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Vol. 59. Issue 6.
Pages 397-400 (June 2023)
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Visits
2262
Vol. 59. Issue 6.
Pages 397-400 (June 2023)
Scientific Letter
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Prevalence and Risk Factors for Airflow Limitation in Patients With Acute Coronary Syndrome
Visits
2262
Santiago Rodriguez-Segade Alonsoa,
Corresponding author
s.rodriguez-segade@udc.es

Corresponding author.
, Carmen Diego Rozaa, Raquel Marzoa Rivasb, Irene Nieto Codesidoa, Elvira Clavero Fernándezc, Félix Camiñad, Santiago Rodriguez-Segaded,e
a Pneumology Department, Hospital Arquitecto Marcide, Ferrol, Spain
b Cardiology Department, Hospital Arquitecto Marcide, Ferrol, Spain
c Internal Medicine Department, Complejo Hospitalario Universitario de A Coruña, A Coruña, Spain
d Department of Biochemistry and Molecular Biology, University of Santiago de Compostela, Santiago de Compostela, Spain
e Clinical Biochemistry Laboratory of University Hospital Complex, Santiago de Compostela, Santiago de Compostela, Spain
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Tables (2)
Table 1. Characteristics of participants with acute coronary syndrome stratified by lung function.
Table 2. Univariate and Multivariate Associations of Risk Factors for Airflow Limitation Versus Normal Lung Function in Patients with Acute Coronary Syndrome.
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To the Director,

Chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD) are considered leading causes of death worldwide.1,2 Few studies have been carried out in patients with coronary artery disease in which standardized pulmonary functional tests were performed, but have often found a high prevalence of COPD and most patients were newly diagnosed.3–6

The main objective of our study is to determine the prevalence of airflow limitation (AL) in patients with ACS and, in addition, we aim to investigate which other clinical or sociodemographic factors, apart from those already known, may be predictors of undiagnosed AL compatible with COPD in such patients.

All consecutive patients with a diagnosis of ACS, hospitalized at the Arquitecto Marcide Hospital of Ferrol from January 1, 2019 to November 17, 2021, were evaluated for inclusion in the study. At least one month after discharge (median 49 days) a post-bronchodilator spirometry was performed, according to international guidelines,1 and reviewed by an experienced pulmonologist.

Forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) were assessed with a spirometer (MasterScreen Body, Jaeger, Würzburg, Germany) and according to current international standards.7 AL compatible with a diagnosis of COPD was defined as a post-bronchodilator FEV1/FVC<0.70.8 Restricted spirometry was defined as pre-bronchodilator FEV1/FVC0.70 and FVC<80% of predicted,9 whereas combined obstructive-restricted spirometry was defined as post-bronchodilator FEV1/FVC<0.70 in combination with pre-bronchodilator FEV1/FVC0.70 and FVC<80% of predicted. Any other acceptable spirometry results were classified as normal lung function.

Patient's demographics, medical and treatment history were recorded. Data collected included the characteristic of the percutaneous coronary intervention due to SCA, smoking history, previous diagnosis of COPD, and risk factors for coronary artery disease. Patients with hemoglobin less than 12g/dl for women or 13g/dl for men were deemed to be anemic.

The research protocol was reviewed and approved by the Clinical Research Ethics Committee from Galicia, Spain (CEIC 2018/508). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.

The association between two continuous variables was tested using the Pearson correlation coefficient, and the association between two categorical or binary variables was assessed using the chi-square or Fisher exact test. Student's t-test or one-way analysis of variance (or Wilcoxon rank sum or Kruskal–Wallis tests, respectively, if normality of observations distribution could not be assumed) were used to assess the association between a categorical or binary variable and a continuous variable. The association between variables considered for abnormal lung function versus normal lung function was assessed by logistic regression models. Variables with a probability value less than 0.1 at univariate analysis and several potential confounding factors were then entered into a multivariate analysis to identify the independent predictors. Multivariate logistic regression analysis with backward elimination was conducted (or elimination threshold P>0.1) to determine the predictors of AL versus lung normal function. Variables such as age, gender, BMI, tobacco status, tobacco consumption, respiratory symptoms, comorbidities, hs-CRP, and NT-proBNP (>500pg/ml vs. ≤500pg/ml) were tested.

A total of 258 patients with acute myocardial infarction were included in this study. Clinical characteristics of patients according to lung function classification are shown in Table 1. Overall, 80.2% participants were men, the mean age was 64 years, and 37.6% were current smokers. A total of 17.1% reported having respiratory disease, including 3.9% who reported COPD. 33.3% of ACS patients presented with STEMI and 66.7% with non-STEMI. On average, patients with AL were slightly older (P=0.08), more often ex-smokers (P=0.05), and had more pack-years (P<0.001). In addition, there are significantly more cases of patients with hypertension (P=0.04), stroke (P=0.044), peripheral arterial disease (P=0.023), and anemia (P<0.0001).

Table 1.

Characteristics of participants with acute coronary syndrome stratified by lung function.

Variable  Total  Normal lung function  Airflow limitation  P-valuea  Restricted spirometry  P-valueb 
No. (%)  258  197 (76.4)  40 (15.5)    21 (8.1)   
Demographics
Age, y  64.0±11.7  63.6±11.8  66.4±8.7  .08  63.7±15.0  .95 
Male sex  80.2  79.7  90.0  .13  66.7  .17 
BMI, kg/m2  28.8±4.6  28.7±4.3  28.4±5.1  .78  30.9±5.6  .029 
Smoking history
Smoker  37.6  36  42.5  .44  42.8  .54 
Ex-smoker  35.7  34  50.0  .050  23.8  .35 
Never smoker  26.7  30  7.5  .003  33.4  .75 
Pack-years  22 (0–40)  20 (0–35)  46 (30–67)  <.001  15 (0–38)  .16 
Spirometry (post-BD)
FEV1, % predicted  101.0±21.6  107.6±16.9  79.4±24.0  <.001  79.2±15.7  <.001 
FVC, % predicted  99.6±18.2  104.8±14.4  91.0±17.9  <.001  67.9±11.4  <.001 
FEV1/FVC, %  77.4±8.5  79.9±5.2  62.5±6.8  <.001  82.4±6.6  .038 
Medical history
Hypertension  51.2  47.2  65.0  .040  61.9  .20 
Diabetes  23.3  21.8  22.5  .92  38.1  .09 
Dyslipidemia  62.0  59.4  75.0  .06  61.9  .82 
Ischemic heart disease  24.4  23.3  30.0  .37  23.8  .96 
Other cardiovascular disease  10.1  10.2  10.0  .97  9.5  .92 
Stroke  4.3  3.0  10.0  .044  4.8  .66 
Peripheral artery disease  7.0  5.1  15.0  .023  9.5  .40 
Chronic kidney disease  14.7  12.1  20.0  .19  28.6  .038 
Anemia  19.4  13.2  40.0  <.001  38.1  .003 
Charlson index  3 (2–5)  3 (2–5)  4 (3–5)  .09  4 (2–5)  .19 
Clinical presentation of ACS
STEMI  33.3  34.0  25.0  .27  42.9  .42 
Non-STEMI  45.0  43.7  50.0  .47  47.6  .73 
Unstable angina  21.7  22.3  25.0  .71  9.5  .17 
Revascularization incomplete  38.4  35.5  50.0  .09  42.9  .50 
History of lung disease
COPD  3.9  –  25.0  <.001  – 
Asthma  8.5  9.1  5.0  .39  9.5  .95 
Tuberculosis  1.6  0.5  5.0  .020  4.8  .049 
Bronchiectasis  1.2  0.5  –  .65  9.5  .001 
Lung cancer  1.2  1.0  2.5  .44  –  .51 
Other  3.9  4.6  –  .17  4.8  .97 
Respiratory medications
SABAs  7.8  3.0  32.5  <.001  4.8  .66 
LABAs  6.6  0.5  40.0  <.001  –  .75 
LABA+ICS  11.2  8.1  25.0  .002  14.3  .34 
NT-proBNP, pg/ml  253 (127–670)  218 (97–548)  446 (183–959)  .010  477 (179–1595)  .09 
NT-proBNP>500pg/ml  30.6  27.4  45.0  .027  47.6  .053 
hs-CRP, mg/l  1.2 (0.5–3.2)  1.0 (0.5–4.8)  1.6 (0.5–4.8)  .36  3.6 (1.2–8.3)  .030 

Data are presented as percentage, mean±SD, or median (interquartile range), unless otherwise indicated. ACS=acute coronary syndrome; hs-CRP=high-sensitivity C-reactive protein; ICS=inhaled corticosteroids; LABA=long acting β2-adrenoceptor agonists; NSTEMI=non-ST-elevation myocardial infarction; NT-proBNP=N-terminal pro B-type natriuretic peptide; post-BD=post-bronchodilator; SABA=short-acting β2-adrenoceptor agonist; and STEMI=ST-elevation myocardial infarction.

a

Comparison between subjects with airflow limitation versus normal lung function.

b

Comparison between subjects with restricted spirometry versus normal lung function.

In univariate analysis, AL was associated with variables such as current or ex-smokers (vs never smoking), greater smoking history, grade of dyspnea, hypertension, peripherical artery disease, anemia, and patients with NT-proBNP>500pg/ml (vs NT-proBNP500pg/ml).

Using a multivariate stepwise logistic regression model, we identified three variables as being independently associated with the presence of AL versus normal lung function: pack-years, anemia, and NT-proBNP>500pg/ml (Table 2).

Table 2.

Univariate and Multivariate Associations of Risk Factors for Airflow Limitation Versus Normal Lung Function in Patients with Acute Coronary Syndrome.

Variable  UnivariateOR (95% CI)  P-value  MultivariateOR (95% CI)  P-value 
Male sex  2.29 (0.77–6.82)  .14     
Age, yr  1.02 (0.99–1.05)  .15     
BMI, kg/m2  0.99 (0.91–1.07)  .75     
Smoking status
Never  Reference  …     
Former  5.87 (1.66–20.8)  .006  –   
Current  4.70 (1.32–16.9)  .017  –   
Pack-years  1.05 (1.03–1.06)  <.001  1.05 (1.03–1.07)  <.001 
Respiratory symptoms
Chronic cough  0.93 (0.30–2.90)  .90     
Chronic sputum  1.69 (0.69–4.10)  .25     
Wheezy  1.45 (0.50–4.21)  .50     
Dyspnea, ≥grade 2  2.18 (1.01–4.72)  .047  –   
Comorbidities
Hypertension  2.08 (1.02–4.21)  .043  –   
Diabetes  1.04 (0.46–2.35)  .93     
Dyslipidemia  2.05 (0.95–4.43)  .067  –   
Ischemic heart disease  1.41 (0.66–2.99)  .37     
Stroke  3.54 (0.95–13.2)  .060  –   
Peripheral arterial disease  3.30 (1.13–9.68)  .030  –   
Chronic kidney disease  1.80 (0.94–3.68)  .19     
Anemia  4.39 (2.06–9.33)  <.001  3.73 (1.48–9.40)  .005 
Incomplete revascularization  1.86 (0.94–3.68)  .077  –   
hs-CRP, mg/l  1.04 (0.97–1.12)  .23     
NT-proBNP, pg/ml  1.01 (0.98–1.04)  .57     
NT-proBNPa  2.24 (1.09–4.61)  .028  3.34 (1.37–8.44)  .008 

BMI=body mass index; hs-CRP=high-sensitivity C-reactive protein; and NT-proBNP=N-terminal pro B-type natriuretic peptide; (–)=not included in the model, not statistically significant in multivariate analyses.

a

Patients with NT-proBNP>500pg/ml versus ≤500pg/ml.

In the population of ACS patients studied, the prevalence of AL was 15.5%; however, if only patients with smoking history ≥10 pack-years are considered, the total prevalence raised up to 21.5% (eTable 1).

In patients without anemia (n=208), the prevalence of AL was 11.5% versus 32% in those with anemia (n=50) (P<0.001) (eTable 1). A smaller increase in cases of AL was observed among patients with NT-proBNP levels500pg/ml versus patients with levels>500pg/ml (12.0 vs. 21.5%, P=0.052).

Among the 258 patients with ACS, 40 had CODP and the percentage of undiagnosed was 75%. A relevant finding of our study is that among undiagnosed patients 36.7% had anemia, but its value increases up to 56.7% if patients without anemia having a history of smoking and NT-proBNP>500pg/ml are included.

Very few studies were performed in patients with ACS who had undergone standardized spitometry.3–6 In these studies the prevalence of AL range from 11 to 29%, which is similar to 15.5% we have found in our ACS patients, but it is higher than 10.6% found by Mooe,5 although up to 35% of his patients did not smoke against to 26.7% did not in our study.

In Campo's study4 the prevalence of AL was higher (28.5%); but patients who never smoked were excluded. In a large study including patients with stablished ischemic heart disease,10 30.5% of patients had COPD by spirometric criteria; but all participants were (ex-)smokers and had stablished ischemic heart disease and no ACS. In our study, if only patients with smoking history greater than 10 pack-years are taken into account the total prevalence of AL increases from 15.5% to 21.5%.

We also found a 75% of patients with undiagnosed AL, which is generally and consistently indicated by data from previous studies.11-13

The main independent risk factors for AL that we were able to identify in our patients were smoking history, anemia, and circulating NT-proBNP levels. The smoking history was largely confirmed in previous studies10-12,14; however, as far as we known, anemia and NT-proBNP levels have never been identified as independent risk factors, neither in the general population nor in patients with different cardiovascular diseases. The direct association between risk for AL and levels of NT-proBNP>500pg/ml in our patients is in line with the high prevalence of COPD in patients with heart failure.15

Many studies have previously described an association between anemia and COPD morbidity.16–19 In our study, the prevalence of anemia was 19.4%, and the probability of AL in patients with ACS having anemia is 3.7 times higher than patients without anemia, and it was independent of age, sex, smoking history, respiratory symptoms, comorbidities, and circulating levels of NT-proBNP or hs-CR.

Adequate detection of anemia in these patients and assessment of different treatment options is supported by our results. Indeed, each increase in hemoglobin levels by 1g/dl is associated to a 36% reduction in the prevalence of AL.

In conclusion, we observed a significant higher prevalence of chronic AL in patients with ACS than in general population and the majority of patients were undiagnosed. We also observed that, in addition to tobacco use, anemia and elevated NT-proBNP levels are important risk factors for the development of AL. We conclude that performing spirometry in those patients with ACS having these risk factors could identify up to 60% of new cases of AL. However, further studies are needed to assess whether the impact of these risk factors is generalizable to other populations.

Authors’ contributions

SR-SA conceived the study, developed the statistical analysis with SR-S assistance. Both drafted the manuscript and are the guarantors of this work, and as such, had full access to the data in the study and take responsibility for the integrity and the accuracy of the data. SR-SA, CDR, RMR, INC and ECF undertook patient visits. All authors contributed to research data and critically reviewed the manuscript for important intellectual content. SR-SA, FC and SR-S edited the final version of the manuscript and made the artwork. All Authors approved the final version of the manuscript.

Data sharing

Data are available upon reasonable request from the corresponding Author. All data relevant to the study are included in the article or uploaded as supplementary information.

Fundings

This research received no specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Conflict of interest

SR-SA declares honoraria for presentations in educational events from AstraZeneca (2021) and GSK (2021). This research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funders had no role in study design, data collection and analysis, preparation of the manuscript, or the submission process.

Acknowledgments

We gratefully to all participants in this research.

Appendix A
Supplementary data

The following are the supplementary data to this article:

References
[1]
GBD 2016 Disease and Injury Incidence and Prevalence Collaborators.
Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.
Lancet, 390 (2017), pp. 1211-1259
[2]
C.D. Mathers, D. Loncar.
Projections of global mortality and burden of disease from 2002 to 2030.
[3]
P. Almagro, A. Lapuente, J. Pareja, S. Yun, M.E. Garcia, F. Padilla, et al.
Underdiagnosis and prognosis of chronic obstructive pulmonary disease after percutaneous coronary intervention: a prospective study.
Int J Chron Obstruct Pulmon Dis, 10 (2015), pp. 1353-1361
[4]
G. Campo, R. Pavasini, C. Barbetta, E. Maietti, S. Biscaglia, F. Zaraket, et al.
Predischarge screening for chronic obstructive pulmonary disease in patients with acute coronary syndrome and smoking history.
Int J Cardiol, 222 (2016), pp. 806-812
[5]
T. Mooe, N. Stenfors.
The prevalence of COPD in individuals with acute coronary syndrome: a spirometry-based screening study.
J Chronic Obstr Pulm Dis, 12 (2015), pp. 453-461
[6]
M. Dreher, A. Daher, A. Keszei, N. Marx, T. Müller, C. Cornelissen, et al.
Whole-body plethysmography and blood gas analysis in patients with acute myocardial infarction undergoing percutaneous coronary intervention.
Respiration, 97 (2019), pp. 24-33
[7]
M.R. Miller, J. Hankinson, V. Brusasco, F. Burgos, R. Casaburi, A. Coates, et al.
Standardisation of spirometry.
Eur Respir J, 26 (2005), pp. 319-338
[8]
J. Vestbo, S.S. Hurd, A.G. Agusti, P.W. Jones, C. Vogelmeier, A. Anzueto, et al.
Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary.
Am J Respir Crit Care Med., 187 (2013), pp. 347-365
[9]
P. Enright, W.M. Vollmer, B. Lamprecht, R. Jensen, A. Jithoo, W. Tan, et al.
Quality of spirometry tests performed by 9893 adults in 14 countries: the BOLD study.
Respir Med., 105 (2011), pp. 1507-1515
[10]
F.M. Franssen, J.B. Soriano, N. Roche, P.H. Bloomfield, G. Brusselle, L.M. Fabbi, et al.
Lung function abnormalities in smokers with ischemic heart disease.
Am J Respir Crit Care Med, 194 (2016), pp. 568-576
[11]
C.H. Martinez, D.M. Mannino, F.A. Jaimes, J.L. Curtis, M.K. Han, N.N. et al. Hansel.
Undiagnosed obstructive lung disease in the United States: associated factors and long-term mortality.
Ann Am Thorac Soc., 12 (2015), pp. 1788-1795
[12]
A.S. Gershon, D. Thiruchelvam, K.R. Chapman, S.D. Aaron, M.B. Stanbrook, J. Bourbeau, et al.
Health services burden of undiagnosed and overdiagnosed COPD.
Chest, 153 (2018), pp. 1336-1346
[13]
J.B. Soriano, I. Alfageme, M. Miravitlles, P. de Lucas, J.J. Soler-Cataluña, F. García-Rio, et al.
Prevalence and determinants of COPD in Spain: EPISCAN II.
Arch Bronconeumol, 57 (2021), pp. 61-69
[14]
M.C. Delmas, L. Benezet, C. Ribet, Y. Iwatsubo, M. Zins, R. Nadif, et al.
Underdiagnosis of obstructive lung disease: findings from the French CONSTANCES cohort.
BMC Pulm Med, 21 (2021), pp. 319-329
[15]
K. Deskmukh, A. Khanna.
Implications of managing chronic obstructive pulmonary disease in cardiovascular diseases.
Tuberc Respir Dis, 84 (2021), pp. 35-45
[16]
M. Ferrari, L. Manea, K. Anton, P. Bruzzone, M. Meneghello, F. Zamboni, et al.
Anemia and hemoglobin serum levels are associated with exercise capacity and quality of life in chronic obstructive pulmonary disease.
BMC Pulm Med, 15 (2015), pp. 58
[17]
F. Kollert, A. Tippelt, C. Müller, R.A. Jörres, C. Porzelius, M. Pfeifer, et al.
Hemoglobin levels above anemia thresholds are maximally predictive for long-term survival in COPD with chronic respiratory failure.
Respir Care, 58 (2013), pp. 1204-1212
[18]
A.P. Toft-Petersen, C. Torp-Pedersen, U.M. Weinreich, B.S. Rasmussen.
Association between hemoglobin and prognosis in patients admitted to hospital for COPD.
Int J Chron Obstruct Pulmon Dis, 11 (2016), pp. 2813-2820
[19]
A. Pizzini, M. Aichner, T. Sonnweber, I. Tancevski, G. Weiss, J. Löffler-Ragg.
The significance of iron deficiency and anemia in a real-life COPD cohort.
Int J Med Sci, 17 (2020), pp. 2232-2239
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