A New Severity of Illness Scale Using a Subset of Acute Physiology and Chronic Health Evaluation Data Elements Shows Comparable Predictive Accuracy*

Alistair E. W. Johnson; Andrew A. Kramer; Gari D. Clifford

doi:10.1097/ccm.0b013e31828a24fe

May 9, 2013Critical Care Medicine

A New Severity of Illness Scale Using a Subset of Acute Physiology and Chronic Health Evaluation Data Elements Shows Comparable Predictive Accuracy*

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Why the study?

Does the Oxford Acute Severity of Illness Score (OASIS) accurately predict ICU mortality using fewer physiologic parameters?

Structured PICO

Does the Oxford Acute Severity of Illness Score (OASIS) accurately predict ICU mortality using fewer physiologic parameters?

P

Population

81,087 ICU admissions from 2007 to 2011 at 86 ICUs across 49 U.S. hospitals where an Acute Physiology, Age, and Chronic Health Evaluation IV system had been installed.

I

Intervention

Oxford Acute Severity of Illness Score (OASIS) developed using machine-learning algorithms (particle swarm optimization)

O

Outcome

Predictive accuracy for ICU mortality (area under the receiver operating characteristic curve and calibration)

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Implication

A reduced severity of illness score (OASIS) developed using machine learning provides predictive accuracy for ICU mortality comparable to more complex existing models.

Abstract

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Cite This Study

Johnson et al. (2013) studied this question.

synapsesocial.com/papers/6a036189ccb28b3f3381e6e4 https://doi.org/10.1097/ccm.0b013e31828a24fe

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A New Severity of Illness Scale Using a Subset of Acute Physiology and Chronic Health Evaluation Data Elements Shows Comparable Predictive Accuracy*

Structured PICO

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

A New Severity of Illness Scale Using a Subset of Acute Physiology and Chronic Health Evaluation Data Elements Shows Comparable Predictive Accuracy*

Structured PICO

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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