Subtypes of patients experiencing exacerbations of COPD and associations with outcomes

Abstract

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous condition characterized by occasional exacerbations. Identifying clinical subtypes among patients experiencing COPD exacerbations (ECOPD) could help better understand the pathophysiologic mechanisms involved in exacerbations, establish different strategies of treatment, and improve the process of care and patient prognosis. The objective of this study was to identify subtypes of ECOPD patients attending emergency departments using clinical variables and to validate the results using several outcomes. We evaluated data collected as part of the IRYSS-COPD prospective cohort study conducted in 16 hospitals in Spain. Variables collected from ECOPD patients attending one of the emergency departments included arterial blood gases, presence of comorbidities, previous COPD treatment, baseline severity of COPD, and previous hospitalizations for ECOPD. Patient subtypes were identified by combining results from multiple correspondence analysis and cluster analysis. Results were validated using key outcomes of ECOPD evolution. Four ECOPD subtypes were identified based on the severity of the current exacerbation and general health status (largely a function of comorbidities): subtype A (n = 934), neither high comorbidity nor severe exacerbation; subtype B (n = 682), moderate comorbidities; subtype C (n = 562), severe comorbidities related to mortality; and subtype D (n = 309), very severe process of exacerbation, significantly related to mortality and admission to an intensive care unit. Subtype D experienced the highest rate of mortality, admission to an intensive care unit and need for noninvasive mechanical ventilation, followed by subtype C. Subtypes A and B were primarily related to other serious complications. Hospitalization rate was more than 50% for all the subtypes, although significantly higher for subtypes C and D than for subtypes A and B. These results could help identify characteristics to categorize ECOPD patients for more appropriate care, and help test interventions and treatments in subgroups with poor evolution and outcomes.

Figure 1. Graphical displays of the first component derived from the multiple correspondence analysis.

Full legend: Maps created by the second (a) and the third (b) components with respect to the first one, respectively. Black dots represent the categories of the active variables. The closer the points are, stronger is the relationship between the categories. The horizontal axis in both graphs represents the first component, interpreted as observed (left side) vs. missing (right side) arterial blood gases. (a) The second component, vertical axis, represents the severity of the exacerbation or the acute COPD process, more acute (bottom side) vs. less acute (top side). (b) The third component, vertical axis, represents the comorbidity status, more severe (bottom side) vs. less severe (top side). Blue dots represent the relative position of the outcomes.

Figure 2. Map created by the second and third components derived from the multiple correspondence analysis.

Full legend: The horizontal axis, second component, can be interpreted as an index of the severity of the exacerbation or the acute COPD process, more acute (left side) vs. less acute (right side). The vertical axis, third component, can be interpreted as an index of the comorbidity status, more severe (bottom) vs. less severe (top). Black dots in the plane represent the categories of the active variables included in the multiple correspondence analysis, only the most representative ones were labeled. The closer the points are, stronger is the relationship between the categories. Relative positions of the subjects in this plane are represented by different colors, depending on the subtype provided by the cluster analysis. Large blue dots represent the relative position of the outcomes.

Figure 3. Partial dendrogram obtained from the cluster analysis.

Full legend: The dendogram represents the results from the cluster analysis performed with the three components obtained from the multiple correspondence analysis. The graphical display includes an easy interpretation of the partition and a brief description of the resulting groups.