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Front Med (Lausanne). 2018 Jun 11;5:173. doi: 10.3389/fmed.2018.00173. eCollection 2018.

The Laboratory-Based Intermountain Validated Exacerbation (LIVE) Score Identifies Chronic Obstructive Pulmonary Disease Patients at High Mortality Risk.

Author information

1
Division of Pulmonary and Critical Care Medicine, Department of Medicine, Intermountain Medical Center, Murray, UT, United States.
2
Division of Respiratory, Critical Care, and Sleep Medicine, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States.
3
Office of Research, Intermountain Healthcare, Salt Lake City, UT, United States.
4
Homer Warner Center for Informatics Research, Murray, UT, United States.
5
Intermountain Medical Center, Intermountain Heart Institute, Murray, UT, United States.
6
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
7
Section of General Internal Medicine, Department of Medicine, University of Chicago Medicine, Chicago, IL, United States.
8
Section of Pulmonary and Critical Care Medicine, Department of Medicine, University of Chicago Medicine, Chicago, IL, United States.
9
Kaiser Permanente Center for Health Research-Northwest, Portland, OR, United States.
10
Division of Pulmonary and Critical Care Medicine, Oregon Health & Science University, Portland, OR, United States.
11
Division of Pulmonary and Critical Care Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States.
12
Division of Pulmonary and Critical Care Medicine, University of California, San Francisco, San Francisco, CA, United States.

Abstract

Background: Identifying COPD patients at high risk for mortality or healthcare utilization remains a challenge. A robust system for identifying high-risk COPD patients using Electronic Health Record (EHR) data would empower targeting interventions aimed at ensuring guideline compliance and multimorbidity management. The purpose of this study was to empirically derive, validate, and characterize subgroups of COPD patients based on routinely collected clinical data widely available within the EHR. Methods: Cluster analysis was used in 5,006 patients with COPD at Intermountain to identify clusters based on a large collection of clinical variables. Recursive Partitioning (RP) was then used to determine a preferred tree that assigned patients to clusters based on a parsimonious variable subset. The mortality, COPD exacerbations, and comorbidity profile of the identified groups were examined. The findings were validated in an independent Intermountain cohort and in external cohorts from the United States Veterans Affairs (VA) and University of Chicago Medicine systems. Measurements and Main Results: The RP algorithm identified five LIVE Scores based on laboratory values: albumin, creatinine, chloride, potassium, and hemoglobin. The groups were characterized by increasing risk of mortality. The lowest risk, LIVE Score 5 had 8% 4-year mortality vs. 56% in the highest risk LIVE Score 1 (p < 0.001). These findings were validated in the VA cohort (n = 83,134), an expanded Intermountain cohort (n = 48,871) and in the University of Chicago system (n = 3,236). Higher mortality groups also had higher COPD exacerbation rates and comorbidity rates. Conclusions: In large clinical datasets across different organizations, the LIVE Score utilizes existing laboratory data for COPD patients, and may be used to stratify risk for mortality and COPD exacerbations.

KEYWORDS:

COPD; LIVE Score; cluster analysis; comorbidity; informatics; risk stratification

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