Generalizability of Randomized Controlled Trials in Heart Failure PhD thesis. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, the Netherlands. Author: Yvonne Mei Fong Lim Cover design: Amin Adli Bin Dolbaharin and John Paul Snead Layout and printing: Gildeprint - The Netherlands ISBN: 978-94-6419-916-1 Studies in this thesis were funded by BigData@Heart (Innovative Medicines Initiative 2 Joint Undertaking under grant agreement number 116074) and the University Medical Center Utrecht Global Health Support Programme. Financial support by the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and the Dutch Heart Foundation for the printing of this thesis are gratefully acknowledged. Copyright © 2023 Yvonne Mei Fong Lim For published or accepted articles, the copyright has been transferred to the respective publishers. No part of this book may be reproduced without written permission from the author or as appropriate, the publishers of the manuscripts.
Generalizability of Randomized Controlled Trials in Heart Failure General iseerbaarheid van gerandomiseerde kl inische trials in hartfalen (met een samenvatt ing in het Nederlands) Hasi l percubaan kl inikal dan apl ikasi secara umum bagi pesaki t kegagalan jantung (dengan ringkasan dalam Bahasa Melayu) Proefschrift ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector magnificus, prof.dr. H.R.B.M. Kummeling, ingevolge het besluit van het college voor promoties in het openbaar te verdedigen op woensdag 27 september 2023 des middags te 4.15 uur door Mei Fong Lim geboren op 2 oktober 1985 te Selangor, Maleisië
Promotoren: Prof. dr. F.W. Asselbergs Prof. dr. D.E. Grobbee Copromotoren: Dr. C.H. Vaartjes Dr. S. Koudstaal
TABLE OF CONTENTS Chapter 1 General Introduction 7 Chapter 2 Comparing heart failure trial and registry populations Chapter 2.1 Generalizability of randomized controlled trials in heart failure with reduced ejection fraction 13 Chapter 2.2 Eligibility of Asian and European registry populations for phase III randomized trials in heart failure with reduced ejection fraction 53 Chapter 3 Age, sex and racial/ ethnic representation in the design of trials for heart failure Chapter 3.1 Sex differences in the generalizability of randomized controlled trials in heart failure with reduced ejection fraction: large-scale analysis of five trials and two registries 83 Chapter 3.2 Incidence of heart failure hospitalizations across ethnic groups in Malaysia: a ten-year population-based analysis from 2007 to 2016 117 Chapter 3.3 Trends for readmission and mortality after heart failure hospitalization in Malaysia, 2007 to 2016 155 Chapter 4 General Discussion 191 Appendices Summary Nederlandse samenvatting Ringkasan dalam Bahasa Melayu Acknowledgements About the author List of publications 209
1
General Introduction
CHAPTER 1 8 INTRODUCTION Clinical practice guidelines are developed for the general heart failure population and their evidence generally depend on randomized controlled trials. However, clinical trials are often criticized for poor generalizability, and this is among the cited reasons for underuse of effective treatments. For instance, lack of trial data in older patients explains in part the under-prescribing of warfarin in patients aged 75 years and above; the group who has the highest prevalence for non-rheumatic atrial fibrillation and also at highest risk without treatment.1–3 Historically, younger, white men were considered the normative population in heart failure trials whereas women or older persons were expected to have similar responses.4–6 However, recent posthoc analyses of cardiac resynchronization therapy (CRT) trials revealed that females were more likely to respond to CRTs and at shorter QRS complex duration than men7,8 whereas observational studies demonstrate that women benefited from lower doses than guideline recommended target doses of beta-blockers and RAASinhibitors,9 suggesting that this one-size-fits-all approach is flawed. Extrapolation of clinical trial results to its population of interest is often broadly described as external validity. It reflects a complex assessment of patient selection, trial setting, differences between trial and routine practice, outcome measurement and statistical methods.1,10 In this context, terms including generalizability, applicability, transferability, transportability and extrapolation have been used with overlapping meanings.1,10,11 The present thesis focuses on generalizability, defined as making inference from a study sample back to the target population, e.g. the domain (inclusive of the study population).11 While it is neither realistic nor reasonable to expect generalizability to every patient and setting, it can be assessed and described to allow clinicians, regulators and policy makers decide to whom trial results are applicable.1 Moreover, standardizing assessments of generalizability in current trials will pave the way for improving future trials. One way to formally ensure generalizability is by replicating the study in its new target population as effectiveness or pragmatic trials but this is impractical and very expensive to implement widely.10,12 In the absence of guidelines on trial generalizability assessment, another approach would be to conduct secondary data analyses in heart failure trial datasets, trial registers and observational cohort data.1,12 These methods can be classified into those involving emulation of
1 General introduction 9 existing or hypothetical trials and after-the-fact analysis of trial data sets13 and both approaches will be covered in this thesis. Inadequate generalizability can arise from under-representation of important subgroups among people with heart failure such as those of older ages, women, minority ethnic groups. Questions arise to whether meaningful differences exist for outcomes within these subgroups as patient characteristics are increasingly shown to be modifiers of treatment effect or safety. Racial differences in incidence and outcomes are well-established in studies in the U.S., in which Black and Hispanic/Latinx persons with heart failure are known to fare worse than White patients.4 This is attributable to a disproportionate burden of CV risk factors that leads to earlier onset of atherosclerotic disease and shorter life expectancy. Risk differences between racial groups are linked to social and system/institutional determinants of health, which cannot be ignored when considering the effectiveness of treatments in real-world heart failure patients. With that said, a broad racial category such as Asians can be heterogenous in terms of culture, ethnic, language and biology. For instance, people of South Asian and East Asian origins have marked differences in the prevalence of ischemic heart disease and its subsequent disease outcomes.4 For heart failure, inter-ethnic differences in prognosis among diverse South East Asian communities is much less understood compared to those on racial disparities in the U.S. On a similar note, people with multimorbid conditions are often excluded from heart failure trials. And yet it is likely that they are part of the population treated with an approved drug, despite a disproportionately small amount of data on safety. Given that extensive exclusion criteria limits target population representativeness and trial accrual rates (57% of terminated trials were terminated because of poor accrual14), knowing how individual criteria affects eligibility in a collective manner would be of added value at the design stage of trials. Among the challenges to assessing generalizability in trials for heart failure, is that it requires access to trial datasets but this is limited particularly for pharmaceutical industry-sponsored trials. Public-private partnerships such as the BigData@Heart project15,16, has enabled sharing of individual patient data for direct comparison of trial populations and heart failure registries. From here, we examine generalizability in terms of heart failure outcomes among enrolled trial participants relative to observational registry patients, quantify age, sex and ethnic differences in
CHAPTER 1 10 heart failure incidence and outcomes and assess the impact of eligibility criteria on the representativeness of heart failure trials. Aims of thesis The main aim of this thesis is to investigate generalizability of clinical trials for heart failure with reduced ejection fraction (HFrEF). The objectives of this thesis take three perspectives. First, we contrast patient characteristics and estimate generalizability of heart failure trials to observational registry cohorts. Because clear differences exist between trial and registry populations, we will include case-mix-adjusted outcome comparisons between study populations. In addition, we compare generalizability of these trials by outcomes in men and women. For the second objective, we assess at the design stage of trials, the impact of eligibility criteria of randomized trials on patient representativeness within European and Asian heart failure registries and subsequently in hypothetical trials by stepwise addition of the most commonly used criteria. Lastly, we focus on disentangling differences in incidence and heart failure outcomes by sex and ethnicity in a multi-ethnic community to highlight the importance of patient subgroup representation and diversity in future trials. OUTLINE OF THIS THESIS In the first part of this thesis (Chapter 2.1), we explore the differences between trial participants and registry cohorts and examined how risk factor adjustments affected the standardised mortality ratios between the populations. In Chapter 2.2 we identified the most frequently used inclusion and exclusion criteria for phase III HFrEF trials registered in ClinicalTrials.gov and subsequently compared eligibilities for the trials, based patient characteristics from an Asian and European registry cohort. In chapter three, we focus on demographic representation of trial populations. First, we determine whether all-cause and cardiovascular mortality outcomes for males and females in HFrEF trials differed from their counterparts in the registry cohort. In chapter 3.1 Next in chapter 3.2, we investigate the incidence of heart failure hospitalization and its 10- year trends by age, sex and ethnicity in a multiracial population in Malaysia. In chapter 3.3, we determine trends in prognosis of heart failure, in terms of readmission and mortality, differentiating between age, sex and ethnicity.
1 General introduction 11 REFERENCES 1. Rothwell PM. External validity of randomised controlled trials: “to whom do the results of this trial apply?” Lancet 2005;365:82–93. doi:10.1016/S0140-6736(04)17670-8. 2. Antani MR, Beyth RJ, Covinsky KE, Anderson PA, Miller DG, Cebul RD, et al. Failure to prescribe warfarin to patients with nonrheumatic atrial fibrillation. J Gen Intern Med 1996;11:713–720. doi:10.1007/BF02598984. 3. Oswald N, Bateman H. Applying research evidence to individuals in primary care: a study using nonrheumatic atrial fibrillation | Family Practice | Oxford Academic. Family Practice 1999;16:414–419. doi:https://doi.org/10.1093/fampra/16.4.414. 4. Michos ED, Reddy TK, Gulati M, Brewer LC, Bond RM, Velarde GP, et al. Improving the enrollment of women and racially/ethnically diverse populations in cardiovascular clinical trials: An ASPC practice statement. Am J Prev Cardiol 2021;8:100250. doi:10.1016/j.ajpc.2021.100250. 5. Pinn VW. Sex and Gender Factors in Medical Studies: Implications for Health and Clinical Practice. JAMA 2003;289:397–400. doi:10.1001/jama.289.4.397. 6. Tahhan AS, Vaduganathan M, Greene SJ, Fonarow GC, Fiuzat M, Jessup M, et al. Enrollment of Older Patients, Women, and Racial and Ethnic Minorities in Contemporary Heart Failure Clinical Trials: A Systematic Review. JAMA Cardiol 2018;3:1011–1019. doi:10.1001/jamacardio.2018.2559. 7. Woods B, Hawkins N, Mealing S, Sutton A, Abraham WT, Beshai JF, et al. Individual patient data network meta-analysis of mortality effects of implantable cardiac devices. Heart 2015;101:1800– 1806. doi:10.1136/heartjnl-2015-307634. 8. Zusterzeel R, Selzman KA, Sanders WE, Caños DA, O’Callaghan KM, Carpenter JL, et al. Cardiac Resynchronization Therapy in Women: US Food and Drug Administration Meta-analysis of PatientLevel Data. JAMA Internal Medicine 2014;174:1340–1348. doi:10.1001/jamainternmed.2014.2717. 9. Santema BT, Ouwerkerk W, Tromp J, Sama IE, Ravera A, Regitz-Zagrosek V, et al. Identifying optimal doses of heart failure medications in men compared with women: a prospective, observational, cohort study. Lancet 2019;394:1254–1263. doi:10.1016/S0140-6736(19)31792-1. 10. Dekkers OM, von Elm E, Algra A, Romijn JA, Vandenbroucke JP. How to assess the external validity of therapeutic trials: a conceptual approach. Int J Epidemiol 2010;39:89–94. doi:10.1093/ije/dyp174. 11. Lesko CR, Buchanan AL, Westreich D, Edwards JK, Hudgens MG, Cole SR. Generalizing Study Results: A Potential Outcomes Perspective. Epidemiology 2017;28:553–561. doi:10.1097/EDE.0000000000000664. 12. Flay BR, Biglan A, Boruch RF, Castro FG, Gottfredson D, Kellam S, et al. Standards of evidence: criteria for efficacy, effectiveness and dissemination. Prev Sci 2005;6:151–175. doi:10.1007/s11121-0055553-y. 13. Stuart EA, Bradshaw CP, Leaf PJ. Assessing the generalizability of randomized trial results to target populations. Prev Sci 2015;16:475–485. doi:10.1007/s11121-014-0513-z. 14. Williams RJ, Tse T, DiPiazza K, Zarin DA. Terminated Trials in the ClinicalTrials.gov Results Database: Evaluation of Availability of Primary Outcome Data and Reasons for Termination. PLoS One 2015;10:e0127242. doi:10.1371/journal.pone.0127242. 15. Innovative Medicines Initiative. BigData@Heart. IMI Innovative Medicines Initiative 2021. http://www.imi.europa.eu/projects-results/project-factsheets/bigdataheart(accessed September 7, 2021). 16. UMC Utrecht. BigData@Heart 2022. https://www.bigdata-heart.eu/ (accessed June 9, 2022).
2 Comparing heart failure trial and registry populations
Chapter 2.1 Generalizability of Randomized Controlled Trials in Heart Failure with Reduced Ejection Fraction Yvonne Mei Fong Lim Megan Molnar Ilonca Vaartjes Gianluigi Savarese Marinus JC Eijkemans Alicia Uijl Eleni Vradi Kiliana Suzart-Woischnik Jasper J. Brugts Hans-Peter Brunner-La Rocca Vanessa Blanc-Guillemaud Fabrice Couvelard Claire Baudier Tomasz Dyszynski Sandra Waechter Lars H Lund Arno W Hoes Benoit Tyl Folkert W Asselbergs Christoph Gerlinger Diederick E Grobbee Maureen Cronin Stefan Koudstaal European Heart Journal - Quality of Care and Clinical Outcomes 2021; 0, 1–9
CHAPTER 2.1 14 ABSTRACT Background Heart failure (HF) trials have stringent in- and ex- clusion criteria, but limited data exists regarding generalizability of trials. We compared patient characteristics and outcomes between patients with HF and reduced ejection fraction (HFrEF) in trials and observational registries. Methods and Results Individual patient data for 16922 patients from five randomized clinical trials and 46914 patients from two HF registries were included. The registry patients were categorised into trial-eligible and non-eligible groups using the most commonly used in- and ex-clusion criteria. A total of 26104 (56%) registry patients fulfilled the eligibility criteria. Unadjusted all-cause mortality rates at one year were lowest in the trial population (7%), followed by trial-eligible patients (12%) and trial-non-eligible registry patients (26%). After adjustment for age and sex, all-cause mortality rates were similar between trial participants and trial-eligible registry patients (standardised mortality ratio (SMR) 0.97; 95% confidence interval (CI) 0.92 -1.03) but cardiovascular mortality was higher in trial participants (SMR 1.19; 1.12 -1.27). After full case-mix adjustment, the SMR for cardiovascular mortality remained higher in the trials at 1.28 (1.20- 1.37) compared to RCT-eligible registry patients. Conclusion In contemporary HF registries, over half of HFrEF patients would have been eligible for trial enrolment. Crude clinical event rates were lower in the trials, but, after adjustment for case-mix, trial participants had similar rates of survival as registries. Despite this, they had about 30% higher cardiovascular mortality rates. Age and sex were the main drivers of differences in clinical outcomes between HF trials and observational HF registries.
Generalizability of HFrEF trials 15 INTRODUCTION Randomized controlled trials (RCTs) are the gold standard for evaluating the efficacy and safety of investigational therapies due to their robust methodology conducted within a strict regulatory framework.1 A well-conducted RCT has high internal validity, which ensures that the observed treatment effect is directly the result of the therapy tested.1–4 However, high internal validity can come at the expense of external validity, defined as the degree to which the treatment effect found in the study can be generalized and replicated outside the RCT.1 If the RCT results found in the study population are not generalizable to the target population, it is unclear which patients in routine care can receive a treatment safely and effectively.1–5 Physicians’ uncertainty and criticism of RCTs’ generalizability has been suggested as one reason for the underuse of evidence-based treatments, specifically in the field of heart failure (HF).2,6 There is currently no consensus on how to assess generalizability, but a logical and important first step is to assess if an RCT study population is representative of the projected target population.2–4,7 Studies comparing summary data on baseline characteristics between RCTs and observational data have already been conducted, specifically for heart failure with reduced ejection fraction (HFrEF).5,8–10 Although these studies have shown differences in crude outcomes between trial and real-world patients, it is not known how differences in patient characteristics drive the observed differences in prognosis. In addition, some of these comparisons have been limited by the small sample sizes from single trials. Here, we compared individual patient data of five HFrEF randomized clinical trials and two HF registries by direct data access and collaboration between academic researchers and pharmaceutical industry partners. We first determined their differences in patient characteristics, treatment, and clinical outcomes. Then, we identified the proportion of registry patients who were eligible for inclusion in the trials and compared their outcomes with trial participants while adjusting for known prognostic factors of HF at the individual patient level. 2.1
CHAPTER 2.1 16 METHODS Data sources Based on a collaboration with industry partners through the BigData@Heart Consortium11, data access to patient level information was obtained for five randomized clinical trials in HFrEF patients.. BEAUTIFUL and SHIFT were ivabradine trials (n= 15732)12,13, FAIR-HF and CONFIRM were studies on intravenous iron supplementation (n=763)14,15 and PANTHEON was a trial for neladenosone bialanate (n=427).16 Of these, three were phase III trials, one was phase II and lastly, one phase IV study. All RCTs included HFrEF patients based on left ventricular ejection fraction (LVEF) values (ranging from ≤ 35 to ≤ 45%) except for the BEAUTIFUL study, which recruited coronary artery disease (CAD) patients who had left ventricular dysfunction. To maintain comparability between patients from the RCTs, only patients with New York Heart Association (NYHA) class II-IV from BEAUTIFUL (n=9227) were included. Aggregated data from both treatment and placebo arms of each RCT were pooled and compared against the HFrEF population from two observational data sources: the CHECK-HF and the SwedeHF registries.17,18 Detailed information on the methods for both registries can be found elsewhere.17,18 Briefly, the CHECK-HF registry included patients with chronic HF if they had an HF diagnosis based on ESC 2012 guidelines between 2013 and 2016.17 The ongoing SwedeHF registry enrolled patients with clinician-judged HF patients in Sweden.18 For the current analysis, outpatients registered between 2000 to 2016 (n=40 230) were included to ensure consistency with CHECK-HF. Data from both registries were combined for describing patient characteristics and treatment but only SwedeHF data was used in the reporting on clinical outcomes because CHECK-HF did not have follow-up data. For each of the five trials, ethics approval and written informed consent were obtained by the respective study investigators.12–16 CHECK-HF registry was granted ethics approval for anonymised analysis of existing patient data, while in the SwedeHF registry, enrolment was based on specific health centres’ participation and patients allowed to opt-out should they wish not to participate.17,18
Generalizability of HFrEF trials 17 Eligibility criteria and outcomes The inclusion and exclusion criteria listed in the study protocol of the five RCTs were tabulated (Supplementary Table 1) to identify common study entry criteria. These criteria were cross-checked for data availability within the registries and a set of most commonly used eligibility criteria was then identified to select subsets of RCT-eligible and non-eligible patients from the registries. The following inclusion criteria were used: age ≥18 years, LVEF<40%, NYHA functional class II to IV, on optimallytolerated chronic HF medications of β-blocker and angiotensin-converting enzyme inhibitor (ACEI) or angiotensin-II receptor blocker (ARB). Then, the following exclusion criteria were applied: serum haemoglobin concentrations <11g/dL in men or <10g/dL in women, chronic liver disease, creatinine >220μmol/L and cancer. Comparisons were made based on (i) patient baseline characteristics (ii) cardiovascular medications and (iii) mortality outcomes. For summary statistics, aggregated data were extracted from each trial and there were instances of low patient numbers in the data contingency tables. To maintain patient anonymity, all table cells with counts of 3 and below were replaced with a central number of 2.19 For HF medications, the percentage of patients who received <50% or ≥ 50% target doses of the HF medications were assessed (Supplementary table 2). Lastly, the following clinical outcomes at one year were assessed: all-cause mortality, cardiovascular mortality (ICD-10 codes I00 – I99) and first HF hospitalization (main diagnosis with codes I50, I11.0, I42.0, I42.3-I42.9, I43, I25.5, K76.1, I13.0, I32.2 or J81). Follow-up duration differed between the five trials. Three trials (BEAUTIFUL, CONFIRM-HF and SHIFT) had follow-up data for at least one year, so outcome at one year was reported here. The remaining two trials (FAIR-HF and PANTHEON) had less than a year’s follow-up and patients were censored at the end of study. Statistical analysis Continuous data are presented as mean with standard deviation while categorical variables are reported in frequencies and percentages. Mean and proportion differences between the RCT and RCT-eligible registry patients were calculated and reported with their corresponding 99% confidence intervals (CI). Data are presented by three groups: (i) RCT participants, (ii) RCT-eligible, and (iii) RCT-non-eligible registry patients. Cumulative incidence curves were used to compare unadjusted outcomes between study groups. For cardiovascular mortality, deaths due to other 2.1
CHAPTER 2.1 18 causes were treated as competing events. For first HF hospitalization, all-cause deaths were treated as competing events. Then, standardised mortality ratios (SMRs) were used to compare adjusted mortality rates between the trials and the SwedeHF registry population. First, we fitted a Poisson model with 11 prognostic indicators from a validated MAGGIC HF risk score (age, sex, LVEF, NYHA class, serum creatinine, chronic obstructive pulmonary disease (COPD), diabetes, systolic blood pressure, body mass index (BMI), HF duration, smoking status) in a stepwise manner to the trial-eligible SwedeHF patients’ data.20,21 Next, the model with the derived β coefficients was applied to each trial to estimate each individual’s expected mortality, which was then summed across all participants to derive total expected mortality counts. The observed mortality count for each trial was divided by the expected mortality count to give the SMRs. An SMR value > 1 indicated that the observed risk of mortality in a trial was higher than the risk predicted based on SwedeHF patients as the reference population. The SMR was risk-adjusted for 11 prognostic factors to address heterogeneity between the trials. This was considered sufficient adjustment to pool the trials using fixed effect meta-analysis without introducing partial pooling. The corresponding 95% CI was determined using methods described by Breslow and Day.22 SMRs were not estimated for HF hospitalization because its existing risk prediction models do not have adequate discriminative performance compared to those designed to predict mortality.23 For cardiovascular causes of mortality, the Poisson model has taken into account competing risk from other causes of death as every patient’s follow-up duration was included in the estimation of the number of events. Rather than predicting cumulative probabilities, the Poisson model gives a prediction of the number of events for each individual which can be summed to obtain the total expected number of events in a trial. Missing data was multiply imputed by chained equations using the mice package in R.24 The number of imputations was set at 20.25 Statistical significance was set at 0.05. Statistical analysis was performed using the R statistical software version 3.6.1 (R Core Team, 2019) and Stata SE Version 15 (StataCorp LP, College Station, TX).26,27 The largest RCTs (BEAUTIFUL and SHIFT) in this analysis only included patients who were in sinus rhythm and the BEAUTIFUL study included a population who had CAD; therefore, sensitivity analyses were conducted in subsets of registry patients who were (i) in sinus rhythm or (ii) diagnosed with CAD. The fully-adjusted
Generalizability of HFrEF trials 19 SMRs from each subset were then compared to the original estimates. A third sensitivity analysis was performed to determine the effects of time period differences between trial and registry data on HF medication prescription. RESULTS Study population Majority of registry patients (56%) were eligible for inclusion in the trials (Figure 1). Compared to the overall registry group, RCT patients were younger (mean 63.6 years vs 72.7 years), less frequently women (22% vs 31%), had longer duration of HF, were more often in LVEF category of 30-39% as opposed to <30% and predominantly in NYHA class II rather than III- IV (Table 1). The baseline characteristics of each registry is provided in Supplementary Table 3. Hypertension, diabetes, and CAD were more common in the RCT group compared to the overall registry group. However, the proportion of patients with valve disease, stroke, anaemia, COPD, cancer, and coronary revascularisation were markedly lower in the RCT patients. After restricting the registry group to those who would be eligible for inclusion in the RCTs, this RCT-eligible registry group was more similar to the RCT group in NYHA class, serum creatinine, and haemoglobin, but differences in comorbidities largely remained (Table 1). In the selection of trialeligible patients, the most restrictive inclusion criteria were NYHA class II-IV and the use of ACEI/ARB and ß-blockers while the most restrictive exclusion criterion was cancer (Figure 1). Use and target doses of cardiovascular medication Prescription of medications was higher for antiplatelets, mineralocorticoid receptor antagonists, and statins in the RCTs compared to registry patients. Despite similar proportions in use of ACEI/ ARB (87% vs 90%), more registry than RCT patients received higher doses (≥ 50% of target doses) of these medications (Supplementary table 4). We then restricted the comparison to the same time periods (2005 - 2009) between the 2 largest trials and SwedeHF registry patients and found that the proportion of patients who were given target doses did not differ much from the main findings, which used data from 2001 to 2016 (Supplementary Table 5). 2.1
CHAPTER 2.1 20 Clinical outcomes at one year Cumulative incidence curves are shown in the central illustration and Figure 2. Allcause mortality, cardiovascular mortality and first HF hospitalization at one year were lower in the RCTs than in trial-eligible and trial non-eligible registry groups. There was no remaining difference in all-cause mortality risk between trial and registry patients after adjusting for known HF prognostic factors (fully-adjusted (model 4) SMR 1.04; 95%CI 0.98 – 1.11)) (Central Illustration). However, higher cardiovascular mortality risk persisted in the RCT group compared to trial-eligible registry patients (fully-adjusted (model 4) SMR 1.28; 95%CI 1.20 – 1.37). Age and sex explained most of the mortality difference between patient groups, as reflected in the large shift of SMR between Model 1 (empty model) to Model 2 (with age and sex). Stepwise addition of prognostic factors changed SMR in the same direction but to a lesser degree, as seen in the shift of SMR in Model 2 (with age and sex) to Model 4 (fully adjusted) for all-cause and cardiovascular mortality. Sensitivity analyses were conducted by estimating SMRs in a subset of patients who were in sinus rhythm and estimates were similar to those obtained in the main results (Supplementary figures 1 and 2).
21 Generalizability of HFrEF trials Figure 1. Flow chart of selection of RCT-eligible patients based on harmonised eligibility criteria CHECK-HF registry (2013-2016) 6684 (100%) RCT-eligible 3256 (49%) RCT-non-eligible 3428 (51%) Inclusion criteria: Number (%) which did not fulfil inclusion criteria Age >=18 0 (0%) NYHA class II-IV 992 (15%) B-blocker -yes 976 (15%) ACEI/ARB- yes 876 (13%) Exclusion criteria : Number(%) Creatinine >220 µmol/L 276 (4%) Haemoglobin <11 g/dL (men) or <10g/dL (women) 660 (10%) Cancer 902 (13%) SwedeHF registry (2000-2016) 40 230 (100%) RCT-eligible 22 848 (57%) RCT-non-eligible 17 382 (43%) Inclusion criteria: Number (%) which did not fulfil inclusion criteria Age >=18 5 (0.0001%) NYHA class II-IV 3475 (9%) B-blocker -yes 4437 (11%) ACEI/ARB- yes 5162 (13%) Exclusion criteria : Number(%) Liver disease 957 (2%) Creatinine >220 µmol/L 1478 (4%) Haemoglobin <11 g/dL (men) or <10g/dL (women) 2522 (6%) Cancer 5808 (14%) Percentages of those not included and excluded based on individual criteria does not add up to percentage of non-eligible patients because one patient can be excluded based on one or more criteria Liver disease status was not recorded in CHECK-HF 2.1
CHAPTER 2.1 22 Table 1. Characteristics of HFrEF patients by RCT and registry groups Registry population RCT vs RCT-eligible RCT population N= 16 922 RCT- eligible N= 26 104 (56%) RCT-noneligible N=20 810 (44%) Difference in mean or proportion (99% CI) p-value b Patient characteristics Age (years) 63.6 ± 10.0 71.1 ± 12.6 74.7 ± 13.1 -7.5 (-7.8, -7.2) *** Women 3663 (22%) 8294 (32%) 6290 (30%) -10.1% (-11.2%, -9.0%) *** Body mass index (kg/m2) 28.3 ± 4.4 27.1 ± 5.7 25.9 ± 6.1 -1.2 (-1.3, -1.1) *** Systolic blood pressure – mmHg 125.2 ± 13.4 124.6 ± 21.0 124.5 ± 22.7 0.6 (0.1, 1.1) *** Diastolic blood pressure – mmHg 76.6 ± 8.4 73.7 ± 12.3 72.3 ± 13.4 2.9 (2.6, 3.2) *** Heart rate – beats per minute 74.4 ± 9.5 74.5 ± 15.9 75.7 ± 17.4 -0.1 (-0.4, 0.2) Serum creatinine - μmol/L 99.2 ± 29.5 99.4 ± 37.9 123.4 ± 91.2 -0.2 (-1.1, 0.6) Haemoglobin -g/dL 14.1 ± 1.3 13.7 ± 2.1 12.9 ± 2.6 0.4 (0.4, 0.4) *** Current smoker 2667 (16%) 3370 (15%) a 2301 (13%) a 1.1% (0.1%, 2.0%) ** Heart failure severity Duration of heart failure -months 42.0 ± 56.4 29.8 ± 61.7 32.9 ± 61.9 12.2 (10.7, 13.7) *** LVEF categories-no (%) <30 5338 (32%) 13 936 (53%) 9751 (47%) - *** 30-39 11 225 (66%) 12 168 (47%) 11 059 (53%) >=40 247 (1%) - - missing 112(1%) - - Mean LVEF (%) 31 - - -
23 Generalizability of HFrEF trials Table 1 (continued). Characteristics of HFrEF patients by RCT and registry groups Registry population RCT vs RCT-eligible RCT population N= 16 922 RCT- eligible N= 26 104 (56%) RCT-noneligible N=20 810 (44%) Difference in mean or proportion (99% CI) p-value b NYHA Functional Class – no (%) I 3 (0.02%) 0 (0%) 4459 (21%) - *** II 10 394 (61%) 14 478 (55%) 7231 (35%) III 6422 (38%) 10 623 (41%) 7673 (37%) IV 113 (1%) 1003 (4%) 1447 (7%) Medical history – no (%) Hypertension 11 517(68%) 14 654 (56%) 11 505 (55%) 11.9% (10.7%, 13.2%) *** Diabetes mellitus 5711 (34%) 7083 (27%) 5649 (27%) 6.6% (5.4%, 7.8%) *** Coronary artery disease 14 541 (86%) 11 916 (52%) a 9497 (55%) a 33.8% (32.7%, 34.9%) *** History of MI 5721 (34%) 8120 (31%) 6776 (33%) 2.7% (1.5%, 3.9%) *** Atrial fibrillation 449 (38%) c 12563 (48%) 10014 (48%) -10.4% (-14.1% -6.7%) *** Valvular disease 2009 (12%) 5616 (22%) 5280 (25%) -10.7% (-11.7%, -9.8%) *** Stroke/ TIA 1564 (9%) 3220 (14%) a 3067 (18%) a -4.8% (-5.7%, -4.0%) *** Anaemia 588 (3%) 6611 (25%) 9064 (44%) -21.8% (-22.6%, -21.1%) *** COPD 1482 (9%) 5084 (19%) 4417 (21%) -10.7% (-11.6%, -9.9%) *** Depression 451 (3%) 1117 (5%) a 894 (5%) a -2.2% (-2.7%, -1.7%) *** Cancer 462 (3%) 0 (0%) 6710 (32%) 2.7% (2.4%, 3.1%) *** Coronary revascularisation PCI 1538 (9%) 1994 (8%) 1528 (7%) - *** CABG 1029 (6%) 3038 (12%) 2609 (13%) PCI + CABG 236 (1%) 2913 (11%) 2020 (10%) 2.1
CHAPTER 2.1 24 Values are expressed as mean standard deviation or number (%) *p<0.05, **p<0.01, ***p<0.001 a. data from SwedeHF only b. comparison between RCT and registry (RCT-eligible) population (independent t-test for continuous and ꭓ2 -test for categorical variables) c. RCT data only from CONFIRM, FAIR HF and PANTHEON d. Statistical comparisons were not done for ACEI/ ARB and β-blocker because these treatments were part of the criteria for selecting RCTeligible registry patients Percentages may not add up to 100% due to rounding. ACE, angiotensin-converting enzyme inhibitor; ARB, angiotensin-II receptor blocker; CABG, coronary artery bypass graft; CI, confidence intervals; COPD, chronic obstructive pulmonary disease; CRT, cardiac resynchronisation therapy; HF, heart failure; ICD, implantable cardioverter defibrillator; LVEF, left ventricular ejection fraction; MRA, mineralocorticoid receptor antagonist; MI, myocardial infarction; PCI, percutaneous coronary intervention; TIA, transient ischaemic attack Table 1 (continued). Characteristics of HFrEF patients by RCT and registry groups Registry population RCT vs RCT-eligible RCT population N= 16 922 RCT- eligible N= 26 104 (56%) RCT-noneligible N=20 810 (44%) Difference in mean or proportion (99% CI) p-value b Clinical outcomes at 1 year All-cause mortality 1112 (7%) 2674 (12%) a 4482 (26%) a -5.1% (-5.9%, -4.4%) *** Cardiovascular mortality 1005 (6%) 2026 (9%) a 3114 (18%) a -2.9% (-3.6%, -2.3%) *** First HF hospitalization 1399 (8%) 5544 (24%) a 4310 (25%) a -16.0% (-16.9%, -15.1%) *** Cardiovascular medications at baseline ACEI/ ARB d 15 251 (90%) 26 104 (100%) 14 773 (71%) - - β -blocker d 14 808 (88%) 26 104 (100%) 15 392 (75%) - - MRA 7294 (43%) 10 275 (40%) 6880 (33%) 3% (2%, 5%) *** Diuretic 12 120 (72%) 20 697 (79%) 16 379 (79%) -8% (-9%, -7%) *** Antiplatelet 13 208 (78%) 12 329 (47%) 9788 (47%) 31% (30%, 32%) *** Digitalis 2500 (15%) 4447 (17%) 3002 (14%) -2% (-3%, -1%) *** Statins 11 231 (66%) 13 995 (54%) 9674 (47%) 13% (11%, 14%) ***
Generalizability of HFrEF trials 25 Central illustration. Cumulative incidence and case-mix adjusted standardised mortality ratios for all-cause and cardiovascular mortality at one year. (A) Cumulative incidence for allcause mortality between RCT and registry patients. (B) Cumulative incidence for cardiovascular mortality between RCT and registry patients. (C) Standardised mortality ratios (SMR) for allcause and cardiovascular mortality with stepwise adjustment for HF prognostic factors. Pooled SMRs estimated from 5 trials with their 95% CI were reported. ϱϲй ĞůŝŐŝďůĞ Z d ZĞŐŝƐƚƌLJ ůŝŐŝďůĞ EŽŶͲĞůŝŐŝďůĞ KůĚĞƌ͕ ŵŽƌĞ ǁŽŵĞŶ ,ŝŐŚĞƌ >s & >ŽǁĞƌ >s & DŽƌĞ ŶŽŶͲĐĂƌĚŝĂĐ ĐŽŵŽƌďŝĚŝƚŝĞƐ ĂƐĞůŝŶĞ ĚŝĨĨĞƌĞŶĐĞƐ ďĞLJŽŶĚ ĞůŝŐŝďŝůŝƚLJ ĐƌŝƚĞƌŝĂ ƉƉůLJ ŚĂƌŵŽŶŝƐĞĚ ĞůŝŐŝďŝůŝƚLJ ĐƌŝƚĞƌŝĂ ZĞĂů ǁŽƌůĚ ,& ƉŽƉƵůĂƚŝŽŶ ; Ϳ KŶĞͲLJĞĂƌ ĂůůͲĐĂƵƐĞ ŵŽƌƚĂůŝƚLJ ; Ϳ KŶĞͲLJĞĂƌ ĐĂƌĚŝŽǀĂƐĐƵůĂƌ ŵŽƌƚĂůŝƚLJ Number at risk Registry (RCT-non-eligible) 17382 14739 13649 12693 11886 Registry (RCT-eligible) 22848 21437 20518 19609 18713 RCT 16922 16605 16324 15624 14993 Number at risk Registry (RCT-non-eligible) 17382 14740 13651 12697 11888 Registry (RCT-eligible) 22848 21436 20516 19607 18711 RCT 16922 16605 16324 15624 14993 DŽĚĞů ϰ ;ŵŽĚĞů ϯ н ƐŵŽŬŝŶŐ ƐƚĂƚƵƐ͕ D/͕ ,& ĚƵƌĂƚŝŽŶ͕ >s &͕ KW ͕ ĚŝĂďĞƚĞƐͿ DŽĚĞů ϯ ;ŵŽĚĞů Ϯ н Ez, ͕ ^ W͕ ĐƌĞĂƚŝŶŝŶĞͿ DŽĚĞů Ϯ ;ĂĚũƵƐƚĞĚ ĨŽƌ ĂŐĞ͕ ƐĞdžͿ DŽĚĞů ϭ ;ƵŶĂĚũƵƐƚĞĚͿ ĂƌĚŝŽǀĂƐĐƵůĂƌ ŵŽƌƚĂůŝƚLJ DŽĚĞů ϰ ;ŵŽĚĞů ϯ нƐŵŽŬŝŶŐ ƐƚĂƚƵƐ͕ D/͕ ,& ĚƵƌĂƚŝŽŶ͕ >s &͕ KW ͕ ĚŝĂďĞƚĞƐͿ DŽĚĞů ϯ ;ŵŽĚĞů Ϯ нEz, ͕ ^ W͕ ĐƌĞĂƚŝŶŝŶĞͿ DŽĚĞů Ϯ ;ĂĚũƵƐƚĞĚ ĨŽƌ ĂŐĞ͕ ƐĞdžͿ DŽĚĞů ϭ ;ƵŶĂĚũƵƐƚĞĚͿ ůůͲĐĂƵƐĞ ŵŽƌƚĂůŝƚLJ ϭ͘Ϯϴ ϭ͘Ϯ͕ ϭ͘ϯϳ ϭ͘Ϯϰ ϭ͘ϭϳ͕ ϭ͘ϯϯ ϭ͘ϭϵ ϭ͘ϭϮ͕ ϭ͘Ϯϳ Ϭ͘ϲϮ Ϭ͘ϱϴ͕ Ϭ͘ϲϲ ϭ͘Ϭϰ Ϭ͘ϵϴ͕ ϭ͘ϭϭ ϭ͘Ϭϭ Ϭ͘ϵϱ͕ ϭ͘Ϭϴ Ϭ͘ϵϳ Ϭ͘ϵϮ͕ ϭ͘Ϭϯ Ϭ͘ϱϮ Ϭ͘ϰϵ͕ Ϭ͘ϱϲ ^DZ ϵϱй / Ϭ͘Ϯϱ Ϭ͘ϱ ϭ Ϯ Lower risk in Z d <--- ---> Higher risk in RCT (C) Standardised mortality ratios (RCT vs RCT-eligible registry patients) 2.1
CHAPTER 2.1 26 Figure 2. Cumulative incidence curves for first HF hospitalization at 1 year by (i) RCT participants, (ii)RCT-eligible and (iii) RCT-non-eligible registry patients DISCUSSION The present study has individual patient data of over 62 000 patients from five clinical trials and two observational HF registries, which allowed direct and adjusted comparisons on patient characteristics for both all-cause and cause-specific mortality. Overall, we found that over half of patients in the registries met the most commonly used in- and ex-clusion criteria for trial enrolment. Unadjusted survival was markedly lower in registries than trials. However, after adjusting for case-mix, all-cause mortality rates were comparable between the trials and registries while cardiovascular mortality occurred more frequently in the trial participants compared to registry patients. We identified a higher proportion of trial-eligible patients compared to previous studies on patients with acute decompensated HF and HF with reduced and preserved ejection fraction: 56% vs. 13 % to 42%.8,28,29 Furthermore, the percentage of trial-eligible registry patients who were given at least 50% target doses of HF medications were slightly higher than in RCTs. This higher proportion compared to previous reports could be explained at least in part by extensive heart failure programs and nurse-led up-titration of disease-modifying therapies in the Number at risk Registry (RCT-non-eligible) 17382 12802 11320 10242 9334 Registry (RCT-eligible) 22848 18911 17179 15766 14583 RCT 16922 16387 15895 15151 14459
Generalizability of HFrEF trials 27 Netherlands and Sweden. Also, data in the registries were from more recent years than the trials, thus reflecting more contemporary prescribing practices. Accordingly, we would expect background therapies in newer HF trials to be at a higher rate than the ones described here. Therefore, our findings, along with other recent studies in acute HF suggest that the gap in HF guideline-adherent treatment between trial and real-world patients is narrowing.6,30 The differences observed between trial participants and trial-eligible registry patients highlight other factors besides eligibility criteria that influence patient selection in RCTs. Physicians intuitively recruit patients who are deemed less likely to drop out to ensure low attrition rates which retain high internal validity.31–33 Older patients and those with comorbidities are not always physically or mentally able to comply and finish the treatment protocol due to frailty, low mobility and increased risk for adverse events.7,34 Women with HF tend to be older and are less likely to participate due to perceived harm from clinical studies, transportation difficulties, or constraints from a caregiving role.33,35,36 Consequently, the additional criteria introduced by investigators alongside the eligibility criteria consistently cause underrepresentation of older patients, those with comorbidities and women in CV trials..37 However, expanding the study population to include these groups would increase the cost of already expensive HF trials, and other solutions to improve generalizability that have been proposed include individual participant data metaanalysis, proper reporting of subgroup analysis, registry-based trials or comparative effectiveness studies.38–40 The growing trend to conduct RCTs as site-less or directto-patient studies may reduce this bias in the future. We have shown, by direct comparisons between study groups that the risk of mortality and HF hospitalization was lowest in the trial population. However, after accounting for known prognostic factors for survival in HF, differences in survival between trial and registry patients disappeared. In fact, age and sex combined explained the largest variation in standardised mortality ratios between trials and registries. This observation is evident for both all-cause and cardiovascular mortality and highlights their important contribution on the generalizability of HF trials. Taken together, it seems that differences in overall survival between HF trials and registries behave predictably and could be addressed by clinical variables which are readily available in daily clinical practice. Although well-accepted, we have demonstrated for the first time that there are increased cardiovascular mortality rates 2.1
CHAPTER 2.1 28 in the HF trial participants compared to trial-eligible registry patients, as high up to 30% even after adjustment for prognostic factors. From a drug developer and/or regulatory perspective, prognostic enrichment strategies were advocated and used in many cardiovascular trials to identify patients who a have higher likelihood of cardiovascular events.32 Additionally, excluding patients with other comorbidities in these trials could lead to lower competing risks of death from non-cardiovascular causes. On a broadly similar note, trial-eligible registry patients selected for the PARADIGM-HF trial criteria had higher risk of both cardiovascular and noncardiovascular mortality compared to non-eligible registry patients.41 From the clinicians’ perspective, it is important to be aware that half of patients were ineligible, and that even among trial-eligible patients, residual differences between cardiovascular and non-cardiovascular outcomes persists. Strengths and limitations The strength of this study lies in the large sample sizes from both trial and observational datasets. Direct access to individual patient data also enabled the reporting of case-mix-adjusted differences in outcomes between trials and registry. There are also several limitations to this study. First, we applied a harmonised set of criteria which were common across the trials based only on data that were also available from the registries. There was not sufficient depth in the data from the registries to assess many of the eligibility criteria such as worsening HF in the past 12 months, scheduled coronary revascularisation within 3 months or severe valve disease. Also, not all criteria per RCT have been considered but only the most common ones. For these reasons, the percentage of patients eligible for trial inclusion is likely overestimated. The trials included in this study were a convenient sample based on data accessibility; thus, it can be difficult to infer these findings to other HF trials. Secondly, a large proportion of trials patients came from two RCTs which excluded patients with atrial fibrillation (BEAUTIFUL and SHIFT), which might have impacted the results. However, we believe that this impact is not substantial, as supported by sensitivity analyses (Supplementary figures 1 and 2). Although the trials evaluated here were not the most recent HFrEF trials, we do not expect large changes in patient and clinical characteristics among those enrolled in trials then and now. This is supported by a baseline characteristics comparison with DAPA-HF and PARADIGM-HF, which showed comparable patient characteristics in terms of mean
Generalizability of HFrEF trials 29 age, percentage of women, percentage in NYHA class III/IV and mean LVEF, except for percentage with atrial fibrillation which was lower in this study.42 It is also necessary to note that, although registry patients are a fair representation of realworld patients, there are likely to be some differences in characteristics and treatment practices between patients who were and were not enrolled in the registries. We also acknowledge that the trial and real-world populations differed on geographical location, healthcare systems and time of data collection.43 CONCLUSION In summary, over half of patients in registries met the most commonly used in- and ex- clusion criteria for potential trial enrolment. In terms of generalisability, age and sex were the main drivers of differences in clinical outcomes between HF trials and observational HF registries. As expected, HF trial participants showed higher residual cardiovascular mortality rates after correction for case mix. 2.1
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