Development and Validation of a Healthcare Utilization-Based Algorithm to Identify Acute Exacerbations of Chronic Obstructive Pulmonary Disease

Douglas W Mapel; Melissa H Roberts; Susan Sama; Priyanka J Bobbili; Wendy Y Cheng; Mei Sheng Duh; Catherine Nguyen; Philippe Thompson-Leduc; Melissa K Van Dyke; Kieran J Rothnie; Devi Sundaresan; Julia M Certa; Thomas S Whiting; Jennifer L Brown; Douglas W Roblin

doi:10.2147/COPD.S302241

Development and Validation of a Healthcare Utilization-Based Algorithm to Identify Acute Exacerbations of Chronic Obstructive Pulmonary Disease

Int J Chron Obstruct Pulmon Dis. 2021 Jun 9:16:1687-1698. doi: 10.2147/COPD.S302241. eCollection 2021.

Authors

Affiliations

¹ College of Pharmacy, University of New Mexico, Albuquerque, NM, USA.
² Reliant Medical Group, Inc., Worcester, MA, USA.
³ Analysis Group, Inc., Boston, MA, USA.
⁴ GlaxoSmithKline plc., London, UK.
⁵ Kaiser Permanente Mid-Atlantic States (KPMAS), Mid-Atlantic Permanente Research Institute (MAPRI), Rockville, MD, USA.

^# Contributed equally.

Abstract

Introduction: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are important events that may precipitate other adverse outcomes. Accurate AECOPD event identification in electronic administrative data is essential for improving population health surveillance and practice management.

Objective: Develop codified algorithms to identify moderate and severe AECOPD in two US healthcare systems using administrative data and electronic medical records, and validate their performance by calculating positive predictive value (PPV) and negative predictive value (NPV).

Methods: Data from two large regional integrated health systems were used. Eligible patients were identified using International Classification of Diseases (Ninth Edition) COPD diagnosis codes. Two algorithms were developed: one to identify potential moderate AECOPD by selecting outpatient/emergency visits associated with AECOPD-related codes and antibiotic/systemic steroid prescriptions; the other to identify potential severe AECOPD by selecting inpatient visits associated with corresponding codes. Algorithms were validated via patient chart review, adjudicated by a pulmonologist. To estimate PPV, 300 potential moderate AECOPD and 250 potential severe AECOPD events underwent review. To estimate NPV, 200 patients without any AECOPD identified by the algorithms (100 patients each without moderate or severe AECOPD) during the two years following the index date underwent review to identify AECOPD missed by the algorithm (false negatives).

Results: The PPVs (95% confidence interval [CI]) for both moderate and severe AECOPD were high: 293/298 (98.3% [96.1-99.5]) and 216/225 (96.0% [92.5-98.2]), respectively. NPV was lower for moderate AECOPD (75.0% [65.3-83.1]) than for severe AECOPD (95.0% [88.7-98.4]). Results were consistent across both healthcare systems.

Conclusion: This study developed healthcare utilization-based algorithms to identify moderate and severe AECOPD in two separate healthcare systems. PPV for both algorithms was high; NPV was lower for the moderate algorithm. Replication and consistency of results across two healthcare systems support the external validity of these findings.

Keywords: AECOPD events; algorithm; claims database; electronic medical records; predictive values; validation.

MeSH terms

Algorithms
Databases, Factual
Electronic Health Records
Humans
International Classification of Diseases
Patient Acceptance of Health Care
Pulmonary Disease, Chronic Obstructive* / diagnosis
Pulmonary Disease, Chronic Obstructive* / epidemiology
Pulmonary Disease, Chronic Obstructive* / therapy

Grants and funding

This study was sponsored and funded by GlaxoSmithKline plc. (study 210043). The study sponsor participated in the conception and design of the study, analysis and interpretation of the data, drafting and critical revision of the report, and approved submission of the manuscript. All authors had access to the results of the analyses, reviewed and edited the manuscript, approved the final draft, and were involved in the decision to submit the manuscript for publication.