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A comparison of methodologies for the real-time identification of hospitalized patients with acute exacerbations of COPD

Authors Shah P, McWilliams A, Howard D, Roberge J

Received 25 May 2018

Accepted for publication 1 February 2019

Published 22 March 2019 Volume 2019:14 Pages 693—698


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 4

Editor who approved publication: Dr Richard Russell

Parth Shah,1 Andrew McWilliams,2 Daniel Howard,3 Jason Roberge4

1UNC School of Medicine, Center for Outcomes Research and Evaluation, Atrium Health, Charlotte, NC 28203, USA; 2Carolinas Hospitalist Group, Center for Outcomes Research and Evaluation, Atrium Health, Charlotte, NC 28203, USA; 3Medical Group Division, Atrium Health, Charlotte, NC 28232, USA; 4Center for Outcomes Research and Evaluation, Atrium Health, Charlotte, NC 28203, USA

Background: COPD is a lung disease characterized by chronic, irreversible airway obstruction that can precipitate into acute exacerbations of COPD (AECOPD) often requiring hospitalization. Improving these outcomes will require proactive innovations in care delivery to at-risk populations. Data-driven models to identify patients with AECOPD on admission to the hospital are needed, but do not exist.
Objective: This study aimed to compare the performance of several models designed to identify patients with AECOPD within 24 hours of hospital admission.
Methods: Clinical factors associated with admissions for AECOPD that are available within 24 hours of an encounter were combined into six different models and then tested retrospectively to evaluate each model’s performance in predicting AECOPD. The data set incorporated billing and clinical data from patients who were older than 40 years of age with an inpatient or observation encounter in 2016 at one of the nine hospitals within a large integrated healthcare system.
Results: Of the 116,329 encounters, 6,383 had a billing diagnosis for AECOPD. The models showed a wide range of sensitivity (0.473 vs 0.963) and positive predictive value (0.190 vs 0.827).
Conclusion: It is possible to leverage clinical and administrative data to identify patients admitted with AECOPD in real-time for quality improvement or research purposes. Because models relied on clinical data, local variation in care delivery also likely contributed to performance variation across hospitals. These findings emphasize the importance of testing model performance on local data and choosing the model that best aligns with the specific goals of the targeted initiative.

Keywords: quality improvement, outcomes research, AECOPD, model, validity

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