Abstract
Background:
Chronic obstructive pulmonary disease (COPD) is highly prevalent in older adults with heart failure and heart failure is highly prevalent in older adults with COPD. Information is presently lacking about the extent to which COPD and heart failure co-occur among nursing home residents.
Objective:
To describe the epidemiology of, and factors associated with, COPD among nursing home residents with heart failure.
Methods:
This cross-sectional study included 97,495 long-term stay nursing home residents with heart failure in 2018. The Minimum Data Set 3.0 (MDS) provided information on sociodemographic characteristics, comorbid conditions, and activities of daily living. Heart failure and COPD were defined based on notes at admission, hospitalizations, progress notes, and through physical examination findings.
Results:
The majority of the study population were ≥ 75 years old (74.1%), women (67.3%), and Non-Hispanic Whites (77.4%). Nearly one in five residents had reduced ejection fraction findings, 23.1% had a preserved ejection fraction, and 53.8% of nursing home residents with heart failure had COPD. This pulmonary condition was less frequently noted in women, residents of advanced age, and racial/ethnic minorities and more frequently diagnosed in residents with comorbid conditions such as pneumonia, anxiety, obesity, diabetes mellitus, and coronary artery disease.
Conclusions:
We found a high prevalence of COPD, and identified several factors associated with COPD, in nursing home residents with heart failure. Our findings highlight challenges in the clinical management of COPD in nursing home residents with heart failure and how best to meet the care needs of this understudied population.
1. INTRODUCTION
As one of the major global health concerns, especially with an aging population, heart failure affects millions of people worldwide with significant associated morbidity and mortality (1). Heart failure disproportionately affects older adults, and its prevalence increases with advancing age (2). Heart failure is the most common reason for hospitalization in older patients, with more than 85% of all patients hospitalized with heart failure aged 65 years and older (3–6). With the aging of the world’s population, increases in the number of older adults affected by heart failure are expected.
People are living longer, healthier lives due to advances in the early detection and effective treatment of commonly occurring diseases and increased longevity has led to many older adults being diagnosed with multiple chronic conditions (7). Understanding how having multiple chronic conditions adversely affects patient-centered and clinical outcomes of older adults is of considerable clinical and public health importance (8). These multiple comorbid conditions are concerning because they may precipitate acute decompensation which in turn leads to greater health care utilization and increases the risk of nonfatal complications and death (9).
The objectives of this large cross-sectional study were to describe the sociodemographic and clinical characteristics of nursing home residents with heart failure and Chronic Obstructive Pulmonary Disease (COPD). This pulmonary condition is one of the comorbidities with the highest mortality risk among Medicare beneficiaries hospitalized with heart failure (9). In nursing homes, residents with COPD are more likely to have comorbid heart failure than those without COPD (10). However, little is known about the epidemiology of the co-occurrence of heart failure and COPD in older adults, especially among nursing home residents, a population often neglected in clinical research. A better understanding of the co-occurrence of COPD and heart failure in nursing home residents is needed to inform the development of new interventions, or to improve existing ones, reduce the disease burden associated with these chronic conditions among older adults, and tailor management protocols according to residents’ needs.
2. METHODS
2.1. Study Design and Data Source
The Institutional Review Board at the University of Massachusetts Medical School approved this observational study.
Three national datasets from the United States were linked for purposes of the present study: 1) 2018 Minimum Data Set (MDS) Version 3.0, 2) Medicare Master Beneficiary Summary File, and 3) the Medicare hospitalization data (Part A, MEDPAR). The MDS is a federally mandated geriatric clinical assessment tool which provides a standardized assessment to trigger care management protocols for nursing home residents (11). A multidisciplinary team of trained nursing home staff complete a comprehensive clinical assessment on all residents at the time of nursing home admission, annually, and when there is a significant change in the resident’s clinical status; an abbreviated assessment is conducted quarterly and at the time of nursing home discharge. The MDS captures information on residents’ demographic and clinical characteristics including their age, sex, race/ethnicity, activities of daily living, cognitive impairment assessment, pain, as well as comorbidities such as hypertension, end stage renal disease, and obesity (11).
Medicare is a U.S federal health insurance program for individuals aged ≥ 65 years, individuals with disabilities <65 years old, and individuals with end stage renal disease (12). Medicare beneficiaries may elect to have Fee-for-Service coverage or participate in a Health Maintenance Organization. The Medicare data resource only includes claims for those who elected Fee-for-Service health insurance. The Medicare Beneficiary Summary File was used to determine which nursing home residents elected for Fee-for-Service coverage. The MEDPAR dataset was used to identify hospitalizations that occurred between 2011 and 2018.
2.2. Study Sample
The source population from which we drew our study sample consisted of all long-term stay residents in Medicare and Medicaid certified U.S. nursing homes in 2018 who satisfied the following inclusion criteria: (1) extended stay residents with at least one quarterly or annual MDS assessment in 2018; and (2) residents with heart failure using a validated algorithm based on health claims (13), as used by others (14), and modified for nursing home context (15). In brief, using MEDPAR data between 2011–2018, we identified hospitalizations with primary heart failure related discharge ICD-9 codes of 398.91, 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428 or ICD-10 heart failure codes I50, I09.81, I11.0, I13.0, or I13.2 (Supplemental Table 1). Nursing home residents with at least one hospitalization before the index date (the date of selected annual or quarterly MDS assessment in 2018) were retained in the sample. We excluded residents in a comatose state because their care planning needs are markedly different from those not in a comatose state as well as residents experiencing short post-acute illness stays.
2.3. Operational Definition of COPD
For the present study, we used a validated health claims algorithm to operationalize COPD (16). We considered residents to have COPD if they had at least one MEDPAR claim on any diagnosis code on the claim before or on the target MDS assessment date. Because the time frame for the look back period was unspecified (16), we used all data available to the research team (2011–2018). During this period, there was a shift from ICD 9 to ICD 10 coding on October 1, 2015. Inasmuch, we used ICD9 codes 491, 492, or 496 and ICD10 codes J41, J43, or J44 to identify prevalent cases of COPD (See Supplemental Table 1).
2.4. Operational Definition of Heart Failure Type
Residents with heart failure were further classified according to their type of heart failure(14, 15): 1) heart failure with reduced ejection fraction (HFrEF), 2) heart failure with preserved ejection fraction (HFpEF), or 3) unspecified heart failure (See Supplemental Table 1). Residents were classified as having HFrEF if any hospitalizations before the index date were coded with primary discharge codes ICD-9 428.2 or 428.4 or ICD 10 codes I50.2 or I50.4. Residents with an ICD-9 code of 428.3 or ICD-10 code of I50.3 were classified as having HFpEF. Residents were coded as having unspecified heart failure based on ICD-9-CM codes: 398.91, 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.0, 428.1, or 428.9 or ICD-10 codes I50.1, I50.8, I50.9, I11.0, I13.0, or I13.2.
2.5. Covariates
Most of the data on resident’s demographic and clinical characteristics were retrieved from the MDS, and other variables which were not in the MDS were retrieved from the claims data. Demographic variables included age group (< 65 years, 65–74, 75–84, ≥85 years), sex, race/ethnicity, and clinical characteristics (e.g., cognitive impairment and activities of daily living). The race/ethnicity variable was collapsed into four categories: non-Hispanic white, non-Hispanic black, Hispanic, and others (American Indian / Native Alaskan / Native Hawaiian / Other Pacific Islander/Multiple categories) were grouped together because of the small number of survey respondents (11). We created a variable to denote those with a length of stay in nursing homes greater than one year. We chose this cut-point because it corresponds with the annual comprehensive assessment. A social connectedness index (SCI) was used for estimating the amount of social connectedness present among nursing home residents with COPD and heart failure which has been shown to be an important determinant of health and well-being among nursing home residents (17).
We included comorbid conditions that have been found to be related to COPD using a 7-day look back period. Diagnoses documented were those that required an ongoing medication or therapy, a positive test result, or procedure (11). We considered neurological conditions (i.e., Alzheimer’s disease and related dementias, stroke), nephropathy, gastroesophageal reflux disease, depression, anxiety, osteoporosis, hyperlipidemia, hypertension, anemia, obesity, cancer, cirrhosis, peripheral vascular disease, atrial fibrillation, pneumonia, coronary artery disease, and diabetes mellitus (18–20). Body mass index (BMI) was classified as ≤ 18.5 kg/m2, between ≥18.5 kg/m2 and ≤25 kg/m2, between >25 kg/m2 and <30 kg/m2, and ≥30 kg/m2. A summary measure (modified Charlson Comorbidity Index) was used to characterize the extent of disease burden.
To classify cognitive impairment, we used the Cognitive Function Scale (21) which combines the Brief Interview for Mental Status and the Cognitive Performance Scale (22). The MDS Activities of Daily Living (ADL) Self-Performance Hierarchy Scale ranges from 0 – 6. Scores between 0 – 4 indicate physical independence, whereas scores between 5 – 8 and 9–12 indicate mild and moderate dependence, respectively (23).
Additional characteristics included were whether residents were receiving hospice care, and whether residents had a life expectancy of less than 6 months as recorded in the MDS. Current tobacco use, dyspnea (with exertion, when sitting at rest, and when lying flat) during a 7-day look-back period in the MDS, and use of oxygen, which has a 14-day look-back-period in the MDS, were used to characterize patient’s symptoms and treatment to alleviate dyspnea.
2.6. Statistical Analysis
Differences in the prevalence of selected demographic and clinical characteristics were examined according to the presence or absence of COPD among nursing home residents with heart failure. Using the Poisson regression model, and having COPD as the outcome of interest, we estimated the prevalence ratios and examined the crude and multivariable adjusted associations between demographic, clinical characteristics, comorbidities, and COPD. Prior to conducting the fully adjusted regression model, we avoided multicollinearity by excluding any variable whose variance inflation factor exceeded 2.5. We found that the Charlson comorbidity index was collinear with the individual diagnoses. Therefore, we created two multivariable adjusted models: 1) one model in which all variables were included with the exception of the Charlson comorbidity index; and 2) a second model in which all variables were included except for the individual disease diagnoses to remove the potential for collinearity. To account for the clustering of residents within nursing homes, a crude and adjusted Poisson regression model with generalized estimating equations approach was utilized.
3. RESULTS
3.1. Study Sample Characteristics
Among the more than 15,000 nursing homes in the United States, our study sample included 97,495 residents diagnosed with heart failure. Overall, most of the sample population were ≥ 75 years old (74.1%), women (67.3%), and Non-Hispanic Whites (77.4%). More than one-half of this population (58.5%) had extensive limitations in their activities of daily living, 44.7% were cognitively intact, and 40.7% had a BMI ≥30 kg/m2 (Table 1).
Table 1:
Characteristics of Long Stay Residents with Heart Failure living in US nursing homes in 2018 (N = 97,495)
Characteristics | Total n = 97,495 | |
---|---|---|
Age category (years): | Percentage | |
< 65 | 7.7 | |
65–74 | 18.3 | |
75–84 | 29.5 | |
≥85 | 44.6 | |
Women | 67.3 | |
Race/Ethnicity: | Non-Hispanic White | 77.4 |
Non-Hispanic Black | 16.0 | |
Hispanic | 4.7 | |
American Indian/Native Alaskan or Hawaiian/Pacific Islander/ Multi | 1.9 | |
Activities of daily living: | ||
Independent/Supervised | 21.8 | |
Extensive limitations | 58.5 | |
Dependence | 19.7 | |
Cognitive Impairment: | Cognitively Intact | 44.7 |
Mildly Impaired | 26.5 | |
Moderately Impaired | 24.6 | |
Severely Impaired | 4.2 | |
Body mass index (kg/m2): | <18.5 | 4.6 |
18.5 to 25 | 28.9 | |
25 to <30 | 25.8 | |
≥30 | 40.7 | |
Co-Morbid Conditions: | ||
Alzheimer’s Disease | 9.1 | |
Other dementia | 38.1 | |
Coronary Artery Disease | 34.8 | |
Cerebrovascular Disease | 10.9 | |
Renal Insufficiency / End Stage Renal Disease | 31.4 | |
Hypertension | 88.0 | |
Anxiety | 32.2 | |
Osteoporosis | 10.0 | |
Hyperlipidemia | 55.4 | |
Peripheral Vascular Disease | 17.2 | |
Atrial Fibrillation | 42.8 | |
Anemia | 40.5 | |
Pneumonia | 3.4 | |
Depression | 53.0 | |
Cancer | 5.9 | |
Cirrhosis | 1.0 | |
Diabetes | 49.7 | |
Gastroesophageal Reflux Disease/ Ulcer | 44.3 | |
Chronic Obstructive Pulmonary Disease | 53.8 | |
Charlson Comorbidity Index: | 1–4 | 46.1 |
5 | 19.3 | |
6–7 | 26.3 | |
≥ 8 | 8.3 | |
Social Connectedness Index=5 (high) | 88.2 | |
Length of stay in nursing home greater than 1 year | 32.1 | |
Current tobacco user | 4.3 | |
Receiving hospice care | 5.6 | |
Life expectancy less than 6 months | 4.8 | |
Heart failure type | ||
Reduced ejection fraction | 18.8 | |
Preserved ejection fraction | 23.1 | |
Not specified | 58.1 | |
Any dyspnea | 21.6 | |
Short of breath with exertion | 14.8 | |
Short of breath sitting | 5.4 | |
Short of breath lying down | 14.4 | |
Use of oxygen | 29.9 |
Missing data: race/ethnicity (n=1,935), activities of daily living (n=23), body mass index (n=2,852), Alzheimer’s disease and dementia and pneumonia (n=11), coronary artery disease (n=13), stroke (n=9), end stage renal disease and anemia (n=14), hypertension (n=15), anxiety and depression and Gastroesophageal Reflux Disease/ Ulcer (n=12), osteoporosis (n=4), hyperlipidemia (n=29), peripheral vascular disease (n=5), atrial fibrillation (n=8), cancer (n=20), cirrhosis (n=2), Charlson comorbidity index (n=711), Social index (n=221), smoking status (n=62), hospice (n=162), life expectancy (n=38), any shortness of breath (n=47), shortness of breath with exertion (n=42), sitting (n=42), lying down (n=64), and oxygen (n=160).
Among all residents with heart failure, 53.8% also had COPD. With regards to the presence of other comorbid conditions, 88.0% had hypertension, 55.4% had hyperlipidemia, and 53.0% and 32.2% had a diagnosis of depression and anxiety respectively; approximately one-half of the residents had diabetes mellitus, and 46.1% had a Charlson Comorbidity Index between 1 – 4. Heart failure type was not specified in more than half of the residents, while 23.1% and 18.8% had HFpEF and HFrEF, respectively. Additionally, 4.3% were current smokers, 5.6% were receiving hospice care, 21.6% had dyspnea, and 29.9% were on oxygen therapy.
3.2. Prevalence of COPD According to Resident Characteristics
In examining the prevalence of COPD according to various resident characteristics (Table 2), the frequency of COPD decreased with advancing age, with increasing cognitive impairment, and was slightly higher among men than women. The prevalence of COPD was highest among those who were current tobacco users and was also markedly elevated among residents with a BMI ≥30 kg/m2, and among those with anxiety, pneumonia, and liver cirrhosis.
Table 2:
Prevalence of Chronic Obstructive Pulmonary Disease (COPD) by Characteristics of Long Stay Residents with Heart Failure Living in US Nursing Homes in 2018
Characteristics | COPD Prevalence % | |
---|---|---|
Age category (years): | <65 | 64.0 |
65–74 | 65.7 | |
75–84 | 59.2 | |
≥85 | 43.6 | |
Gender: | Women | 52.4 |
Men | 56.7 | |
Race/Ethnicity: | Non-Hispanic White | 54.1 |
Non-Hispanic Black | 53.3 | |
Hispanic | 53.2 | |
American Indian/Native Alaskan/Native Hawaiian / Pacific Islander/Multi | 45.1 | |
Activities of daily living: | ||
Independent/Supervised | 56.2 | |
Extensive limitations | 52.6 | |
Dependent | 54.6 | |
Cognitive Impairment: | Cognitively Intact | 59.2 |
Mildly Impaired | 54.0 | |
Moderately Impaired | 45.6 | |
Severely Impaired | 43.0 | |
Body mass index (kg/m2): | <18.5 | 50.9 |
18.5 to 25.0 | 48.1 | |
25 to <30 | 50.5 | |
≥30 | 60.4 | |
Co-Morbid Conditions: | Alzheimer’s Disease | 46.9 |
Other dementia | 49.1 | |
Coronary Artery Disease | 57.3 | |
Cerebrovascular Disease | 51.4 | |
Renal Insufficiency / End Stage Renal Disease | 56.1 | |
Hypertension | 53.7 | |
Anxiety | 59.6 | |
Osteoporosis | 50.0 | |
Hyperlipidemia | 55.4 | |
Peripheral Vascular Disease | 57.5 | |
Atrial Fibrillation | 54.1 | |
Anemia | 56.3 | |
Pneumonia | 66.0 | |
Depression | 57.1 | |
Cancer | 53.7 | |
Cirrhosis | 61.8 | |
Gastroesophageal Reflux Disease/ Ulcer | 58.3 | |
Diabetes Mellitus | 58.1 | |
Charlson Comorbidity Index: | 1 – 4 | 49.7 |
5 | 54.7 | |
6 – 7 | 57.7 | |
≥ 8 | 62.5 | |
Current tobacco user | 82.6 | |
Heart Failure Type: | ||
Reduced ejection fraction | 50.9 | |
Preserved ejection fraction | 54.4 | |
Not specified | 54.5 |
Missing data: race/ethnicity (n=1935), activities of daily living (n=23), body mass index (n=2,852), Alzheimer’s disease and dementia and pneumonia (n=11), coronary artery disease (n=13), stroke (n=9), end stage renal disease and anemia (n=14), hypertension (n=15), anxiety and depression and Gastroesophageal Reflux Disease/ Ulcer (n=12), osteoporosis (n=4), hyperlipidemia (n=29), peripheral vascular disease (n=5), atrial fibrillation (n=8), cancer (n=20), cirrhosis (n=2), Charlson comorbidity index (n=711), and smoking status (n=62).
3.3. Factors Associated with COPD in Nursing Home Residents with Heart Failure
COPD was less commonly diagnosed among residents aged ≥ 85 years old when compared with those aged 65 – 74 years, after adjusting for several sociodemographic, clinical characteristics, and comorbidities (Table 3). Women were less likely to be diagnosed with COPD than men, and racial/ethnic minorities were less likely to be diagnosed with COPD than non-Hispanic Whites.
Table 3.
Association between Sociodemographic, Clinical Characteristics, and Comorbid Conditions and Chronic Obstructive Pulmonary Disease (COPD) among Nursing Home Residents Living with Heart Failure
Characteristics | Crude Prevalence Ratio (95% Confidence Interval) | Adjusted Prevalence Ratio (95% Confidence Interval) | |
---|---|---|---|
Age (years): | <65 | 0.98 (0.96 – 1.00) | 0.95 (0.93 – 0.97) |
65–74 | Reference | Reference | |
75–84 | 0.90 (0.89 – 0.91) | 0.94 (0.92 – 0.95) | |
≥85+ | 0.66 (0.65 – 0.68) | 0.73 (0.72 – 0.74) | |
Gender | Men | Reference | Reference |
Women | 0.92 (0.91 – 0.94) | 0.96 (0.95 – 0.97) | |
Race/Ethnicity: | Non-Hispanic White | Reference | Reference |
Non-Hispanic Black | 0.98 (0.97 – 1.00) | 0.96 (0.94 – 0.97) | |
Hispanic | 0.99 (0.95 – 1.02) | 0.99 (0.95 – 1.02) | |
American Indian / Native Alaskan / Native Hawaiian / Other Pacific Islander/ Multi | 0.84 (0.79 – 0.88) | 0.90 (0.85 – 0.950 | |
Activities of daily living limitations: | Independent/Supervised | Reference | Reference |
Extensive limitations | 0.94 (0.92 – 0.95) | 0.98 (0.96 – 0.99) | |
Dependent | 0.97 (0.95 – 0.99) | 1.02 (1.00 – 1.04) | |
Cognitive Impairment: | Intact | Reference | Reference |
Mildly Impaired | 0.91 (0.90 – 0.93) | 0.99 (0.97 – 1.00) | |
Moderately Impaired | 0.77 (0.75 – 0.78) | 0.89 (0.88 – 0.91) | |
Severely Impaired | 0.73 (0.70 – 0.76) | 0.84 (0.81 – 0.88) | |
Body mass index (kg/m2): | <18.5 | 1.06 (1.03 – 1.09) | 1.09 (1.05 – 1.12) |
18.5 to 25.0 | Reference | Reference | |
25 to <30 | 1.05 (1.04 – 1.07) | 1.00 (0.99 – 1.02) | |
≥30 | 1.26 (1.24 – 1.28) | 1.08 (1.07 – 1.10) | |
Co-Morbid Conditions: | Alzheimer’s Disease | 0.86 (0.84 – 0.88) | 0.96 (0.94 – 0.99) |
Dementia | 0.86 (0.85 – 0.88) | 0.96 (0.95 – 0.97) | |
Coronary Artery Disease | 1.10 (1.09 – 1.11) | 1.07 (1.06 – 1.09) | |
Cerebrovascular Disease | 0.95 (0.93 – 0.97) | 0.94 (0.92 – 0.96) | |
Renal Insufficiency/End Stage Renal Disease | 1.06 (1.05 – 1.08) | 0.99 (0.97 – 1.00) | |
Hypertension | 0.97 (0.96 – 0.99) | 0.95 (0.93 – 0.97) | |
Anxiety | 1.16 (1.15 – 1.18) | 1.11 (1.10 – 1.13) | |
Osteoporosis | 0.92 (0.90 – 0.94) | 0.97 (0.94 – 0.99) | |
Hyperlipidemia | 1.07 (1.05 – 1.08) | 1.00 (0.99 – 1.01) | |
Peripheral Vascular Disease | 1.08 (1.07 – 1.10) | 1.03 (1.01 – 1.04) | |
Atrial Fibrillation | 1.01 (1.00 – 1.02) | 1.03 (1.01 – 1.04) | |
Anemia | 1.08 (1.07 – 1.09) | 1.04 (1.02 – 1.05) | |
Pneumonia | 1.24 (1.21 – 1.27) | 1.20 (1.17 – 1.23) | |
Depression | 1.14 (1.13 – 1.15) | 1.06 (1.05 – 1.07) | |
Cancer | 0.99 (0.97 – 1.02) | 0.98 (0.95 – 1.00) | |
Cirrhosis | 1.15 (1.09 – 1.21) | 1.00 (0.95 – 1.00) | |
Gastroesophageal Reflux Disease /Ulcer | 1.16 (1.14 – 1.17) | 1.11 (1.09 – 1.12) | |
Diabetes Mellitus | 1.17 (1.16 – 1.19) | 1.04 (1.03 – 1.05) | |
Charlson Comorbidity Index: | 1 – 4 | Reference | Reference |
5 | 1.10 (1.08 – 1.12) | 1.08 (1.06 – 1.10) | |
6 – 7 | 1.16 (1.14 – 1.18) | 1.11 (1.09 – 1.13) | |
≥ 8 | 1.25 (1.23 – 1.28) | 1.17 (1.15 – 1.20) | |
Heart Failure Type: | Reduced ejection fraction | 0.93 (0.92 – 0.95) | 0.93 (0.91 – 0.95) |
Preserved ejection fraction | Reference | Reference | |
Not specified | 1.00 (0.99 – 1.02) | 0.99 (0.98 – 1.01) |
On the other hand, COPD was more often diagnosed in residents who were underweight as well as those with a BMI ≥30 kg/m2, and in those diagnosed with other cardiovascular conditions, diabetes mellitus, anxiety, depression, anemia, pneumonia, and gastroesophageal reflux disease (GERD). The prevalence of COPD also increased with a higher Charlson Comorbidity Index score.
4. DISCUSSION
This study of U.S nursing home residents with heart failure showed that over half (53.8%) had COPD, with a slightly higher prevalence in men than women. The prevalence of COPD decreased significantly with advanced age and nursing home residents with several additional clinical conditions had the highest prevalence of COPD.
4.1. Prevalence of COPD among Nursing Home Residents with Heart Failure
The prevalence of COPD in patients with heart failure in the U.S varies widely ranging from between 11% to upwards of one-half (24–26). Our results showed a higher prevalence of COPD among U.S nursing home residents with heart failure than in the general U.S. population. The prevalence of COPD is likely higher among nursing home residents because of the advanced age of this population, presence of additional cardiovascular and other comorbidities predisposing to an increased risk of COPD, and long-term tobacco use (27).
4.2. Prevalence of COPD among Nursing Home Residents with Heart Failure According to Select Characteristics
a. Demographic characteristics
The Behavioral Risk Factor Surveillance System (BRFSS) data between 2011 – 2020 showed that the prevalence of COPD was higher among women than men (28). Our cross-sectional study results showed a slightly higher prevalence of COPD in men than in women. The differences between our study, which showed a decreased prevalence of COPD in those of advanced age, and others study which have shown an increased prevalence with advancing age, is likely a function of selective survival and age range included in prior work.
b. Comorbid Conditions
Among the comorbid conditions examined in our study, the prevalence of COPD was highest among residents with pneumonia (66%). Previous studies have also found COPD to be one of the most frequent comorbid conditions and a risk factor for developing pneumonia (29, 30). Residents with COPD are susceptible to pneumonia because of clinical conditions such as chronic bronchitis with persistent mucus production and presence of pathogenic bacteria in their airways which increases an inflammatory and host immune response (31). In the present study, the prevalence of HFpEF was higher than HFrEF in nursing home residents with COPD. The higher prevalence of unspecified heart failure type than HFpEF and HFrEF in our study was consistent with previous work in the US and globally (32–34).)))
Heart failure and COPD are two independent health conditions with significant morbidity and mortality, but these two conditions have similar signs and symptoms, and share the same main risk factors, most notably cigarette smoking and advanced age (35). Since both conditions are systemic disorders with overlapping pathophysiological processes, it is essential for clinicians to carefully interpret patient’s acute and chronic signs and symptoms, consider the timing of investigations in the disease trajectory, and enhance collaboration in treatment making decisions between cardiologists, pulmonologists, and primary care physicians.
Our study found a high prevalence of COPD in nursing home residents with obesity (≥30 kg/m2), underweight (<18.5 kg/m2), anxiety, peripheral vascular disease, coronary artery disease, anemia, pneumonia, diabetes mellitus, as well as among current smokers, which is consistent with previous study results (20, 26, 27, 36). Furthermore, the medical complexity of residents is underscored by the findings related to the Charlson comorbidity index. In the present study, the prevalence of COPD was higher in residents with multiple comorbid conditions who had a higher CCI score. A prior study of 91,453 patients with a COPD diagnosis between 2011 and 2015 with specific comorbid conditions from the CCI confirmed our findings of finding higher CCI scores in patients with, as compared to those without COPD, supporting the need to include comorbidities in COPD research to tailor clinical management, health services, and support (37).
4.4. Clinical Implications
In the U.S and worldwide, there is typically a higher prevalence of HFpEF found among older adults than HFrEF (38, 39). In our study sample, 58.1% of the residents did not have a specified heart failure type in their clinical documentation. This estimate was higher than that observed in a prior study of 150,959 heart failure patients admitted to 13,858 skilled nursing facilities in the US which used ICD-9 codes and the MDS which showed that 39% of nursing home residents did not have a specified heart failure type diagnosis in their clinical documentation (15). While the reasons for these differences are unknown, it is a concern as clinicians may improve the management of residents with heart failure by knowing whether they have preserved or reduced ejection fraction findings which would facilitate the use of guideline-directed therapies.
Supplemental oxygen has long been a recommended therapy for patients with COPD and heart failure, especially for patients with dyspnea and HFrEF (40). Our study results showed a lower prevalence of residents with HFrEF on oxygen therapy compared to residents who had HFpEF, a more stable form of heart failure. This could be due to current practice guidelines based on expert opinion steering away from previous consensus reports, because of the lack of justification for the routine administration of oxygen in patients with heart failure (41), as well as the possible detrimental effects of oxygen therapy on COPD and heart failure, particularly in normoxemic or mild or intermittent hypoxemic states (42).
4.5. Study Strengths and Limitations
This study focused on nursing home residents who are typically excluded from clinical research, despite being medically complex. We used a national database of nursing home residents with a high prevalence of co-occurring COPD and heart failure. We used validated algorithms based on ICD codes to define heart failure and COPD, rather than relying on self-reported diagnoses of COPD and heart failure. We also used the modified Charlson comorbidity index, an important construct for determining the resident’s disease burden for specific comorbid conditions (43). Using health care administrative claims data to identify persons with COPD has been deemed acceptable for research purposes (44). (45). (16)
Despite these strengths, several important limitations must be kept in mind when interpreting the study findings. The study lacked data on forced expiratory volume which would have been helpful in determining the forms and severity of COPD among nursing home residents (46). The MDS did not capture ejection fraction findings based on the New York Heart Association classification. The MDS does not include information on laboratory values (e.g., pulse oximetry values, complete blood count), family history (e.g., childhood exposure to COPD, history of heart failure), and genetic factors.
4.6. Conclusions
The management of older men and women with COPD and heart failure can be challenging because of possible misdiagnosis between these two chronic conditions or lack of adequate diagnosis as reflected by the high prevalence of unspecified type of heart failure observed in the present study of nursing home residents. In the nursing home setting, the management of chronically ill and functionally limited patients is highly challenging and trying to balance their quality of life with measures designed to reduce their risk of dying or developing other chronic conditions. Considering the care goals and preferences of nursing home residents is paramount given the absence of evidence on how best to treat and manage older adults in nursing homes with both COPD and heart failure.
Supplementary Material
Highlights.
Over half (53.8%) of U.S nursing home residents with heart failure had COPD
The prevalence of COPD decreased slightly with advancing age
Factors associated with COPD includes current tobacco users, obesity, anxiety, CAD
Those having COPD with a higher comorbidity index have an increased disease burden
Funding
Dr. Seun Osundolire was funded by a training grant from the National Institute of Health grants to (T32HL120823). The research was funded in part by a research grant to Dr. Lapane (R01AG071692).
Footnotes
Statement of Authorship: This author takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.
Seun Osundolire: Conceptualization, Methodology, Software, Data Curation, Investigation, Writing- Original draft preparation, Visualization, Funding Acquisition. Robert J. Goldberg: Data curation, Writing - Review & Editing. Kate L. Lapane: Software, Validation, Resources, Data Curation, Writing - Review & Editing, Supervision, Funding Acquisition.
Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Conflicts of Interest: Drs. Osundolire, Goldberg, and Lapane have no conflicts to report.
REFERENCES
- [1].Lippi G, Sanchis-Gomar F. Global epidemiology and future trends of heart failure. AME Medical Journal. 2020;5. [Google Scholar]
- [2].Kitzman DW, Rich MW. Age disparities in heart failure research. Jama. 2010;304(17):1950–1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Butler R, Fonseka S, Barclay L, Sembhi S, Wells S. The health of elderly residents in long term care institutions in New Zealand. The New Zealand medical journal. 1999;112(1099):427–9. [PubMed] [Google Scholar]
- [4].Daamen MAMJ, Hamers JPH, Gorgels APM, Brunner-La Rocca H-P, Tan FES, van Dieijen-Visser MP, et al. Heart failure in nursing home residents; a cross-sectional study to determine the prevalence and clinical characteristics. BMC Geriatrics. 2015;15(1):167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [5].Orr NM, Forman DE, De Matteis G, Gambassi G. Heart Failure Among Older Adults in Skilled Nursing Facilities: More of a Dilemma Than Many Now Realize. Current geriatrics reports. 2015;4(4):318–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [6].Zarowitz BJ, O’Shea T. Chronic obstructive pulmonary disease: prevalence, characteristics, and pharmacologic treatment in nursing home residents with cognitive impairment. Journal of managed care pharmacy : JMCP. 2012;18(8):598–606. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Saczynski JS, Lessard D, Spencer FA, Gurwitz JH, Gore JM, Yarzebski J, et al. Declining length of stay for patients hospitalized with AMI: impact on mortality and readmissions. The American journal of medicine. 2010;123(11):1007–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Garg T, Anzuoni K, Landyn V, Hajduk A, Waring S, Hanson LR, et al. The AGING Initiative experience: a call for sustained support for team science networks. Health research policy and systems. 2018;16(1):41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Ahluwalia SC, Gross CP, Chaudhry SI, Ning YM, Leo-Summers L, Van Ness PH, et al. Impact of comorbidity on mortality among older persons with advanced heart failure. Journal of general internal medicine. 2012;27(5):513–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Osundolire S, Naqvi S, Nunes AP, Lapane KL. Heart failure among US nursing home residents with diabetes mellitus. International journal of cardiology. 2022;349:138–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Cf Medicare, Services M.Long-term care facility resident assessment instrument 3.0 user’s manual accessed on May. 2015;9:2016. [Google Scholar]
- [12].Mues KE, Liede A, Liu J, Wetmore JB, Zaha R, Bradbury BD, et al. Use of the Medicare database in epidemiologic and health services research: a valuable source of real-world evidence on the older and disabled populations in the US. Clinical epidemiology. 2017;9:267–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Li Q, Glynn RJ, Dreyer NA, Liu J, Mogun H, Setoguchi S. Validity of claims-based definitions of left ventricular systolic dysfunction in Medicare patients. Pharmacoepidemiology and drug safety. 2011;20(7):700–8. [DOI] [PubMed] [Google Scholar]
- [14].Loop MS, Van Dyke MK, Chen L, Brown TM, Durant RW, Safford MM, et al. Comparison of Length of Stay, 30-Day Mortality, and 30-Day Readmission Rates in Medicare Patients With Heart Failure and With Reduced Versus Preserved Ejection Fraction. The American journal of cardiology. 2016;118(1):79–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Li L, Jesdale BM, Hume A, Gambassi G, Goldberg RJ, Lapane KL. Who are they? Patients with heart failure in American skilled nursing facilities. Journal of cardiology. 2018;71(4):428–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Gershon AS, Wang C, Guan J, Vasilevska-Ristovska J, Cicutto L, To T. Identifying individuals with physcian diagnosed COPD in health administrative databases. Copd. 2009;6(5):388–94. [DOI] [PubMed] [Google Scholar]
- [17].Bova CA, Jesdale BM, Mbrah A, Botelho L, Lapane KL. Development and psychometric evaluation of the Social Connectedness Index in nursing home residents with Alzheimer’s disease and dementia using the Minimum Data Set 3.0. International journal of geriatric psychiatry. 2021;36(7):1110–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Cavaillès A, Brinchault-Rabin G, Dixmier A, Goupil F, Gut-Gobert C, Marchand-Adam S, et al. Comorbidities of COPD. European respiratory review : an official journal of the European Respiratory Society. 2013;22(130):454–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].de Miguel Díez J, García TG, Maestu LP. [Comorbidities in COPD]. Archivos de bronconeumologia. 2010;46 Suppl 11:20–5. [DOI] [PubMed] [Google Scholar]
- [20].Panetta NL, Krachman S, Chatila WM. Chronic obstructive pulmonary disease and its comorbidities. Panminerva medica. 2009;51(2):115–23. [PubMed] [Google Scholar]
- [21].Thomas KS, Dosa D, Wysocki A, Mor V. The Minimum Data Set 3.0 Cognitive Function Scale. Medical Care. 2017;55(9):e68–e72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Chodosh J, Edelen MO, Buchanan JL, Yosef JA, Ouslander JG, Berlowitz DR, et al. Nursing Home Assessment of Cognitive Impairment: Development and Testing of a Brief Instrument of Mental Status. Journal of the American Geriatrics Society. 2008;56(11):2069–75. [DOI] [PubMed] [Google Scholar]
- [23].Morris JN, Fries BE, Morris SA. Scaling ADLs Within the MDS. The Journals of Gerontology: Series A. 1999;54(11):M546–M53. [DOI] [PubMed] [Google Scholar]
- [24].André S, Conde B, Fragoso E, Boléo-Tomé JP, Areias V, Cardoso J. COPD and Cardiovascular Disease. Pulmonology. 2019;25(3):168–76. [DOI] [PubMed] [Google Scholar]
- [25].McCullough PA, Hollander JE, Nowak RM, Storrow AB, Duc P, Omland T, et al. Uncovering heart failure in patients with a history of pulmonary disease: rationale for the early use of B-type natriuretic peptide in the emergency department. Academic emergency medicine : official journal of the Society for Academic Emergency Medicine. 2003;10(3):198–204. [DOI] [PubMed] [Google Scholar]
- [26].Mentz RJ, Schmidt PH, Kwasny MJ, Ambrosy AP, O’Connor CM, Konstam MA, et al. The Impact of Chronic Obstructive Pulmonary Disease in Patients Hospitalized for Worsening Heart Failure With Reduced Ejection Fraction: An Analysis of the EVEREST Trial. Journal of cardiac failure. 2012;18(7):515–23. [DOI] [PubMed] [Google Scholar]
- [27].Pleasants R.Chronic obstructive pulmonary disease in long-term care. Annals of Long-Term Care. 2009;17:24–30. [Google Scholar]
- [28].Prevention CfDCa. National Trends in COPD 2022. [Available from: https://www.cdc.gov/copd/data-and-statistics/national-trends.html].
- [29].Søgaard M, Madsen M, Løkke A, Hilberg O, Sørensen HT, Thomsen RW. Incidence and outcomes of patients hospitalized with COPD exacerbation with and without pneumonia. International journal of chronic obstructive pulmonary disease. 2016;11:455–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Restrepo MI, Sibila O, Anzueto A. Pneumonia in Patients with Chronic Obstructive Pulmonary Disease. Tuberculosis and respiratory diseases. 2018;81(3):187–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [31].Seemungal TA, Donaldson GC, Paul EA, Bestall JC, Jeffries DJ, Wedzicha JA. Effect of exacerbation on quality of life in patients with chronic obstructive pulmonary disease. American journal of respiratory and critical care medicine. 1998;157(5 Pt 1):1418–22. [DOI] [PubMed] [Google Scholar]
- [32].Upadhya B, Taffet GE, Cheng CP, Kitzman DW. Heart failure with preserved ejection fraction in the elderly: scope of the problem. Journal of molecular and cellular cardiology. 2015;83:73–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [33].Groenewegen A, Rutten FH, Mosterd A, Hoes AW. Epidemiology of heart failure. European journal of heart failure. 2020;22(8):1342–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34].Andersson C, Vasan RS. Epidemiology of Heart Failure with Preserved Ejection Fraction. Heart Failure Clinics. 2014;10(3):377–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [35].Horodinschi RN, Bratu OG, Dediu GN, Pantea Stoian A, Motofei I, Diaconu CC. Heart failure and chronic obstructive pulmonary disease: a review. Acta cardiologica. 2020;75(2):97–104. [DOI] [PubMed] [Google Scholar]
- [36].Barnes PJ. Sex Differences in Chronic Obstructive Pulmonary Disease Mechanisms. American journal of respiratory and critical care medicine. 2016;193(8):813–4. [DOI] [PubMed] [Google Scholar]
- [37].Shen E, Lee JS, Mularski RA, Crawford P, Go AS, Sung SH, et al. COPD Comorbidity Profiles and 2-Year Trajectory of Acute and Postacute Care Use. Chest. 2021;159(6):2233–43. [DOI] [PubMed] [Google Scholar]
- [38].Hage C, Löfgren L, Michopoulos F, Nilsson R, Davidsson P, Kumar C, et al. Metabolomic Profile in HFpEF vs HFrEF Patients. Journal of cardiac failure. 2020;26(12):1050–9. [DOI] [PubMed] [Google Scholar]
- [39].van Riet EES, Hoes AW, Wagenaar KP, Limburg A, Landman MAJ, Rutten FH. Epidemiology of heart failure: the prevalence of heart failure and ventricular dysfunction in older adults over time. A systematic review. European journal of heart failure. 2016;18(3):242–52. [DOI] [PubMed] [Google Scholar]
- [40].Sepehrvand N, Ezekowitz JA. Oxygen Therapy in Patients With Acute Heart Failure: Friend or Foe? JACC Heart failure. 2016;4(10):783–90. [DOI] [PubMed] [Google Scholar]
- [41].Sepehrvand N, Ezekowitz JA. Oxygen Therapy in Patients With Acute Heart Failure: Friend or Foe? JACC: Heart Failure. 2016;4(10):783–90. [DOI] [PubMed] [Google Scholar]
- [42].Asano R, Mathai SC, Macdonald PS, Newton PJ, Currow DC, Phillips J, et al. Oxygen use in chronic heart failure to relieve breathlessness: A systematic review. Heart Failure Reviews. 2020;25(2):195–205. [DOI] [PubMed] [Google Scholar]
- [43].Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases. 1987;40(5):373–83. [DOI] [PubMed] [Google Scholar]
- [44].Camp PG, Chaudhry M, Platt H, Roch M, Road J, Sin D, et al. The sex factor: epidemiology and management of chronic obstructive pulmonary disease in British Columbia. Can Respir J. 2008;15(8):417–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [45].Stein BD, Bautista A, Schumock GT, Lee TA, Charbeneau JT, Lauderdale DS, et al. The validity of International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes for identifying patients hospitalized for COPD exacerbations. Chest. 2012;141(1):87–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [46].Gentry S, Gentry B. Chronic Obstructive Pulmonary Disease: Diagnosis and Management. American family physician. 2017;95(7):433–41. [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.