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.