Co-Morbidity Patterns Identified Using Latent Class Analysis of Medications Predict All-Cause Mortality Independent of Other Known Risk Factors: The COPDGene® Study

Longitudinal Assessment of Multimorbidity Medication Patterns among Smokers in the COPDGene Cohort

Background and objectives: Chronic obstructive pulmonary disease (COPD) is usually comorbid with other chronic diseases. We aimed to assess the multimorbidity medication patterns and explore if the patterns are similar for phase 1 (P1) and 5-year follow-up phase 2 (P2) in the COPDGene cohort. Materials and Methods: A total of 5564 out of 10,198 smokers from the COPDGene cohort who completed 2 visits, P1 and P2 visits, with complete medication use history were included in the study. We conducted latent class analysis (LCA) among the 27 categories of chronic disease medications, excluding COPD treatments and cancer medications at P1 and P2 separately. The best number of LCA classes was determined through both statistical fit and interpretation of the patterns. Results: We found four classes of medication patterns at both phases. LCA showed that both phases shared similar characteristics in their medication patterns: LC0: low medication; LC1: hypertension (HTN) or cardiovascular disease (CVD)+high cholesterol (Hychol) medication predominant; LC2: HTN/CVD+type 2 diabetes (T2D) +Hychol medication predominant; LC3: Hychol medication predominant. Conclusions: We found similar multimorbidity medication patterns among smokers at P1 and P2 in the COPDGene cohort, which provides an understanding of how multimorbidity medication clustered and how different chronic diseases combine in smokers.

Distribution of COPD Comorbidities and Creation of Acute Exacerbation Risk Score: Results from SCICP

Background

Chronic obstructive pulmonary disease (COPD) often coexists with multiple comorbidities which may have a significant impact on acute exacerbations of patients. At present, what kind of comorbidities affects acute exacerbations and how comorbidities lead to poor prognosis are still controversial. The purpose of our study is to determine the impact of comorbidities on COPD exacerbation and establish an acute exacerbation risk assessment system related to comorbidities.

Methods

A total of 742 COPD patients participated in the Shanghai COPD Investigation on Comorbidity Program (SCICP, ChiCTR2000030911). Finally, the baseline information of 415 participants and one-year follow-up data were involved in the analysis. We collected hemogram indices, pulmonary function tests and acute exacerbation of COPD with regular medical follow-up. Q-type cluster analysis was used to determine the clusters of participants. Receiver operating characteristic (ROC) analysis was constructed to assess the ability of indicators in predicting acute exacerbations.

Results

Almost 65% of the population we investigated had at least one comorbidity. The distribution and incidence of comorbidities differed between exacerbation group and non-exacerbation group. Three comorbidity clusters were identified: (1) respiratory, metabolic, immune and psychologic disease (non-severe cases); (2) cardiovascular and neoplastic disease (severe cases); (3) less comorbidity. Different sub-phenotypes of COPD patients showed significant distinction in health status. Anxiety (OR=5.936, P=0.001), angina (OR=10.155, P=0.025) and hypertension (OR=3.142, P=0.001) were found to be independent risk factors of exacerbation in a year. The novel risk score containing BODEx and four diseases showed great prognostic value of COPD exacerbation in developing sample.

Conclusion

Our study detailed the major interaction between comorbidities and exacerbation in COPD. Noteworthily, a novel risk score using comprehensive index – BODEx – and comorbidity parameters can identify patients at high risk of acute exacerbation.

Multimorbidity patterns and their relationship to mortality in the US older adult population

Background

Understanding patterns of multimorbidity in the US older adult population and their relationship with mortality is important for reducing healthcare utilization and improving health. Previous investigations measured multimorbidity as counts of conditions rather than specific combination of conditions.

Methods

This cross-sectional study with longitudinal mortality follow-up employed latent class analysis (LCA) to develop clinically meaningful subgroups of participants aged 50 and older with different combinations of 13 chronic conditions from the National Health Interview Survey 2002–2014. Mortality linkage with National Death Index was performed through December 2015 for 166,126 participants. Survival analyses were conducted to assess the relationships between LCA classes and all-cause mortality and cause specific mortalities.

Results

LCA identified five multimorbidity groups with primary characteristics: “healthy” (51.5%), “age-associated chronic conditions” (33.6%), “respiratory conditions” (7.3%), “cognitively impaired” (4.3%) and “complex cardiometabolic” (3.2%). Covariate-adjusted survival analysis indicated “complex cardiometabolic” class had the highest mortality with a Hazard Ratio (HR) of 5.30, 99.5% CI [4.52, 6.22]; followed by “cognitively impaired” class (3.34 [2.93, 3.81]); “respiratory condition” class (2.14 [1.87, 2.46]); and “age-associated chronic conditions” class (1.81 [1.66, 1.98]). Patterns of multimorbidity classes were strongly associated with the primary underlying cause of death. The “cognitively impaired” class reported similar number of conditions compared to the “respiratory condition” class but had significantly higher mortality (3.8 vs 3.7 conditions, HR = 1.56 [1.32, 1.85]).

Conclusion

We demonstrated that LCA method is effective in classifying clinically meaningful multimorbidity subgroup. Specific combinations of conditions including cognitive impairment and depressive symptoms have a substantial detrimental impact on the mortality of older adults. The numbers of chronic conditions experienced by older adults is not always proportional to mortality risk. Our findings provide valuable information for identifying high risk older adults with multimorbidity to facilitate early intervention to treat chronic conditions and reduce mortality.