abstract

• Chronic obstructive pulmonary disease is a heterogeneous disease. In this retrospective study, we hypothesize that it is possible to identify clinically relevant phenotypes by applying clustering methods to electronic medical records. We included all the patients >40 years with a diagnosis of chronic obstructive pulmonary disease admitted to the University of New Mexico Hospital between 1 January 2011 and 1 May 2014. We collected admissions, demographics, comorbidities, severity markers and treatments. A total of 3144 patients met the inclusion criteria: 46 percent were >65 years and 52 percent were males. The median Charlson score was 2 (interquartile range: 1-4) and the most frequent comorbidities were depression (36%), congestive heart failure (25%), obesity (19%), cancer (19%) and mild liver disease (18%). Using the sphere exclusion method, nine clusters were obtained: depression-chronic obstructive pulmonary disease, coronary artery disease-chronic obstructive pulmonary disease, cerebrovascular disease-chronic obstructive pulmonary disease, malignancy-chronic obstructive pulmonary disease, advanced malignancy-chronic obstructive pulmonary disease, diabetes mellitus-chronic kidney disease-chronic obstructive pulmonary disease, young age-few comorbidities-high readmission rates-chronic obstructive pulmonary disease, atopy-chronic obstructive pulmonary disease, and advanced disease-chronic obstructive pulmonary disease. These clusters will need to be validated prospectively.© The Author(s) 2016.

authors

• Vazquez Guillamet, Rodrigo
• Ursu, Oleg
• Iwamoto, Gary
• Moseley, Pope
• Oprea, Tudor I.

publication date

• November 2016

published in

• Health informatics journal  Journal

Digital Object Identifier (DOI)

• https://doi.org/10.1177/1460458216675661

PubMed ID

• 27856785

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