To identify predictors of chronic obstructive pulmonary disease (COPD) severity and assess the relation between COPD severity and risk of cardiovascular ...
Springer 2006
European Journal of Epidemiology (2006) 21:803–813 DOI 10.1007/s10654-006-9066-1
CARDIOVASCULAR DISEASES
Chronic obstructive pulmonary disease severity and cardiovascular outcomes Suellen M. Curkendall1, Stephan Lanes2, Cynthia de Luise3, Mary Rose Stang4, Judith K. Jones5, Dewei She6 and Earl Goehring Jr.5 1
Cerner LifeSciences, 1953 Gallows Road, Suite 570, Vienna, VA, 22182, USA; 2Epidemiology, Boehringer-Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA; 3Global Epidemiology – NY, Pfizer Inc., New York, NY, USA; 4Saskatchewan Health, Regina, Saskatchewan, Canada; 5The Degge Group, Ltd., Arlington, VA, USA; 6The EMMES Corporation, Rockville, MD, USA Received: 12 January 2006/Accepted in revised form 14 September 2006
Abstract. Objective: To identify predictors of chronic obstructive pulmonary disease (COPD) severity and assess the relation between COPD severity and risk of cardiovascular outcomes. Study design and setting: A cohort of patients with diagnosed and treated COPD was compiled from the Saskatchewan Health longitudinal databases. We used multivariate modeling to identify predictors of hospitalization for COPD as an indicator of COPD severity, and we used the model to characterize patients according to quintiles of COPD severity. These severity levels were used as independent variables in multivariate models of cardiovascular outcomes. Results: Determinants of COPD severity included emphysema, recent nebulizer
use, home oxygen services, corticosteroid use, frequent bronchodilator use, pneumonia and prior COPD exacerbation. The 20% of patients with the highest COPD severity were 1.27 (CI: 1.07–1.50) times more likely to have arrhythmia, 1.25 (CI: 1.07– 1.46) times more likely to have ischemic heart disease, 1.38 (CI: 1.11–1.71) times more likely to have angina, 2.28 (CI: 1.95–2.66) times more likely to have congestive heart failure, and 1.63 (CI: 1.22–2.16) times more likely to die of cardiovascular causes than the least severe 20% of patients. Conclusions: Patients with more severe COPD, as defined by our model, had higher cardiovascular morbidity and mortality than patients with less severe COPD.
Key words: Cardiovascular comorbidities, Chronic obstructive pulmonary disease, Mortality, Severity, Saskatchewan, Longitudinal Abbreviations COPD = Chronic obstructive pulmonary disease; GOLD = Global initiative for chronic obstructive lung disease; FEV = Forced expiratory volume; FEV1 = Forced expiratory volume in one second; PaO2 = Arterial oxygen tension; PaCO2 = Arterial carbon dioxide tension; BMI = Body mass index; ECG = Electrocardiogram; LVD = Left ventricular dysfunction
Introduction Patients with impaired pulmonary function have an elevated risk of cardiovascular disease [1–4]. A study in the Saskatchewan population provided evidence that the prevalence of cardiovascular diseases and incidence of hospitalization due to cardiovascular diseases is higher among patients with chronic obstructive pulmonary disease (COPD) [5]. At the same time, the relationship between the degree of severity of COPD and cardiovascular outcomes is unclear. In current clinical guidelines, such as the Global Initiative for chronic obstructive lung disease (GOLD) [6], spirometry results have been used to assign severity levels to COPD, which have been incorporated in treatment guidelines [7, 8]. Additionally, severity graded using forced expiratory volume (FEV) and forced expiratory volume in one
second (FEV1), similar to the GOLD guidelines, has been shown to be related to functional limitations such as inability to walk a quarter of a mile [9]. FEV1 has also been shown to be related to cardiovascular outcomes [3], and low FEV1 levels have been shown to predict mortality among patients with obstructive lung disease [10]. Other variables have also been investigated as potential predictors of mortality in COPD, including low values on a 6- or 12-min walk test, patientreported level of dyspnea, modified Medical Research Council (MMRC) dyspnea scale, low diffusion capacity of the lung for carbon monoxide as a percentage of alveolar volume, low arterial oxygen tension (PaO2), high arterial carbon dioxide tension (PaCO2) low body mass index (BMI), high number of medications, cor pulmonale, age, and continued smoking [10–14]. Studies of COPD patients discharged from the hospital have identified the
804 following variables as predictors of mortality: PaCO2, maintenance use of glucocorticosteroids, chronic renal failure, electrocardiogram (ECG) signs of right ventricular hypertrophy, FEV1 below 590 ml, ECG signs of ischemic heart disease, and age [15, 16]. The objectives of the present study were twofold: (1) determine how to measure severity of COPD using data elements that are available in administrative data, and (2) define the relationship between the degree of severity of COPD and cardiovascular outcomes.
Materials and methods Subjects The Province of Saskatchewan funds medical benefits of 99% (approximately 1 million) of its residents. As a result, Saskatchewan Health, a provincial government department, has accumulated and maintains longitudinal, centralized databases of health care information. The databases include health insurance registration data, physician claims, hospital separations, outpatient prescription drugs and vital statistics, including date and cause of deaths registered in Saskatchewan [17]. These data have been widely used for epidemiologic studies, including studies of drug exposure and health outcomes [18, 19], chronic diseases [20, 21], and drug utilization [22]. A cohort of patients with COPD was selected from among all individuals in the database eligible for prescription drug benefits (approximately 90% of the covered population). Inclusion criteria were as follows: (1) at least one ‘‘study diagnosis’’ (chronic airway obstruction, emphysema or chronic bronchitis – ICD-9 codes 491.0 – 492.8 or 496) was present in the physician claims or hospital separation databases any time during the period 1997–2000; (2) at least two prescriptions for an inhaled bronchodilator (anticholinergic, b-agonist or corticosteroid) during the period 1997–2000 and within 6 months of a ‘‘study diagnosis’’; and (3) 40 years old or older at the time he or she qualified. All hospital and home oxygen service records, prescription and physician claims, eligibility information, and vital statistics were obtained for the cohort for the period 1997–2001. Each patient was assigned a study index date, the earliest date between January 1, 1998 and December 31, 2000 by which time the patient fully met the COPD diagnostic and treatment criteria. Methods Developing a measure of COPD severity The study consisted of two analyses. The first analysis defined COPD severity as the likelihood of being hospitalized for COPD. In order to determine the likelihood of COPD hospitalization, a nested
case–control analysis was performed within the COPD cohort. Cases were patients with a COPD hospitalization (primary inpatient diagnoses of chronic airway obstruction, emphysema, chronic bronchitis, or bronchitis not specified as acute or chronic) during the period beginning with their study index date and ending December 31, 2001. The date of the patient’s first hospitalization during this period was defined as the event date for COPD hospitalization. Two controls without COPD hospitalizations, matched on gender and age group (5-year categories between ages 40 and 80 and a single group over age 80), were randomly selected for each case. An additional requirement for controls was that each control must have survived long enough past its study entry date to have had the hospitalization event of its corresponding case. Potential severity markers were examined for their contribution to COPD hospitalization using a logistic regression analysis. There are three categories of potential severity variables: (1) pre-existing chronic conditions, (2) recent acute conditions, and (3) recent high use of bronchodilators (see Appendix Table A1 for variable definitions). Each patient’s available diagnostic history prior to the event date was searched for the following chronic conditions: cor pulmonale, chronic airway obstruction, chronic bronchitis, emphysema, other lung disease due to external agents, and obesity. Variables representing acute conditions, recent home oxygen service, recent COPD hospitalization, recent ventilation or intubation, and recent use of bronchodilators were created by searching specific periods of 90, 180 and 365 days prior to each patient’s COPD hospitalization date. The bronchodilator use variables identified patients who were using above-average amounts of these medications, using the assumption that severely ill patients may require more respiratory medications than other patients. Although the medication amounts consumed were not known, the amount of medication dispensed during the months prior to a hospitalization was used to indicate how much was consumed or anticipated. The cut-off points for defining high drug use during 180 days are shown in Appendix Table A2. These cut-off points were determined by analyzing the combined data for cases and controls during the period 180 days before the event date (an estimated event date was used for controls). Each drug and dose form was analyzed separately. For example, separate cut-off points were tabulated for salbutamol puffers, salbutamol solution for nebulization and salbutamol powder for inhalation. Patients were ranked according to the total dosage dispensed during 180 days. For each drug/ dose form, the cut-off point was set at the amount above which only 40% of the patients were ranked. A conditional logistic regression model of the COPD hospitalizations was constructed [23] using the chronic condition, acute condition and
805 bronchodilator use variables. Since bronchodilator variables were correlated with one another, they could not all be used in the same specification. We searched for the specification that provided the most predictive power while including variables for the major classes of medications (anticholinergics, xanthines, short-acting b-agonists and steroids). Variables that were not statistically significant at the 5% level were not included in the final specification. The estimated values of the endogenous variable in this model were used to determine patients’ relative likelihood of having a COPD hospitalization. The patients were ranked according to lowest to highest likelihood of COPD hospitalization and stratified into quintiles based on the model’s estimates in a manner similar to the creation of propensity scores. Patients with the highest likelihood of having a COPD hospitalization were defined as the most severely ill. Relationship of COPD severity and cardiovascular outcomes The second analysis estimated the relationships between the estimated COPD severity level and risk for cardiovascular outcomes. The period prevalence of cardiovascular diseases and incidence rates of hospitalization for selected cardiovascular causes were computed for the period beginning with each patient’s study index date and ending on December 31, 2001, or when the patient died or became ineligible for coverage. The following cardiovascular outcomes were investigated (identified using the ICD-9 codes shown in parentheses): arrhythmia (426, 427), acute myocardial infarction (410), ischemic heart disease other than acute myocardial infarction (411, 412, 413, 414), angina (413), congestive heart failure (428), and other cardiovascular disease (391–398, 401–405, 415–417, 420–425, 429–438, 440–444, 446–448, 451–459, 798). Incident hospitalizations for cardiovascular conditions were determined using the primary diagnosis for each hospitalization. Chart review was conducted on a sample of incident cardiovascular hospitalizations [5]. Cardiovascular mortality was determined using the underlying cause of death. Multivariate logistic regression models of prevalence and Poisson models of incidence of each cardiovascular outcome were constructed. Severity of COPD was an independent variable in each model. A class variable (high, medium, low) was used to specify severity. Two different specifications of this variable were used: in the first, the two quintiles of patients (40%) with the highest severity were defined as high, those in the middle quintile (20%) were defined as medium, and those in the lowest two quintiles (40%) were defined as low. In the second, the breakout used the top 20% for high, the middle 60% for medium, and the bottom 20% for low. Age group and gender were included in all models. Additionally, the following cardiovascular risk factors, computed
during each patient’s baseline year prior to the beginning of the prevalence / incidence period, were included when significant at the 5% level: diabetes, hypertension, hypercholesterolemia, and obesity.
Results Measure of COPD severity The original cohort of 11,493 patients diagnosed with and treated for COPD comprised 46% females. The cohort was largely made up of elderly people, including 25.0% over 80, 34.4% ages 70–79, 24.8% ages 60–69, 11.1% ages 50–59 and 4.8% ages 40–49. Of the original COPD cohort, 2525 cases with a COPD hospitalization were identified. Follow-up time from index date to COPD hospitalization was £ 90 days for 40% of cases, 91 days to 1 year for 25%, between 1 and 2 years for 22%, between 2 and 3 years for 13% and between 3 and 4 years for 6%. Females comprised 44% of the cases. The cases were slightly older than the overall COPD cohort with 27.7% over 80, 39.8% ages 70–79, 23.1% ages 60–69, 7.6 ages 50–59 and 1.8% ages 40–49. The distributions of the variables tested in the model of COPD hospitalization are shown in Table 1. The results of the final model of COPD hospitalization are shown in Table 2. Of the variables representing chronic and acute conditions, those that were significant were recent home oxygen services, recent pneumonia diagnosis, recent acute exacerbation of COPD, recent COPD hospitalization, and emphysema diagnosis. Chronic bronchitis was marginally significant in some formulations but was not significant in the presence of previous COPD hospitalization. Very low proportions of patients had codes for cachexia, intubation procedures, ventilation, cor pulmonale, and other lung diseases due to external agents. Consequently, these variables were not significant in the models. Obesity and pulmonary congestion/hypostasis diagnoses were also not significant. Two related variables were investigated to represent COPD exacerbations; recent antibiotic use and antibiotic use in the presence of a respiratory diagnosis. Both were significant on their own but antibiotic use was not significant when the two variables were used in the same model. Antibiotic use in the presence of a respiratory diagnosis was chosen because it is more specific. The bronchodilator drug use variables were all significant except ‘‘any long-acting b-agonist’’. However, many were collinear and were not all significant when used together. The model specifications with the most overall predictive power included variables representing all therapeutic classes (anticholinergics, xanthines, short-acting b-agonists and steroids). The bronchodilator variable specifications that made the most significant contribution to the overall predictive power of
806 Table 1. Distributions of binary variables tested in regression models of chronic obstructive pulmonary disease (COPD) hospitalization Cases (N = 2525) Condition (ICD-9 code), procedure or prescription
N
Chronic conditions occurring any time in the patient’s available history Cor pulmonale (415.0) 15 Chronic airway obstruction (496) 2207 Chronic bronchitis (491) 645 Emphysema (492.·) 668 Other lung disease due to external agents (500–508) 7 Obesity (278.·) 61 Indicators of acute conditions occurring within 365 days prior to event Acetylcysteine Rx 16 Acute exacerbation 1050 Antibiotic Rx 1705 Cachexia diagnosis (799.4) 0 Home oxygen 786 Intubation procedure 4 Pneumonia diagnosis (480–486) 720 Previous COPD hospitalization 279 Pulmonary congestion & hypostasis diagnosis (514) 97 Ventilator 29 COPD drug use variables – 180 days prior to event Any nebulizer Rx 906 Any inhaled corticosteroid 1670 Any long-acting inhaled b-agonist 153 Any oral corticosteroid 926 High ipratropium or high xanthine 832 High combivent 434 High ipratropium 723 High short-acting inhaled b-agonist 1097 High xanthine 209
% Yes
Controls (N = 5050) N
% Yes
0.6 87.4 25.5 26.5 0.3 2.4
7 4279 909 767 14 173
0.1 84.7 18.0 15.2 0.3 3.4
0.6 41.6 67.5 0.0 31.1 0.16 28.5 11.0 3.8 1.1
7 1623 2971 1 645 3 932 190 113 33
0.1 32.1 58.8 0.02 12.8 0.1 18.5 3.8 2.2 0.7
35.9 66.1 6.1 36.7 33.0 17.2 28.6 43.4 8.3
891 2610 219 890 724 424 588 1038 190
17.6 51.7 4.3 17.6 14.3 8.4 11.6 20.6 3.8
Table 2. Results of conditional logistic regression model of chronic obstructive pulmonary disease (COPD) hospitalization*
Variable Any inhaled corticosteroid during past 180 days Any oral corticosteroid during past 180 days High amount of ipratropium or xanthine dispensed during past 180 days High amount of combivent dispensed during past 180 days High amount of short-acting inhaled b-agonists dispensed during past 180 days Any nebulizer dispensed during past 180 days Previous COPD hospitalization during past 365 days Home oxygen use during past 180 days Acute exacerbation during past 180 days Pneumonia diagnosis during past 365 days Emphysema diagnosis during patient’s available history
Parameter estimate
p-Valuea
Hazard ratio
HR 95% CI
0.291 0.498 0.470
