metrics using a large patient cohort.

Main Methods:

  • A retrospective cohort study of 490,955 patients admitted to Veterans Health Administration ICUs between 2009 and 2015.
  • Development of the mAPACHE score using electronic health records, adapting APACHE III criteria but excluding Glasgow Coma Scale, urine output, and arterial blood gas components.
  • Assessment of mAPACHE discrimination using the area under the curve (AUC) and calibration via the Hosmer-Lemeshow test and observed vs. expected mortality differences.

Main Results:

  • The ICU and 30-day mortality rates were 5.07% and 7.82%, respectively.
  • The mAPACHE score achieved an AUC of 0.771 for ICU mortality and 0.786 for 30-day mortality.
  • While the Hosmer-Lemeshow test indicated significant results (p < .001), the absolute difference between observed and expected mortality remained within ±1.53% across risk deciles, suggesting good calibration.

Conclusions:

  • The modified APACHE (mAPACHE) scoring system demonstrates adequate performance for predicting patient mortality in the ICU setting.
  • The automated nature of mAPACHE, derived from electronic health records, offers a practical approach to severity scoring.
  • Further validation across diverse patient populations and ICU settings may be warranted.