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Interstitial lung disease
A Step towards understanding coronary artery disease: a complication in idiopathic pulmonary fibrosis
  1. Rishav Sinha1,
  2. Dhairya Nanavaty1,
  3. Arij Azhar2,
  4. Pradeep Devarakonda3,
  5. Sohrab Singh3,
  6. Rupa Garikipati4,
  7. Ankushi Sanghvi5,
  8. Suganya Manoharan3,
  9. Gaurav Parhar2,
  10. Kiran Zaman2,
  11. Cesar Ayala-Rodriguez3,
  12. Viswanath Vasudevan1,
  13. Sarath Reddy3 and
  14. Louis Gerolemou2
  1. 1Internal Medicine, Brooklyn Hospital Center, Brooklyn, New York, USA
  2. 2Pulmonary/Critical Care Medicine, Brooklyn Hospital Center, Brooklyn, New York, USA
  3. 3Cardiology, Brooklyn Hospital Center, Brooklyn, New York, USA
  4. 4Pediatrics, Cooper University Health Care, Camden, New Jersey, USA
  5. 5Internal Medicine, Saint Vincent Hospital, Worcester, Massachusetts, USA
  1. Correspondence to Dr Rishav Sinha; rishav1911{at}gmail.com

Abstract

Background Idiopathic pulmonary fibrosis (IPF) is a relatively rare disease with increasing incidence trends. Cardiovascular disease is a significant complication in IPF patients due to the role of common proatherogenic immune mediators. The prevalence of coronary artery disease (CAD) in IPF and the association between these distinct pathologies with overlapping pathophysiology remain less studied.

Research question We hypothesised that IPF is an independent risk factor for CAD.

Methods We conducted a retrospective case–control study using the national inpatient sample (2017–2019). We included adult hospitalisations with IPF after excluding other interstitial lung diseases and other endpoints of CAD, acute coronary syndrome and old myocardial infarction. We examined their baseline characteristics, such as demographic data, hospital characteristics and socioeconomic status. The prevalence of cardiac risk factors and CAD was also compared between hospitalisations with and without IPF. Univariate and multivariate regression analysis was further performed to study the odds of CAD with IPF. The cases of IPF in the study population were propensity-matched, after which generalised linear modelling analysis was performed to validate the findings.

Results A total of 116 010 admissions were hospitalised in 2017–2019 with IPF, of which 55.6% were men with a mean age of 73 years. Adult hospitalisations with IPF were found to have a higher prevalence of diabetes mellitus (29.3% vs 24.0%; p<0.001), hypertension (35.6% vs 33.8%; p<0.001), hyperlipidaemia (47.7% vs 30.2%; p<0.0001) and tobacco abuse (41.7% vs 20.9%; p<0.001), while they had a lower prevalence of obesity (11.7% vs 15.3%; p<0.0001) compared with hospitalisations without IPF. Multivariate logistic regression analysis revealed 28% higher odds of developing CAD in IPF hospitalisations (OR −1.28; CI 1.22 to 1.33; p<0.001). Postpropensity matching, generalised linear modelling analysis revealed even higher odds of CAD with IPF (OR −1.77; CI 1.54 to 2.02; p<0.001)

Conclusions Our study found a higher prevalence of CAD in IPF hospitalisations and significantly higher odds of CAD among IPF cases. IPF remains a terminal lung disease that portends a poor prognosis, but addressing the cardiovascular risk factors in these patients can help reduce the case fatality rate due to the latter and potentially add to quality-adjusted life years.

  • interstitial fibrosis
  • systemic disease and lungs
  • tobacco and the lung
  • inflammation

Data availability statement

Data are available in a public, open access repository. All data relevant to the study are included in the article or uploaded as supplementary information.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjresp-2023-001834

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    WHAT IS KNOWN ABOUT THIS TOPIC

    • The common pathologic processes underlying fibrotic lung disorders and atherosclerosis have been studied. The traditional risk factors for coronary artery disease (CAD), including smoking status, overlap with idiopathic pulmonary fibrosis (IPF).

    WHAT THIS STUDY ADDS

    • We conducted this retrospective study from a nationwide inpatient sample to investigate the risk of CAD in patients hospitalised with IPF. To the best of our knowledge, there is limited literature utilising a large database to study this association.

    HOW MIGHT THIS STUDY AFFECT RESEARCH, PRACTICE AND POLICY

    • Our study identifies an association between CAD and IPF. This should encourage clinical practice guidelines formulation to evaluate for CAD in IPF patients. Our brief review on the potential role of cardiopulmonary exercise testing and novel therapeutic agents also incites further research in this domain to better manage CAD in IPF patients.

    Introduction

    Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive and fibrosing interstitial lung disease (ILD) of undetermined aetiology. IPF occurs primarily in older adults, is limited to the lungs and is associated with histopathologic and/or radiologic patterns of usual interstitial pneumonia.1 It foreshadows a poor prognosis, with a mean survival of 2–5 years from the age of diagnosis.2

    IPF is the most common type of idiopathic interstitial pneumonia.3 Its prevalence is estimated to be between 14 and 63 cases per 100 000 population in the USA and between 1.25 and 23.4 cases per 100 000 population in Europe.4 The prevalence and incidence of IPF are likely underestimated due to the low index of suspicion and misdiagnosis. Despite this, the incidence of IPF has been on the rise.5

    Prior literature has demonstrated an increased prevalence of coronary artery disease (CAD) in patients with IPF, with up to 68% of patients inflicted with both conditions.6 7 Ischaemic heart disease (IHD) and its sequelae are among the most common causes of death in patients with IPF.8 Common pathophysiological pathways are likely responsible for this association. Despite the higher morbidity and mortality associated with the coexistence of these diseases, there are limited recommendations suggesting the management of CAD in patients with pre-existing IPF. Our study aims to study the association between IPF and CAD.

    Study methodology

    Overview of study design

    The national inpatient sample (NIS) for 2017–2019 was queried to conduct this retrospective case–control study. NIS is a deidentified database consisting of information pertaining to hospitalisations from states participating in the Healthcare Cost and Utilization Project. It is a stratified sample of hospital discharges based on key hospital characteristics that include bed size, teaching status, urban/rural location and ownership. Every year, it provides information on roughly 8 million hospitalisations from around 1000 hospitals, which correlate to around 20% of hospitalisations in the entire USA. NIS also provides discharge weights, which can be used to extrapolate data to calculate expected national hospitalisation rates. This database is publicly available and updated every year.

    The NIS data are coded using International Classification of Disease-Tenth Edition-Clinical Modification (ICD-10-CM) codes. These were used to identify all the different diagnoses (online supplemental e-table 1). Inclusion criteria included all adult hospitalisations (age>18 years) with a diagnosis of IPF from 2017 to 2019. Hospitalisations with other ILD and end points of CAD, including acute coronary syndrome (ACS) (unstable angina and acute myocardial infarction), were excluded. Since IPF is a diagnosis of exclusion, hospitalisations with other ILDs were excluded to account for coding errors. Complications of CAD, which include old myocardial infarction or ACS, were excluded since the aim of the study was to study the association of IPF and CAD before these complications develop.

    Definition of variables

    The outcome of our study, ‘CAD’ was defined as atherosclerotic heart disease of the native coronary artery without angina pectoris ‘I2510’, atherosclerotic heart disease of the native coronary artery with unstable angina pectoris ‘I25110’, atherosclerotic heart disease of native coronary artery with angina pectoris with documented spasm ‘I25111’, atherosclerotic heart disease of native coronary artery with other forms of angina pectoris ‘I25118’, atherosclerotic heart disease of the native coronary artery with unspecified angina pectoris ‘I25119’, ischaemic cardiomyopathy ‘I255’, coronary atherosclerosis due to lipid rich plaque ‘I2583’ and coronary atherosclerosis due to calcified coronary lesions.

    The prevalence of CAD, along with other baseline cardiac risk factors, including diabetes mellitus (DM), hypertension (HTN), hyperlipidemia (HLD), obesity, and tobacco abuse, was compared between adult hospitalisations with and without IPF. Obesity has been defined as body mass index >30 for coding purposes. Morbid (severe) obesity due to excess calories ‘E6601’, other obesity due to excess calories ‘E6609’, other obesity ‘E668’ and obesity, unspecified ‘E669’ were the codes used for the purpose of this study. All other comorbidities were identified using ICD-10-CM codes in accordance with NIS data elements. (online supplemental e-table 1).

    Statistical analysis

    STATA/MP V.17.0 software was used for statistical analysis. P values were calculated using the χ2 test for categorical variables and the paired t-test for continuous variables. These tests estimated the demographics (age, sex, race), socioeconomic status (based off income and zip code), type of insurance, hospital characteristics (bed size, location/teaching status and region) and prevalence of cardiac risk factors between hospitalisations with and without IPF. Univariate logistic regression and multivariate logistic regression were used to calculate the odds of developing CAD in adult subpopulation with IPF. Multivariate regression was performed after adjusting for potential confounding variables such as age, sex, race and other cardiac risk factors, as mentioned in the result section. P values <0.05 were taken to be statistically significant.

    Generalised linear regression analysis was later performed to validate these findings after propensity matching cases with controls. We first generated propensity scores, which were used to match hospitalisations with and without IPF. To achieve this, a non-parsimonious multivariate logistic regression model was developed to estimate the propensity scores using the variables mentioned in the results section. The double robust method was then used to generate treatment weights. Furthermore, the inverse probability of treatment weighting was used to match cases with controls using generalised linear methods.

    IRB approval

    Although the protocol was submitted to Institutional Review Board (IRB), the decision of approval was waived since the database has no patient-related information. Since this is a deidentified database, no patients or the public were used in the design or planning of this project.

    Patient and public involvement

    This study did not involve any patients or members of the public.

    Results

    Demographics

    A total of 105 600 adult hospital admissions were identified with a diagnosis of IPF in 2017–2019 after excluding the admissions mentioned in the exclusion criteria. In terms of demographics, 55.6% were men, with a mean age of 73 years. Adult hospitalisations with IPF had a higher proportion of whites as compared with hospitalisations without IPF (76.4% vs 66.9%; p<0.001). The hospitalisations for IPF had a fairly uniform distribution as far as the income quartiles of the subjects are concerned, while hospitalisations without IPF had a higher proportion belonging to the lower socioeconomic strata (30.4% vs 23.6% in the 0–25th quartile of the income range; p<0.001). Medicare reimbursements were much higher in adult hospitalisations with IPF than those without (81.1% vs 47.0%; p<0.001). The southern part of the USA encountered the maximum number of hospitalisations with a diagnosis of IPF (39.5%), followed by the Midwest (22.3%), Western (19.5%) and northeast regions (18.7%) (table 1).

    View this table:
    Table 1

    Demographic characteristics in adult patients with and without IPF

    Subgroup analysis: prevalence of cardiac risk factors compared with the general population.

    In the subpopulation of adults (age >18 years), hospitalisations with IPF were found to have a higher prevalence of DM (29.3% vs 24.0%; p<0.001), HTN (35.6% vs 33.8%; p<0.001), HLD (47.7% vs 30.2%; p<0.001) and tobacco abuse (41.7% vs 20.9%; p<0.001) compared to hospitalisations without IPF. Obesity was slightly less prevalent in this population (11.7% vs 15.3%; p<0.0001) (table 2, figure 1). Adult hospitalisations with IPF were found to have a significantly higher prevalence of CAD as compared with those without IPF (32.5% vs 15.8% (p<0.001) (table 2).

    View this table:
    Table 2

    Risk factor comparison and prevalence of CAD in adult patients with and without IPF

    Figure 1
    Figure 1

    Cardiac risk factor comparison in adult hospitalisations with and without IPF. IPF, idiopathic pulmonary fibrosis.

    Univariate regression analysis

    Unadjusted univariate analysis revealed higher odds of CAD with age, DM, HTN, HLD, tobacco abuse and obesity (table 3). The unadjusted odds ratio of CAD with IPF was also significantly higher (OR−3.04; CI 2.45 to 3.75; p<0.0001) (table 3).

    View this table:
    Table 3

    Univariate regression analysis demonstrating odds of CAD with cardiac risk factors and IPF

    Multivariate regression and generalised linear modelling analysis:

    After adjusting for demographics (age, sex, race) and clinical risk factors (DM, HTN, HLD, obesity and smoking) as mentioned in table 1, multivariate logistic regression analysis revealed 28% higher odds of developing CAD in IPF hospitalisations (OR −1.28; CI 1.22 - 1.33; p<0.001) (table 4). For further validation of these findings, the generalised linear modelling analysis performed after propensity matching revealed much higher odds of CAD with IPF (OR −1.77; CI 1.54 - 2.02; p<0.001) (table 4).

    View this table:
    Table 4

    OR of CAD with IPF before and after propensity analysis

    Discussion

    IPF is a chronic, progressive fibrotic lung disease. A combination of environmental9 and genetic triggers10 initiates the dysregulation of alveolar epithelial cells, which secrete numerous fibrogenic growth factors and cytokines. These mediators alter the extracellular matrix further, disrupting alveolar gas exchange.11 The injured lung parenchyma undergoes dysregulated repair, resulting in reduced lung compliance and respiratory failure, leading to mortality.12

    We conducted a retrospective case–control study using the national inpatient database to investigate the association between IPF and CAD. Although scarce, prior literature has demonstrated similar findings. In a previous study involving the UK population, Clarson et al described that ILD was independently associated with IHD with a hazard ratio of 1.85 (CI 1.56 to 2.18; p<0.05).4 In a cross-sectional study, fibrotic lung diseases were associated with a higher prevalence of CAD (OR, 2.18; 95% CI 1.17–4.06), specifically when the multivessel disease was analysed (OR, 4.16; 95% CI 1.46–11.9). This was studied by Kizer et al in patients who were already referred for lung transplantation.13

    Nathan et al investigated the prevalence and impact of CAD in IPF patients and compared it to chronic obstructive pulmonary disease (COPD) patients as controls in patients with advanced lung diseases being evaluated for a lung transplant. The subjects in both groups had similar demographics; the COPD group had higher smoking rates. Diagnosis of IPF was predictive of CAD status after adjustment (OR: 1.67; 95% CI 0.59 to 4.78) for typical risk factors. However, this study was limited because it was done on patients with a smaller sample size.6

    Pathophysiology

    Proatherogenic immune mediators are hypothesised to be the main culprits in driving morbidity in patients with IPF and concomitant CAD. However, the pathophysiological role of inflammation as a driver of IPF has been controversial.14 Kizer et al described interleukins IL-4, IL-8, Tumor Necrosis Factor (TNF)-alpha and IL-13 elevation in IPF patients.13 While IL-4 and TNF- alpha upregulate cell adhesion molecules15–17 that cause leucocyte accumulation in vascular intima,18 IL-13 along with IL-4 upregulates lipoxygenases, resulting in accumulation of oxidised low-density lipoprotein.19 These immune mediators are described as potential culprits for atherosclerosis.13

    The role of inflammation in IPF has been identified in biologic samples procured during exacerbations of the disease and from patients with stable disease. Increased influx of inflammatory cells seen in bronchoalveolar fluid and cytokines found in lung biopsies from patients with active IPF exacerbation suggest inflammation likely plays a role in disease genesis and progression. However, recent studies indicate that the underlying pathophysiology of IPF results from fibroblast dysfunction rather than dysregulated inflammation.14

    Despite evidence of inflammation being a common trigger, steroids have lacked efficacy in treating IPF. However, steroid use was found to be an independent risk factor for heart disease.20 The role of statins remains well established in treating CAD. Interestingly, a prospective study by Videl-Krogh et al suggested a mortality benefit with statins in patients with ILD, including IPF.21

    Ischaemia within the subendocardium via disruption of oxygen delivery has been well described by Nyhan and Schulthesis22 proposed mechanisms include both limitations in oxygen-carrying capacity (anaemia, hypoxia) and decreased cardiac output. Given the impaired ability of the lungs to oxygenate haemoglobin, one would expect both secondary polycythemia and increased cardiac output as a compensatory mechanism to preserve oxygen delivery to vital organs. However, increased cardiac output through an increase in heart rate may result in decreased transit time of red blood cell (RBC) in pulmonary circulation, resulting in oxygen becoming a diffusion-impaired molecule, as opposed to a perfusion-impaired one. Patients with IPF also have an increased expression of hypoxia-inducible factors, which is a crucial regulator of cardiac metabolism and angiogenesis, especially during myocardial ischaemia.23 In addition, patients with IPF may have chronic underlying inflammatory processes, resulting in inadequate erythropoietin response, resulting in anaemia of chronic disease.24

    Further investigation is required regarding the haemoglobin target for patients with ACS who have concomitant IPF, given their oxygen-carrying capacity is impaired.

    To determine whether the patients are sufficiently perfusing their myocardium, non-invasive modalities such as cardiopulmonary exercise testing (CPET) or coronary computerised tomography angiography (CTA) may be explored. Cicchitto et al suggested the role of CPET in identifying factors responsible for higher ischaemic risk. CPET can help attribute the cause of dyspnoea to the heart, the lung or a combination of both. It also provides insight into whether or not these patients are a candidate for a lung transplant.25 Nathan et al have demonstrated the utility of CTA to look for coronary calcifications to predict further cardiac workup requirements in patients with IPF.26

    CPET and CTA can be used for CAD case finding in IPF patients, but these costly diagnostic modalities are not ubiquitously available. Physicians typically have a very high threshold of searching for alternative causes of dyspnoea in patients with advanced lung diseases. A population-based study using a health improvement network by Hubbard et al demonstrated that patients with IPF were less likely to be on beta-blockers and statins, although they had a higher chance of ACS.10

    The prognosis in advanced IPF is up to 5 years,2 and CAD complications pose a high burden of morbidity and mortality.6 It is imperative to note that cardiac-related deaths rank second in the list of common causes of death in patients with IPF.27 28 Hence, it is crucial that the diagnostic threshold for CAD in IPF patients should be set low. CAD risk factors should also be adequately optimised to improve outcomes in IPF patients.

    Conclusion/future directions

    Our study concludes that IPF is associated with CAD independently of other causative risk factors. Diagnosing IPF can be challenging in its initial stages, as dyspnoea has a broad differential. Advanced diagnostics, namely CPET and CTA, may be integrated to assess CAD in susceptible individuals. IPF patients should be medically optimised for reversible risk factors (eg, anaemia, hypoxia) to prevent CAD development and/or progression. Further research should be done for therapeutic modalities specifically targeting common pathophysiologic mediators of CAD and IPF. In addition, the role of statins in preventing IPF progression should be further studied. IPF remains a terminal lung disease that ultimately carries a high mortality, but addressing the cardiovascular risk factors in these patients can help reduce the case fatality rate due to the latter and potentially add quality-adjusted life years in the affected population.

    Limitations

    Our study carries a few limitations. The NIS is a retrospective coding database that does not contain granular-level data (eg, patient lab values, imaging and others). Since there is no ICD-10 code for a family history of CAD, it was not possible to adjust for that traditional risk factor for CAD. Coding errors in these studies can introduce bias that cannot be fully addressed. We used propensity matching to enhance the accuracy of our findings. Due to similar reasons, the quantitative degree of risk factors (eg, LDL levels in patients with HLD, HA1c level in patients with diabetes, etc) could not be measured or adjusted for. Retrospective studies can establish an association between variables, but they cannot prove causation. Hence, further prospective studies are required to identify the extent to which IPF is a causative risk factor for the development and/or progression of CAD. Despite these limitations, the power of the study is quite significant (n=1 05 600).

    Generalisability

    Using a nationally representative population, we sought to investigate this important clinical association, if identified early, would help prevent patient-related cardiovascular outcomes.

    Data availability statement

    Data are available in a public, open access repository. All data relevant to the study are included in the article or uploaded as supplementary information.

    Ethics statements

    Patient consent for publication

    Acknowledgments

    Guarantor (RS): The corresponding author, along with all listed co-authors, is responsible for all the content of the manuscript.

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    Supplementary materials

    • Supplementary Data

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    Footnotes

    • Contributors RS and DN contributed equally to this paper. All other authors have contributed substantially in conceptualising, data analysis, writing, editing and proofreading of the manuscript.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer-reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.