TY - JOUR
T1 - Chronic obstructive pulmonary disease phenotypes using cluster analysis of electronic medical records
AU - Vazquez Guillamet, Rodrigo
AU - Ursu, Oleg
AU - Iwamoto, Gary
AU - Moseley, Pope L.
AU - Oprea, Tudor
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is supported by The University of New Mexico School of Medicine.
Publisher Copyright:
© The Author(s) 2016.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
KW - asthma
KW - chronic obstructive pulmonary disease
KW - comorbidity
KW - epidemiology
KW - factor analysis
KW - phenotype
UR - http://www.scopus.com/inward/record.url?scp=85055649260&partnerID=8YFLogxK
U2 - 10.1177/1460458216675661
DO - 10.1177/1460458216675661
M3 - Article
C2 - 27856785
AN - SCOPUS:85055649260
SN - 1460-4582
VL - 24
SP - 394
EP - 409
JO - Health Informatics Journal
JF - Health Informatics Journal
IS - 4
ER -