Ical variables available were submitted to MCA. The variables 76932-56-4 web included in these analyses were comorbidities, and data obtained from CT analysis, including emphysema, bronchial thickening and bronchiectasis. MCA identified 17 axes of which 3 were excluded because they happened to be correlated mostly with missing information on comorbidities (Table S6 and S7). Thus, we were able to exclude these 3 axes without losing significant information and only 14 axes were kept for cluster analysis.Identification of COPD Phenotypes using Cluster Analysis and Mortality RatesWe performed a Ward’s cluster analysis based on the significant mathematical axes identified by PCA and MCA for continuous and categorical variables, respectively. Classification of the 527 COPD patients resulted in a dendrogram showing the progressive joining of the clustering process (Figure 2). Based on visual assessment of the dendrogram, data could be optimally grouped into 3 or 5 clusters, each cluster corresponding to a potential phenotype. To decide on the number of phenotypes, we examined mortality rates among clusters. When grouping the data into 3 clusters, there was a clear difference in mortality rates among CASIN web clusters (Table 2 and Figure 2). Grouping the data into 5 clusters did not improve the ability to predict mortality because this only resulted in the division of clusters 1 and 3 into two new clusters (for each), but mortality was comparable in these newly formed clusters (Figure 2).Vital Status and Survival AnalysesVital status was assessed as per January 1st 2010. For patients followed at the University hospital, mortality data were obtained from medical files. When no data on mortality was retrieved, general practitioners (GP’s) caring for the patient were contacted to check survival. For subjects from the NELSON study, survival was checked by direct telephone contact with GP’s. Subjects who were lost to follow-up (n = 8) were not included in the survival analysis because no information was available on their vital status. Additionally the exact date of death was unavailable in 8 subjects who died during follow-up. Thus, the survival analyses were performed in 511/527 (97 ) subjects. Survival analyses were performed on all-cause mortality using Kaplan-Meier and log-rank tests with Tukey-Kramer adjustments for multiple comparisons. Because age was markedly different among Phenotypes, we further studied mortality risk using a Cox model adjusted for age.Characterization of COPD PhenotypesCharacteristics of subjects grouped into 3 clusters (phenotypes) are presented in Table 2. Phenotype 1 (n = 219 subjects) corresponded to subjects with a median [IQR] age of 62 [58?8] yrs., mild to moderate airflow limitation, absent or mild emphysema, absent or mild dyspnoea, normal nutritional status and limited comorbidities. Two third of these subjects were recruited in the NELSON study whereas one third of these subjects were recruited in the LEUVEN clinic. Of note, 95 of the NELSON subjects 1379592 clustered in this phenotype. Only 1/219 (0.5 ) subject died in this phenotype. Phenotype 2 (n = 99 subjects) corresponded to subjects with a median [IQR] age of 61 [57?6] yrs., severe airflow limitation,COPD Phenotypes at High Risk of MortalityTable 1. Description of the 527 COPD patients based on spirometric GOLD classification.GOLD I n = 120 Demographic Age, yrs. Male sex, BMI, kg/m2 Smoking, pack-year Source of recruitment NELSON study, ( NELSON) LEUVEN clinic, ( LEUVEN) Pulm.Ical variables available were submitted to MCA. The variables included in these analyses were comorbidities, and data obtained from CT analysis, including emphysema, bronchial thickening and bronchiectasis. MCA identified 17 axes of which 3 were excluded because they happened to be correlated mostly with missing information on comorbidities (Table S6 and S7). Thus, we were able to exclude these 3 axes without losing significant information and only 14 axes were kept for cluster analysis.Identification of COPD Phenotypes using Cluster Analysis and Mortality RatesWe performed a Ward’s cluster analysis based on the significant mathematical axes identified by PCA and MCA for continuous and categorical variables, respectively. Classification of the 527 COPD patients resulted in a dendrogram showing the progressive joining of the clustering process (Figure 2). Based on visual assessment of the dendrogram, data could be optimally grouped into 3 or 5 clusters, each cluster corresponding to a potential phenotype. To decide on the number of phenotypes, we examined mortality rates among clusters. When grouping the data into 3 clusters, there was a clear difference in mortality rates among clusters (Table 2 and Figure 2). Grouping the data into 5 clusters did not improve the ability to predict mortality because this only resulted in the division of clusters 1 and 3 into two new clusters (for each), but mortality was comparable in these newly formed clusters (Figure 2).Vital Status and Survival AnalysesVital status was assessed as per January 1st 2010. For patients followed at the University hospital, mortality data were obtained from medical files. When no data on mortality was retrieved, general practitioners (GP’s) caring for the patient were contacted to check survival. For subjects from the NELSON study, survival was checked by direct telephone contact with GP’s. Subjects who were lost to follow-up (n = 8) were not included in the survival analysis because no information was available on their vital status. Additionally the exact date of death was unavailable in 8 subjects who died during follow-up. Thus, the survival analyses were performed in 511/527 (97 ) subjects. Survival analyses were performed on all-cause mortality using Kaplan-Meier and log-rank tests with Tukey-Kramer adjustments for multiple comparisons. Because age was markedly different among Phenotypes, we further studied mortality risk using a Cox model adjusted for age.Characterization of COPD PhenotypesCharacteristics of subjects grouped into 3 clusters (phenotypes) are presented in Table 2. Phenotype 1 (n = 219 subjects) corresponded to subjects with a median [IQR] age of 62 [58?8] yrs., mild to moderate airflow limitation, absent or mild emphysema, absent or mild dyspnoea, normal nutritional status and limited comorbidities. Two third of these subjects were recruited in the NELSON study whereas one third of these subjects were recruited in the LEUVEN clinic. Of note, 95 of the NELSON subjects 1379592 clustered in this phenotype. Only 1/219 (0.5 ) subject died in this phenotype. Phenotype 2 (n = 99 subjects) corresponded to subjects with a median [IQR] age of 61 [57?6] yrs., severe airflow limitation,COPD Phenotypes at High Risk of MortalityTable 1. Description of the 527 COPD patients based on spirometric GOLD classification.GOLD I n = 120 Demographic Age, yrs. Male sex, BMI, kg/m2 Smoking, pack-year Source of recruitment NELSON study, ( NELSON) LEUVEN clinic, ( LEUVEN) Pulm.
