Background Insights on the differences in clinical outcomes, quality of life (QoL) and health resource utilisation (HRU) with different levels of care available to post-acute myocardial infarction (AMI) populations in rural and urban settings are limited.
Methods The long-Term rIsk, clinical manaGement, and healthcare Resource utilisation of stable coronary artery dISease (TIGRIS), a prospective, observational registry, enrolled 8452 patients aged ≥50 years 1–3 years post-AMI from June 2013 to November 2014 from 24 countries in Asia Pacific/Australia, Europe, North America and South America. Differences in QoL (measured using the EuroQol Research Foundation instrument) and HRU between patients in rural and urban settings were evaluated in this post hoc analysis. The incidence of clinical endpoints (cardiovascular (CV) death, AMI, unstable angina with urgent revascularisation and stroke; bleeding; and all-cause mortality) was analysed. Data were collected at baseline and every 6 months for 24 months.
Results There were fewer hospitalisations and visits to general practitioners (GPs) and cardiologists in the rural versus urban populations (adjusted event rate ratio (ERR)=0.90 (95% CI, 0.82 to 1.00, p=0.04); ERR=0.84 (95% CI, 0.78 to 0.92, p<0.001); ERR=0.86 (95% CI, 0.81 to 0.92, p<0.001), respectively). No statistically significant differences were observed between rural and urban populations in all-cause death, AMI, unstable angina with urgent revascularisation, CV death, stroke, major bleeding events and health-related QoL. The adjusted incidence rate ratio was 0.92 (95% CI, 0.74 to 1.15) for the composite of CV death, AMI and stroke.
Conclusions Living in rural areas was associated with fewer GP/cardiologist visits and hospitalisations; no significant differences in clinical outcomes and QoL were observed.
Trial registration number NCT01866904.
- coronary artery disease
- myocardial infarction
- quality of health care
Data availability statement
Data are available upon reasonable request. The data supporting the findings of this study may be obtained upon reasonable request and in accordance with AstraZeneca’s data sharing policy described at https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure.
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/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
Patients living in rural areas receive less good evidenced-based medical therapy after acute myocardial infarction (AMI), and countries with differing socioeconomic status and healthcare systems have variable long-term mortality and health resource utilisation (HRU) post-AMI.
Data on differences in long-term mortality and HRU with different levels of care available to post-AMI populations in rural versus urban settings are limited.
WHAT THIS STUDY ADDS
Data from TIGRIS, a multinational, multicentre, prospective observational study, were used to evaluate the long-term influence of living in rural or urban areas in stable patients post-AMI.
Fewer hospitalisations and outpatient visits to general practitioners and cardiologists were observed in the rural population compared with the urban population.
However, no significant differences were observed in the clinical outcomes and quality of life in patients from rural versus urban areas.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Clinicians managing patients following AMI should recognise that there are differences in HRU between patients living in rural and urban areas although ultimately this does not appear to have a significant impact on clinical outcomes and quality of life.
Moreover, these findings may influence public and private health policies regarding the population living in rural areas.
The comparison between populations with coronary artery disease (CAD), specifically those with acute myocardial infarction (AMI), living in rural or urban areas has been the main objective of some previous publications.1–4 The awareness and management of risk factors is suboptimal in rural areas compared with urban areas, especially among low-income to low-middle–income countries.5 6 Moreover, the median response and transport times for AMI have been longer in rural areas than in urban areas,7 and the use of effective therapies for secondary prevention has been lower in the former compared with the latter.8
The impact of these differences on mortality remains controversial, with conflicting reports published from countries with varying socioeconomic realities and healthcare systems. In a study analysing patients with AMI in Nebraska, USA, the authors reported significant rural–urban disparities in 30-day mortality.1 In contrast, a similar study conducted in Ontario, Canada, demonstrated similarity in outcomes, including mortality, between rural and urban settings, despite variations in processes of care and access to health services.9 Notably, an analysis of mortality trend in North America across four decades found consistently higher mortality rates in individuals living in non-metropolitan areas, specifically those with increasing levels of rurality.10 Furthermore, differences in patients’ health-related quality of life (HRQoL) and health resource utilisation (HRU) in rural versus urban areas in the long-term after AMI are not well described. From a healthcare system perspective, knowing where to allocate resources and hospital facilities as well as their potential impact on patient-centric outcomes for those living in rural and urban areas are of fundamental importance.
Therefore, we undertook a post hoc subgroup analysis from the multicentre, multinational, long-Term rIsk, clinical manaGement, and healthcare Resource utilisation of stable coronary artery dISease (TIGRIS) registry in post-AMI patients.11–13 The aim of the present study was to analyse differences between individuals with previous AMI (1–3 years) living in rural versus urban areas in a broad population from 24 countries by considering their clinical outcomes, HRU and HRQoL in a follow-up of 2 years after inclusion in the registry.
TIGRIS (ClinicalTrials.gov, NCT01866904) enrolment criteria as well as patient characteristics, treatment patterns and clinical outcomes have been published.11–13 TIGRIS was a prospective, observational study that enrolled 8452 patients from June 2013 to November 2014 from 24 countries in Asia Pacific/Australia, Europe, North America and South America. A list of places of residence by region and country and a list of principal investigators for the TIGRIS study is provided in online supplemental tables 1 and 2 in the online supplemental material.
Patients and the public were not involved in the research process. Briefly, we included patients aged ≥50 years with a documented history of a prior myocardial infarction and at least one of the following risk factors: age ≥65 years, documented history of a second AMI >1 year before study enrolment, multivessel CAD, creatinine clearance ≥15 mL/min and <60 mL/min or diabetes mellitus requiring medication. The main exclusion criteria were any condition or circumstance that could limit complete follow-up of the patient (eg, life expectancy of <1 year, psychiatric disturbances or alcohol or drug abuse); current participation in a blinded randomised controlled trial; and treatment with ticagrelor beyond 12 months after AMI or the off-label use of ticagrelor.
This study did not utilise any specific definition for rural place of residence. Instead, rural or urban place of living was prospectively identified in the baseline case report form, as reported by the investigator following what was recalled from the patient.
Data were collected during the initial visit and every 6 months thereafter for 24 months by telephone or in person. The European Quality of Life (EuroQol) Research Foundation survey instrument for measuring self-reported health status in five dimensions (EQ-5D; mobility, self-care, usual activities, pain/discomfort and anxiety/depression) with three levels of severity (EQ-5D-3L; none, moderate and severe) was completed by every individual at each visit.14 15 HRU comprised hospitalisations (number and total length, considered total hospitalisations, hospitalisations for cardiovascular (CV) causes and hospitalisations for bleeding), plus the number of visits to emergency departments, general practitioners (GPs), cardiologists and other specialists.
In the present study, 8186 patients (97% of the total TIGRIS population) had complete data regarding the place of residence and HRQoL and were included in the current analysis. Data on HRU were available for 7081 patients. The main objective was to explore the long-term HRU and associated outcomes and HRQoL in stable individuals with an AMI 1–3 years prior to enrolment. We also analysed the incidence of clinical endpoints, including the composite of CV death, reinfarction, unstable angina requiring urgent revascularisation and stroke (and its individual components); bleeding; and all-cause mortality among patients living in rural versus urban areas. A total of 9027 patients (97.9% from the study population) provided the first-year follow-up data, and during the 2-year follow-up period, a further 292 (3.2%) patients withdrew or were lost to follow-up. All endpoints were confirmed by the treating physician or hospital, including determination of final diagnosis, primary cause of hospitalisation, duration of hospital stay, procedures and interventions. If death occurred, efforts were made to identify the cause (CV related or non-CV) based on the available death certificate or through relatives, physicians or hospitals.11
Comparisons of baseline characteristics by place of residence (urban vs rural) were summarised by mean and SD for quantitative variables and by frequency and percentage for categorical variables. P values were based on a two-sample t-test, a χ2 test and a test for trend for quantitative, binary and ordinal variables, respectively.
The number of hospitalisations for any cause, CV cause and bleeding cause and the number of emergency room (ER), GP, cardiologist and other specialist visits over the 2-year follow-up period were summarised in 7081 patients with HRU data at every visit and compared by urban versus rural places of residence using two-sample t-tests. Unadjusted and adjusted relative comparisons of event rate per time unit (using all defined events irrespective of some subjects contributing with multiple events) were also made using univariable and multivariable negative binomial models (estimating event rate ratio (ERR)),16 adjusting for predictors of HRU selected using a forward stepwise selection procedure with a criterion of p<0.01. The denominator used was person-years follow-up; this was defined as the time from enrolment into the study until the end of follow-up (earliest among loss to follow-up, end of follow-up (2 years) or death).
The 2-year cumulative incidence (risk %) of each clinical outcome was calculated by place of residence using Kaplan-Meier estimates to account for percentage of patients who were lost to follow-up, and univariable comparisons were made using log-rank tests. Multivariable Poisson regression models,17 using only the subject’s first defined event, were used to calculate the adjusted incidence rate ratios (IRRs) for the composite outcomes of CV death, AMI and stroke and CV death, AMI, stroke and major bleeding and for all-cause death. The covariates simultaneously adjusted for were those in the TIGRIS risk index model.18
To estimate the association between the self-reported quality of life (QoL) at enrolment and place of residence, the EQ-5D UK-weighted index score (categorised as <0.65, 0.65–0.69, 0.70–0.74, 0.75–0.99 and 1), the EQ-5D Visual Analogue Scale (VAS) score (categorised as <60, 60–69, 70–79, 80–89 and 90–100), and individual components (mobility, self-care, usual activities, pain/discomfort and anxiety/depression categorised as ‘No problems’, ‘Moderate problems’ and ‘Severe problems’) were summarised and compared using tests for linear trends. Comparisons adjusting for other characteristics associated with the QoL were made using multivariable linear regression models to estimate the mean difference in the EQ-5D index scores and EQ-5D VAS scores between urban and rural places of residence. Multivariable ordinal logistic regression models estimated the common ORs of being in a ‘worse’ versus ‘better’ category for each EQ-5D individual component comparing urban and rural places of residence, for example, the odds of having severe problems versus moderate/no problems or having severe/moderate problems versus no problems. Multivariable modelling was used to account for bias due to confounding, and continuous covariates were categorised if there was evidence of a nonlinear relationship with the outcome. Missing covariate data were imputed using the most common category or mean value, where appropriate.
A two-sided p value <0.05 was considered statistically significant. All analyses were performed using STATA V.17.0.
Baseline characteristics and medications
From the analysed population, 5251 patients reported living in an urban area, and the remaining 2935 patients reported living in a rural area. Patients were followed up for a median of 2.0 years. Online supplemental table 1 in the online supplemental material shows the prevalence by region and countries. Latin America had the lowest proportion of patients in the registry living in rural areas (n=119, 10.8%), whereas North America had the highest (n=516, 47.5%). Notably, in general, the proportion of patients reported to be living in the rural areas were lower than the results reported in the World Bank Data (online supplemental table 1).19 Table 1 shows the baseline characteristics of the population. A higher proportion of patients living in rural areas were male, never smoked, had no formal education, had a history of atrial fibrillation and had a non-ST-elevation AMI compared with those living in urban areas. Moreover, patients living in rural areas had a higher mean body mass index, and a lower proportion were living alone. Patients living in rural areas were less likely to be prescribed aspirin, clopidogrel or dual antiplatelet therapy (DAPT) and were more likely to be prescribed antidepressants. Prescription of other proven post-AMI secondary prevention therapies (eg, statins, beta-blockers) was not significantly different between the groups.
Healthcare resource utilisation
There are some evidence of differences between patients from rural and urban areas in the mean number of hospitalisations over 2 years of follow-up (0.40 vs 0.44, respectively; p=0.047) (table 2). However, despite a numerically lower mean total length of hospitalisations for patients living in rural areas, the difference between the groups was not statistically significant (9.1 vs 10.3 days; p=0.09). Similar results were noted for hospitalisation due to a CV cause. There was also no significant difference between the two groups in the number of ER visits or hospitalisations for bleeding. Patients living in rural areas were less likely to present for medical visits to GPs, cardiologists or other specialists. Of note, the mean number of visits to GPs and cardiologists was significantly higher for patients living in urban areas versus those living in rural areas.
As seen in table 3, the adjusted models showed fewer bleed hospitalisations and visits to a GP and cardiologist among rural patients (ERR=0.57, p<0.05; ERR=0.84, p<0.001; ERR=0.86, p<0.001, respectively). Finally, the total number of hospitalisations adjusted for 11 clinical variables and the QoL score was 10% lower in the rural population compared with that in the urban population (p=0.04).
Mortality, major adverse CV events and bleeding in patients in rural versus urban areas
No significant differences between patients in rural and urban areas were observed with respect to the cumulative incidence rates of the composite of AMI, unstable angina with urgent revascularisation, stroke or all-cause death at 2 years (n=187 (6.5%) vs n=375 (7.2%)) as well as for the composite endpoint of CV death, AMI or stroke (n=129 (4.5%) vs 248 (4.8%), respectively). The cumulative incidence rates for all-cause death at 2 years were 3.1% (n=91) vs 3.5% (n=183) and those for major bleeding were 1.0% (n=28) vs 1.4% (n=70), respectively, in rural versus urban areas (figure 1, online supplemental figure 1 and online supplemental table 3).
The unadjusted IRR for the composite of CV death, AMI and stroke comparing the urban and rural populations was 0.93 (95% CI, 0.75 to 1.15, p=0.49). After adjustments for differences in baseline characteristics, the IRR was essentially unchanged, indicating no significant differences between rural and urban populations (IRR=0.92, 95% CI, 0.74 to 1.15, p=0.50). Similarly, all-cause death was not significantly different between the groups, with an adjusted IRR of 0.86 (95% CI, 0.66 to 1.11, p=0.25), nor was the composite of CV death, AMI, stroke or major bleeding significantly different (adjusted IRR=0.88; 95% CI, 0.72 to 1.08; p=0.22) (online supplemental table 4).
HRQoL of patients in urban versus rural areas
In the unadjusted analyses, HRQoL did not differ between the groups when analysed with either the EQ-5D UK-weighted index score (0–1.0 score) or the EQ-5D VAS score (0–100 score). The proportion of patients in the highest health status category of HRQoL (index 1.0) was similar for rural and urban areas (46.1% vs 46.5%, respectively; p=0.30). The results were also not different considering the five domains of the EQ-5D (mobility, self-care, usual activities, pain, depression/anxiety) in the unadjusted analyses (online supplemental figure 2).
After adjustments for predictors of HRQoL (age, sex, region, country, body mass index, years in education and smoking status) at enrolment, there remained no differences in the HRQoL scores between rural and urban areas for either the EQ-5D UK-weighted index score (mean adjusted difference, 0.007 points (95% CI, −0.003 to 0.017); p=0.17) or the EQ-5D VAS score (mean adjusted difference, 0.31 points (95% CI, −0.50 to 1.12); p=0.46).
Regarding the five domains of the EQ-5D, after adjustments, living in a rural area was associated with lower odds of being in a worse category for mobility compared with living in an urban area (adjusted OR 0.80 (95% CI, 0.72 to 0.90); adjusted p<0.001). Adjusted differences in the other four domains were non-significant (table 4).
From our study comprising approximately 8500 patients 1–3 years post-AMI enrolled from 24 countries worldwide, three important findings emerged. First, patients living in rural areas had less frequent medical visits during a period of 2 years, with lower incidence of all-cause hospitalisations, compared with those living in urban areas. Second, despite these observations, there was no difference in the occurrence of major clinical outcomes (including all-cause mortality and the composite of CV mortality, AMI, unstable angina or stroke and bleeding outcomes) between patients living in urban and rural areas. Lastly, the QoL at enrolment tended to be similar among patients living in rural versus urban areas after AMI, although patients living in urban areas appeared to have a better QoL when considering the mobility domain.
The discrepancies in the quality of care between areas with different socioeconomical status or access to care, such as rural versus urban areas, have been the subject of prior publications.1 9 For example, it is known that AMI incidence is increasing faster in rural areas compared with that in urban areas, as is the prevalence of dyslipidaemia, especially in younger individuals.20 Moreover, the decline in age-adjusted CAD observed in the USA is more pronounced in urban areas than in rural areas,2 and the awareness, treatment and control of risk factors such as hypertension are higher in urban areas than in rural areas in low-income and low-middle–income countries but not in high-income or upper-middle–income countries.5 Furthermore, patients living in rural areas have lower cholesterol and glycated haemoglobin levels and lower rate of statin use compared with those living in urban areas.9
In the early phase of AMI, the median response and transport times are longer in rural areas compared with urban areas,7 whereas urban hospitals have greater use of early coronary angiography, percutaneous coronary intervention (PCI) and assistive devices for patients with cardiogenic shock.3 In our study comprising a multinational cohort of post-AMI patients, we observed a similar use of established therapies for secondary prevention, except that patients living in urban areas were more likely to be treated with ADP receptor inhibitors or to be on DAPT. Interestingly, the rates of PCI use for the treatment of the index event were similar between the groups. Given the established role of DAPT for patients in the long-term after myocardial infarction, regardless of PCI in the index event,21–23 this discrepancy in its use remains a window of opportunity to be improved when comparing rural and urban areas.
Regarding the composite of CV death, AMI, stroke and all-cause mortality, we did not observe significant differences between the groups after adjustments for baseline imbalances. Our results were similar to a study from Ontario, Canada, which enrolled patients with stable ischaemic heart disease.9 Despite having fewer ambulatory visits and cardiology visits over a year, patients from rural areas had similar clinical outcomes (mortality, AMI and total hospitalisation) compared with those from urban areas.9 A similar study conducted in China demonstrated that urban hospitals providing more resource-intensive care did not achieve better outcomes in comparison with rural hospitals.4 Our study expands on these findings, suggesting a similar scenario in a registry comprising more than 20 countries from four different global regions. With regard to mortality, it was found that, in general, the differences are widening in the USA between urban and rural areas (higher in rural areas) but not specifically for the diagnosis of atherosclerosis (rate ratios of 1.20 and 1.09, respectively).10 Patients with AMI living in urban areas have a lower adjusted 30-day mortality; however, after discharge, the rates were similar among urban and rural populations.1 Finally, a publication from the national AMI South Korea databank found a significantly higher HR for mortality in patients aged ≤55 years living in rural areas (compared with those in urban areas) in a 1-year follow-up; no statistically significant differences were found for patients >55 years.
All the aforementioned reports have many limitations. Electronic medical records, in general, have low data quality/completeness, making it difficult to obtain reliable information14 and rarely include information from different countries; moreover, the studies analysed only the in-hospital phase or short-term period after discharge. More importantly, with the exception of a study published in 1999,15 to the best of our knowledge, no other publication has reported a comparative analysis of rural and urban populations regarding post-AMI QoL, particularly among stable post-AMI patients within 1–3 years after hospitalisation. In this regard, our study presents a novel finding, suggesting similar HRQoL between patients after AMI living in rural versus urban areas, although the former appeared to have a lower QoL when only the mobility domain was considered. This finding could be ascribed to the fact that patients living in rural environments tend to exert manual labour more often, and limitations imposed by the cardiac event may impact this domain more compared with patients in an urban setting living a more sedentary, office-based lifestyle. However, we did not confirm other prior reports of higher association between depression and AMI among patients living in rural areas, but it is important to remember that our patients were more often prescribed antidepressants.24
Our study has some limitations. First, as the definition of rural and urban areas was investigator-reported based on what was self-defined by the patient, the incorrect categorisation of some urban/suburban areas as rural may be a possibility, as suggested by the comparison with data from the World Bank (online supplemental table 1). However, it is important to note that there is also an important limitation to the World Bank criteria, since there is no standardised definition of place of living worldwide. For instance, some countries use an urban classification related to the size or characteristics of settlements, while some define urban areas based on the presence of certain infrastructure and services.19 On the contrary, some countries designate urban areas based on administrative arrangements. Additionally, rurality index was not applied to precisely separate those two categories, and the information was obtained only at baseline; patients could have moved between the two settings during the follow-up period. Second, there was substantial variation among countries regarding the inclusion of sites from rural locations in the registry. Of note, patients from Latin America were mostly from urban places of residence. The results could have been different if the representation for urban and rural areas was equal for each country, although our adjusted models considered ‘country’ as a covariate. Third, although we captured data regarding the place of residence, we did not do so for the place of medical attendance. It is possible that someone reporting living in a rural area may have had easy access to a local community hospital, whereas some patients living in urban areas, especially in low-income or low-middle–income countries, could face more hardships with healthcare assistance. However, we consistently found that patients living in rural areas reported fewer visits to GPs, cardiologists or other specialists. Fourth, patients were recruited from urban centres that could conduct clinical research, and thus may not be fully representative of all post-AMI patients from rural settings. Fifth, considering the limited duration of the follow-up period (2 years), it is possible that the study may have been underpowered. Lastly, given the characteristics of the study design, it is possible that residual confounding may still be present in the adjusted models developed.
Among patients with a previous myocardial infarction (1–3 years ago), living in rural areas was associated with fewer GP/cardiologist visits and lesser hospitalisations. Despite this, clinical outcomes, including major adverse CV events, major bleeding, mortality and HRQoL (with the exception of mobility), were not significantly different between patients living in rural and urban areas.
Data availability statement
Data are available upon reasonable request. The data supporting the findings of this study may be obtained upon reasonable request and in accordance with AstraZeneca’s data sharing policy described at https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure.
Patient consent for publication
This study involves human participants. The TIGRIS study was performed in accordance with ethical principles that are consistent with the Declaration of Helsinki, the International Council for Harmonization Good Clinical Practice guidelines and applicable legislation on non-interventional studies. The study protocol and informed consent were reviewed by the corresponding health authorities and ethics boards of all participating study sites. All participants gave informed consent to participate in the study before taking part.
Statistical analyses were performed independently by RO and SP. SP had full access to all the study data and takes responsibility for its integrity and data analysis.
Contributors JCN is the guarantor of the study. JCN: conception and design; JCN, RO, SP: data analysis and interpretation of data; JCN, RO, SP, DBB, RHMF, SG, CBG, MGC, DW, SY, TS, KH, PRH: participated in the conduct of the registry, revised the manuscript critically and made important contributions to its content.
Funding The long-Term rIsk, clinical manaGement and healthcare Resource utilization of stable coronary artery dISease (TIGRIS) study is sponsored by AstraZeneca AB, Södertälje, Sweden. The study was funded by AstraZeneca. Editorial support was provided by Cactus Life Sciences (part of Cactus Communications, Mumbai, India).
Competing interests JCN has received research grants from Amgen, AstraZeneca, Bayer, Esperion, CLS Behring, Dalcor, Daiichi-Sankyo, Janssen, Novartis, Novo Nordisk, Sanofi, Vifor; honoraria/consultation from Daiichi-Sankyo, Novartis, Sanofi; and a scholarship from the Brazilian National Council for Scientific and Technological Development (CNPq #303448/2021/0). RO has received research grant support from AstraZeneca. RHMF reports research grants and personal fees from AstraZeneca, Bayer, Biomm and Servier, and research grants from Pfizer, EMS, Aché, CytoDin, Brazilian Ministry of Health, University Health Network (received from his institution) and Lemann Foundation Research Fellowship. SG has received research grant support (eg, steering committee or data and safety monitoring committee) and/or speaker/consulting honoraria (eg, advisory boards) from Amgen, Anthos Therapeutics, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, CSL Behring, Daiichi Sankyo/American Regent, Eli Lilly, Esperion, Ferring Pharmaceuticals, HLS Therapeutics, JAMP Pharma, Merck, Novartis, Novo Nordisk A/C, Pendopharm/Pharmascience, Pfizer, Regeneron, Sanofi, Servier and Valeo Pharma, and salary support/honoraria from the Heart and Stroke Foundation of Ontario/University of Toronto (Polo) Chair, Canadian Heart Research Centre and MD Primer, Canadian VIGOUR Centre, Cleveland Clinic Coordinating Center for Clinical Research, Duke Clinical Research Institute, New York University Clinical Coordinating Centre, PERFUSE Research Institute, and TIMI Study Group (Brigham Health). CBG has received consulting honoraria and/or research grant support from Armetheon, AstraZeneca, Bayer, Boehringer Ingelheim, BMS, Daiichi-Sankyo, Eli Lilly, Gilead, GlaxoSmithKline, Hoffmann-La Roche, Janssen, Medtronic, Pfizer, Salix Pharmaceuticals, Sanofi, Takeda and The Medicines Company. MGC has received speaker/consulting honoraria and/or research grant support from AstraZeneca, Medtronic, Abiomed and Merit Medical. DW has received speaker/consulting honoraria and/or research grant support from AstraZeneca, Bayer, Berlin-Chemie, Biotronik and Novartis. SY has received speaker/consulting honoraria and/or research grant support from Takeda, Daiichi-Sankyo, AstraZeneca and Boehringer Ingelheim. TS has received speaker/consulting honoraria and/or research grant support from Astellas, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, GlaxoSmithKline, Merck, Novartis, Pfizer and Sanofi. KH and PRH are employees of AstraZeneca. DBB has received speaker/consulting honoraria and/or research grant support from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, BMS, Eli Lilly, Merck and Sanofi. SP has received research grant support from AstraZeneca.
Provenance and peer review Not commissioned; externally peer reviewed.
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