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Crit Care Med. 2017 Aug;45(8):e758-e762. doi: 10.1097/CCM.0000000000002374.

Validation of an Administrative Definition of ICU Admission Using Revenue Center Codes.

Author information

1
1Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA.2Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA.3Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.

Abstract

OBJECTIVES:

Describe the operating characteristics of a proposed set of revenue center codes to correctly identify ICU stays among hospitalized patients.

DESIGN:

Retrospective cohort study. We report the operating characteristics of all ICU-related revenue center codes for intensive and coronary care, excluding nursery, intermediate, and incremental care, to identify ICU stays. We use a classification and regression tree model to further refine identification of ICU stays using administrative data. The gold standard for classifying ICU admission was an electronic patient location tracking system.

SETTING:

The University of Pennsylvania Health System in Philadelphia, PA, United States.

PATIENTS:

All adult inpatient hospital admissions between July 1, 2013, and June 30, 2015.

INTERVENTIONS:

None.

MEASUREMENTS AND MAIN RESULTS:

Among 127,680 hospital admissions, the proposed combination of revenue center codes had 94.6% sensitivity (95% CI, 94.3-94.9%) and 96.1% specificity (95% CI, 96.0-96.3%) for correctly identifying hospital admissions with an ICU stay. The classification and regression tree algorithm had 92.3% sensitivity (95% CI, 91.6-93.1%) and 97.4% specificity (95% CI, 97.2-97.6%), with an overall improved accuracy (χ = 398; p < 0.001).

CONCLUSIONS:

Use of the proposed combination of revenue center codes has excellent sensitivity and specificity for identifying true ICU admission. A classification and regression tree algorithm with additional administrative variables offers further improvements to accuracy.

PMID:
28441234
PMCID:
PMC5511059
DOI:
10.1097/CCM.0000000000002374
[Indexed for MEDLINE]
Free PMC Article

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