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Record W4390055066 · doi:10.1002/ejhf.3109

Effective Medications can Work Only in Patients Who Take Them: Implications for Post-Acute Heart Failure Care

2023· article· en· W4390055066 on OpenAlexaboutno aff
Gad Cotter, Beth A. Davison, Kirkwood F. Adams, Andrew P. Ambrosy, L. S. Atabaeva, Craig J. Beavers, Ankeet S. Bhatt, Michael M. Givertz, Justin L. Grodin, Anuradha Lala, Mikhail Novosadov, George Sokos, Koji Takagi, John R. Teerlink, Deepak L. Bhatt

Bibliographic record

VenueEuropean Journal of Heart Failure · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineChapelLibrary scienceGerontologyFamily medicineArt historyHistory

Abstract

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Acute heart failure (AHF) remains a predominant cause of hospitalization, especially among individuals aged 65 years or older, contributing to millions of hospitalizations per year globally.1, 2 Despite advances in therapeutic approach, AHF remains a syndrome with a poor prognosis, in-hospital mortality rates of 4% to 7% globally, and readmission rates ranging from 22% to 30% at 6 months.3 Guideline-directed medical therapy (GDMT) constitutes the core of effective heart failure (HF) management, making adherence to medical regimens critical to the improvement of outcomes. It is well known that non-adherence to HF treatment is a potential contributor to HF exacerbation, correlating with higher readmission rates and increase in mortality.4 As C. Everett Koop, former United States Surgeon General, is often quoted as saying, 'Drugs don't work in patients who don't take them'. Thus, medication non-adherence, which may lead to HF exacerbations, represents a potentially modifiable risk factor for adverse outcomes.5 Reported medication non-adherence rates among HF patients have varied widely, but most studies have reported rates of up to 70%.6 Consistent with these findings, in a 113 patient sub-study of STRONG-HF, despite the patients receiving medications at no cost, approximately one in three patients prescribed carvedilol or enalapril had non-detectable drug metabolite blood levels suggestive of non-adherence.7 Similar results were seen in studies of other cardiovascular (CV) conditions such as hypertension8 and CV disease in general.9 Thus, the evidence suggests that adherence is suboptimal in the context of clinical trials and is expected to be even lower in real-world settings. Several barriers to adherence have been posited including patient circumstances, treatment-related factors, polypharmacy, or multiple providers (Figure 1).9-11 Patient (health literacy, socioeconomic status, age, gender, region), healthcare system (inadequate communication, prescription practices, lack of support, short and infrequent visits) and payer (lack of coverage) factors were found to contribute to non-adherence.9 A systematic review of 11 fair-to-good quality studies of medication adherence in HF12 found inconsistent evidence relating multiple possible contributors to non-adherence including social and economic, patient-related (social support and patient-perceived barriers), condition-related (comorbidities, functional status, depression), and treatment-related factors. Moderate evidence suggests that prior admissions (hospitalization and nursing home stays) are associated with increased adherence, while education level and visiting more healthcare professionals are not. The authors speculated that the association with past admission might be due to patient education during the admission or that it changed the patients' perception regarding their health, or that patients fear being rehospitalized. In CV diseases in general, treatment factors (side effects) and polypharmacy (including both CV and non-CV medications) result in complexity that limits patients' ability to understand and adhere to treatment regimens.13 Indeed, in the STRONG HF sub-study more side effects (possibly explaining why adherence was lower to carvedilol than enalapril) and being assigned to a study arm where fewer visits were performed was associated with more non-adherence.7 Coping strategies, that is, how patients manage the stress stemming from their perception of a life situation, may affect adherence. Problem-solving and emotion-focused strategies were positively associated, and avoidance negatively associated, with adherence.14, 15 Not surprisingly, data also suggests that medication adherence is associated with health-related quality of life in patients with HF.16 Optimism was also identified as a factor influencing patients' attitude toward healthcare that may be of importance when addressing non-adherence.17 But how can we improve adherence? What approaches are available to increase adherence and to improve patients' quality of life and outcomes? A systematic review of 55 randomized controlled trials found that adherence interventions in general improved medication adherence in 10% of HF patients in 24 studies.18 Any adherence intervention was associated with an overall 2% absolute reduction in 1-year mortality (17 studies), and a 10% absolute reduction in 1-year admission risk (11 studies). The effects of non-adherence are especially important in patients with HF where even a short-term omission of HF medications (i.e. 48 h) could lead to worsening congestion, measured biochemically or by echocardiography.19 What interventions should we use to improve adherence? Simply implementing technology (such as apps) is unlikely to improve adherence by itself. A systematic review of nine randomized controlled trials that used technology to improve medication adherence in HF patients found that in most studies, adherence was not improved by technological interventions alone.20 A systematic review and meta-analysis of 57 studies implementing diverse system-wide and patient-orientated adherence improvement methods found that HF medication adherence interventions were associated with a lower risk of mortality (relative risk 0.89) and hospital readmission (odds ratio 0.79) (Figure 1).21 The interventions most likely to improve adherence include: (1) patient education, (2) medication regimen management, (3) clinical pharmacists' intervention, (4) reminders, and (5) cognitive behavioural therapy.21, 22 Recent observations suggest that getting patients on target or highest tolerated doses of renin–angiotensin system inhibitors, beta-blockers, mineralocorticoid receptor antagonists, and sodium–glucose cotransporter inhibitors at or shortly after discharge from admission for AHF can prolong lives and reduce HF readmissions, while improving quality of life,23-25 a strategy which was recently incorporated into the European Society of Cardiology guidelines for HF management.26 But patients need to take these medications if they are to be effective. Given the results of the STRONG-HF adherence sub-study,7 if indeed one-third of patients in STRONG-HF did not adhere to the triple medication regimen (despite no cost barriers), then the real treatment effect of rapid GDMT up-titration would be expected to be closer to a 50% risk reduction to explain an observed risk ratio of 0.65 in the overall study. As GDMT is effective in improving the outcomes and quality of life of patients with AHF, mitigating barriers to adherence to improve GDMT utilization is the most pressing issue in the improvement of AHF outcomes. Adherence interventions have been shown to increase medication adherence among HF patients and, possibly, lead to reduced hospital admissions and mortality. Evidence is mounting to support their routine incorporation in the care of AHF patients. Further research to identify durable approaches to improve medication adherence, and consequently outcomes, should be prioritized. Conflict of interest: G.C., B.A.D., and K.T. are employees of Momentum Research, which has received research grants from Abbott Laboratories, Amgen, Cirius Therapeutics, Corteria Pharmaceuticals, Heart Initiative, Sanofi, Windtree Therapeutics, and Xylocor Therapeutics and are the directors of the Heart Initiative, a nonprofit organization. K.F.A. has received research grants from Amgen, AstraZeneca, Bayer, Bristol-Myers Squibb Company, Boehringer Ingelheim Pharmaceuticals Inc, Cardurion Pharmaceuticals, Lilly USA, LivaNova USA Inc, Merck, Novartis, Otsuka and Pfizer, as well as consultant fees from Amgen, AstraZeneca, Bayer, Bristol-Myers Squibb Company, Boehringer Ingelheim Pharmaceuticals Inc, Cardurion Pharmaceuticals, Lilly USA, LivaNova USA Inc, Merck, Novartis, Otsuka and Pfizer. A.P.A. has received research grants from the National Heart, Lung, and Blood Institute (K23HL150159), the American Heart Association (Second Century Early Faculty Independence Award), The Permanente Medical Group, Northern California Community Benefits Programs, Garfield Memorial Fund, Abbott Laboratories, Amarin Pharma, Inc., Edwards Lifesciences LLC, Esperion Therapeutics, Inc., and Novartis. A.S.B. reports honorarium fees from Sanofi and is supported by grant funding from the National Heart, Lung and Blood, Institute and the American College of Cardiology. M.M.G. is a scientific advisor and has received research support from NIH/NHLBI. J.L.G. receives consultancy fees from Alnylam, AstraZeneca, Pfizer, Eidos/BridgeBio, and Sarepta; he is supported by the Texas Health Resources Clinical Scholarship, Pfizer, Eidos/BridgeBio and the National, Blood, and Lung Institute (R01HL160892). A.L. is a speakers Bureau member for Abiomed, Lexicon, Novartis, and Zoll and performs contracted research for Merck. J.R.T. has research contacts with 3ive Labs, AstraZeneca, Bayer, Boehringer Ingelheim, Cardurion, Cytokinetics, EBR Systems, Edwards, Impulse Dynamics, Kaiser Permanente, LivaNova, Medtronic, Myovant, PCORI, RECARDIO, V-Wave and is a consultant for 3ive Labs, Arena, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers-Squibb, Cardurion, CorHepta, Cytokinetics, Daiichi-Sankyo, EBR Systems, Edwards, Impulse Dynamics, JuvLabs, Kaiser Permanente, Lilly, LivaNova, Medtronic, Myovant, Novartis, PCORI, Pfizer, RECARDIO, ReCor Medical, Regeneron, Reprieve, Tectonic, V-Wave, Verily, ViCardia, Windtree Therapeutics. D.L.B. discloses the following relationships – Advisory Board: Angiowave, Bayer, Boehringer Ingelheim, CellProthera, Cereno Scientific, Elsevier Practice Update Cardiology, High Enroll, Janssen, Level Ex, McKinsey, Medscape Cardiology, Merck, MyoKardia, NirvaMed, Novo Nordisk, PhaseBio, PLx Pharma, Stasys; Board of Directors: American Heart Association New York City, Angiowave (stock options), Bristol Myers Squibb (stock), DRS.LINQ (stock options), High Enroll (stock); Consultant: Broadview Ventures, Hims, SFJ, Youngene; Data Monitoring Committees: Acesion Pharma, Assistance Publique-Hôpitaux de Paris, Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute, for the PORTICO trial, funded by St. Jude Medical, now Abbott), Boston Scientific (Chair, PEITHO trial), Cleveland Clinic, Contego Medical (Chair, PERFORMANCE 2), Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine (for the ENVISAGE trial, funded by Daiichi Sankyo; for the ABILITY-DM trial, funded by Concept Medical; for ALLAY-HF, funded by Alleviant Medical), Novartis, Population Health Research Institute; Rutgers University (for the NIH-funded MINT Trial); Honoraria: American College of Cardiology (Senior Associate Editor, Clinical Trials and News, ACC.org; Chair, ACC Accreditation Oversight Committee), Arnold and Porter law firm (work related to Sanofi/Bristol-Myers Squibb clopidogrel litigation), Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute; RE-DUAL PCI clinical trial steering committee funded by Boehringer Ingelheim; AEGIS-II executive committee funded by CSL Behring), Belvoir Publications (Editor in Chief, Harvard Heart Letter), Canadian Medical and Surgical Knowledge Translation Research Group (clinical trial steering committees), CSL Behring (AHA lecture), Cowen and Company, Duke Clinical Research Institute (clinical trial steering committees, including for the PRONOUNCE trial, funded by Ferring Pharmaceuticals), HMP Global (Editor in Chief, Journal of Invasive Cardiology), Journal of the American College of Cardiology (Guest Editor; Associate Editor), K2P (Co-Chair, interdisciplinary curriculum), Level Ex, Medtelligence/ReachMD (CME steering committees), MJH Life Sciences, Oakstone CME (Course Director, Comprehensive Review of Interventional Cardiology), Piper Sandler, Population Health Research Institute (for the COMPASS operations committee, publications committee, steering committee, and USA national co-leader, funded by Bayer), WebMD (CME steering committees), Wiley (steering committee); Other: Clinical Cardiology (Deputy Editor); Patent: Sotagliflozin (named on a patent for sotagliflozin assigned to Brigham and Women's Hospital who assigned to Lexicon; neither I nor Brigham and Women's Hospital receive any income from this patent); Research Funding: Abbott, Acesion Pharma, Afimmune, Aker Biomarine, Alnylam, Amarin, Amgen, AstraZeneca, Bayer, Beren, Boehringer Ingelheim, Boston Scientific, Bristol-Myers Squibb, Cardax, CellProthera, Cereno Scientific, Chiesi, CinCor, Cleerly, CSL Behring, Eisai, Ethicon, Faraday Pharmaceuticals, Ferring Pharmaceuticals, Forest Laboratories, Fractyl, Garmin, HLS Therapeutics, Idorsia, Ironwood, Ischemix, Janssen, Javelin, Lexicon, Lilly, Medtronic, Merck, Moderna, MyoKardia, NirvaMed, Novartis, Novo Nordisk, Otsuka, Owkin, Pfizer, PhaseBio, PLx Pharma, Recardio, Regeneron, Reid Hoffman Foundation, Roche, Sanofi, Stasys, Synaptic, The Medicines Company, Youngene, 89Bio; Royalties: Elsevier (Editor, Braunwald's Heart Disease); Site Co-Investigator: Abbott, Biotronik, Boston Scientific, CSI, Endotronix, St. Jude Medical (now Abbott), Philips, SpectraWAVE, Svelte, Vascular Solutions; Trustee: American College of Cardiology; Unfunded Research: FlowCo. All other authors have nothing to disclose.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.007
Open science0.0010.003
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0170.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.268
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2023
Admission routes1
Has abstractyes

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