Another (Louder) Call to Improve the Care We Provide Heart Failure Patients

I am always taken aback when I recommend a switch to sacubitril/valsartan in a patient with heart failure with reduced ejection fraction (HFrEF) and the response is “my patient feels fine”. This is a common response and certainly not a good enough reason to not optimize guideline directed medical therapy (GDMT) in patients with HFrEF. Optimization of GDMT in HFrEF, known to improve morbidity and mortality (1,2), is dismal. The Change the Management of Patients with Heart Failure (CHAMP-HF) registry included patients in the United States with chronic HFrEF receiving at least one oral medication for management of HF and showed >25% of eligible patients are not prescribed angiotensin converting enzyme inhibitor/angiotensin receptor blocker/angiotensin receptor neprilysin inhibitor, >33% are not prescribed a beta blocker, >50% are not prescribed a mineralocorticoid receptor antagonist. Remarkably, even among those receiving GDMT fewer than 25% are prescribed target doses and only 1% of eligible patients are simultaneously on target doses of all 3 classes of GDMT (3,4).

The mechanisms for suboptimal prescription of GDMT in HFrEF are complex and undertreatment is even more evident among women, minority patient populations, and patients from economically disadvantaged backgrounds, among others. Cost is certainly an issue, especially with more novel HF therapies and co-pay assistance programs are not always available to our most vulnerable patients. There are not enough HF cardiologists to take care of the continuously increasing population of HF patients and therefore, optimization of GDMT needs to be done by general cardiologists and primary care clinicians as well. We should also become creative and use telemedicine to optimize GDMT more efficiently. We do our patients a disservice by not optimizing GDMT that improves HF morbidity and mortality.

And just as optimization of GDMT is not ideal, neither is our evaluation of etiology of HF. Optimization of GDMT and determination of etiology of HF whose management may change disease trajectory should be undertaken in all patients with new-onset HF. This begins with a fundamental understanding of the various etiologies of HF, the laboratory and imaging testing needed, and the best treatment strategy for the underlying etiology discovered- if any (cue, “idiopathic” cardiomyopathy). O’Connor and colleagues’ observational cohort study from the Get With The Guidelines- Heart Failure (GWTG-HF) registry demonstrates the need to improve the testing we perform to exclude coronary artery disease (CAD) as the underlying etiology of new-onset HF.4

Why is this important? Well, of course for treatment, which involves deciding whether medical therapy (aspirin, statins) or revascularization (surgical or percutaneous) is a more optimal strategy. And most important to improve disease trajectory as continued ischemia will lead to worsening HF. O’Connor and colleagues found that the majority of  17,185 patients hospitalized for new-onset HF did not receive testing for CAD either during the hospitalization or in the 90 days before and after, despite data demonstrating that 60% (!!!) of HF patients have concomitant significant CAD.4 And consistent with disparities I mentioned earlier regarding the undertreatment of women with GDMT, men were more likely to be tested for CAD.

Diagnosing and treating CAD provides an opportunity to discuss risk factor modification with patients such as smoking cessation, diabetes control, exercise, healthy diets etc.… to further mitigate future risk. The importance of optimization of GDMT in patients with HFrEF cannot be understated and analogous to this, is the importance of examining the underlying etiology of HF in patients with new-onset HF with preserved, borderline, or reduced EF to improve disease trajectory. Furthermore, inequities in both aspects of the care of HF patients in terms of identification of etiology and optimization of GDMT, must be addressed on a national level. We have plenty of data illustrating suboptimal optimization of GDMT in those with established HFrEF and suboptimal testing for CAD in those with new-onset HF. The next steps are understanding the mechanisms and implementing strategies to improve care. The need for this is critical to reduce morbidity and mortality in all HF patients.


  1. Yancy CW, Jessup M, Bozkurt B et al. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation 2017;137.
  2. Yancy CW, Januzzi JL, Allen LA et al. 2017 ACC Expert Consensus Decision Pathway for Optimization of Heart Failure Treatment: Answers to 10 Pivotal Issues About Heart Failure With Reduced Ejection Fraction. Journal of the American College of Cardiology 2017.
  3. Greene SJ, Butler J, Albert NM et al. Contemporary Utilization and Dosing of Guideline-Directed Medical Therapy for Heart Failure with Reduced Ejection Fraction: From the CHAMP-HF Registry. Journal of the American College of Cardiology 2018.
  4. O’Connor, Kyle D., et al. “Testing for Coronary Artery Disease in Older Patients With New-Onset Heart Failure.” Circulation: Heart Failure, vol. 13, no. 4, 2020, doi:10.1161/circheartfailure.120.006963.

“The views, opinions and positions expressed within this blog are those of the author(s) alone and do not represent those of the American Heart Association. The accuracy, completeness and validity of any statements made within this article are not guaranteed. We accept no liability for any errors, omissions or representations. The copyright of this content belongs to the author and any liability with regards to infringement of intellectual property rights remains with them. The Early Career Voice blog is not intended to provide medical advice or treatment. Only your healthcare provider can provide that. The American Heart Association recommends that you consult your healthcare provider regarding your personal health matters. If you think you are having a heart attack, stroke or another emergency, please call 911 immediately.”

hidden Coronary Slow Flow Phenomenon: Myth or Fact?

Coronary Slow Flow Phenomenon: Myth or Fact?

One of the most challenging clinical scenarios to a cardiologist is the patient presenting with symptoms suggestive of obstructive coronary artery disease (i.e. angina), in whom coronary angiography reveals patent coronary vessels. Due to the seemingly ‘normal’ arteries, current clinical practice tends to underestimate the impact of these presentations, but there are subsequent difficulties in their management. To many cardiologists, angina in the absence of CAD is a myth rather than a fact — “what you don’t know, you don’t miss.” One such presentation often being missed or ignored is the “coronary slow flow phenomenon.” A classic example of slow flow angiogram is shown here.


What is coronary slow flow?

Nearly 50 years ago, Tambe and colleagues1 initially described this angiographic entity in patients with angina symptoms where they noted the injected contrast during coronary angiography moved very slowly through the coronary arteries, and aptly named “coronary slow flow phenomenon.” The prevalence is estimated at approximately 1-7% of elective angiograms2,3. The condition was largely neglected until Professor John Beltrame identified the distinct clinical features associated with this intriguing entity and thus concluded the coronary slow flow phenomenon was a new coronary disorder rather than angiographic curiosity. Evidence suggests that the coronary slow flow phenomenon leads to clinical manifestations of ischemia, arrhythmias, acute coronary syndromes and even sudden cardiac death.


How is coronary slow flow diagnosed?

Coronary slow flow phenomenon is usually identified subjectively by visual judgment.

  • Thrombolysis in myocardial infarction (TIMI) flow grade reflects the speed and completeness of the passage of the injected contrast through the coronary tree. In the setting of coronary slow flow, diagnosis can be made on the basis of TIMI 2 flow grade (ie: requiring ≥ 3 beats to opacify the vessel)4.
  • Corrected TIMI frame count (CTFC) facilitates the standardization of TIMI flow grades and flow assessment. It represents the number of cine-frames required for contrast to first reach standard distal coronary landmarks. TIMI frame count > 27 frames have been frequently used to diagnose slow flow5.


What is the underlying cause of this presentation?

The coronary circulation consists of epicardial vessels and microvasculature. In the absence of epicardial stenosis, microvascular dysfunction may explain the pathophysiology of coronary slow flow phenomenon. Supporting this hypothesis, biopsy studies have revealed structural microvascular coronary abnormalities in slow flow patients. Reduced endothelium dependent flow-mediated dilatation (FMD) of the brachial artery has been detected in patients with coronary slow flow phenomenon, suggesting that endothelial dysfunction is implicated in the aetiology. However, there are still multiple questions and controversies regarding the underlying pathophysiology and whether this pathology is limited to coronary arteries or is a manifestation of systemic vascular or endothelial disease remains to be answered.


What is the medical management for coronary slow flow phenomenon?

Although coronary slow flow phenomenon patients have good overall prognosis, ongoing anginal episodes results in considerable impairment of their quality of life. Professor Beltrame has been long fighting the battle of identifying appropriate management for these patients, in particular, therapies that limiting the anginal episodes. His group has shown dipyridamole and mibefradil has some benefit in this setting, yet larger studies are required to confirm these findings. Currently available anti-anginal agents are of limited clinical value. To date, no large trial testing pharmacological approaches has been conducted, and the evidence available is derived from small studies, some with inhomogeneous inclusion criteria.


So, is it a myth or fact?

Over the past 50 years, the coronary slow flow phenomenon has evolved from a curious ‘myth’ to an identified coronary disease entity. Despite this progression of thinking, significant efforts are still required to unpack this intriguing condition, particularly in relation to effective therapies to improve symptoms and quality of life.



  1. Tambe AA, Demany MA, Zimmerman HA and Mascarenhas E. Angina pectoris and slow flow velocity of dye in coronary arteries–a new angiographic finding. Am Heart J. 1972;84:66-71.
  2. Beltrame JF, Limaye SB and Horowitz JD. The coronary slow flow phenomenon–a new coronary microvascular disorder. Cardiology. 2002;97:197-202.
  3. Hawkins BM, Stavrakis S, Rousan TA, Abu-Fadel M and Schechter E. Coronary Slow Flow– Prevalence and Clinical Correlations &ndash. Circulation Journal. 2012;76:936-942.
  4. Chesebro JH, Knatterud G, Roberts R, Borer J, Cohen LS, Dalen J, Dodge HT, Francis CK, Hillis D, Ludbrook P and et al. Thrombolysis in Myocardial Infarction (TIMI) Trial, Phase I: A comparison between intravenous tissue plasminogen activator and intravenous streptokinase. Clinical findings through hospital discharge. Circulation. 1987;76:142-54.
  5. Gibson CM, Cannon CP, Daley WL, Dodge JT, Jr., Alexander B, Jr., Marble SJ, McCabe CH, Raymond L, Fortin T, Poole WK and Braunwald E. TIMI frame count: a quantitative method of assessing coronary artery flow. Circulation. 1996;93:879-88.




Shared Decision Making In Cardiac Care

Illustration of seniors with life insurance

Shared decision making (SDM) is an approach both patients and clinicians can use to improve patient education and discussion in decision making. Decision aids are tools that promote SDM by improving patient-clinician communication about all treatment options and how the risks and benefits fit with their personal values and preferences. Today, we’ll chat about SDM in the context of cardiac care, with examples from atrial fibrillation and heart failure, as well as the entire CAD spectrum.

A Chronological Look at SDM
In 2012, Karen Sepucha reflected on shared decision-making and patient decision aids in an editorial for Circulation: Cardiovascular Quality and Outcomes. Two studies published in the same issue evaluated the impact of decision aids in urgent management of acute coronary syndrome (The Chest Pain Choice Decision Aid) and vascular access choice for coronary angiogram procedures. These studies reflect scenarios that are not traditionally thought to be amenable to SDM, but the results show that SDM in the emergency department and regarding technical treatment decisions, such as femoral versus radial access, had a positive impact on patient knowledge and decisional conflict. Sepucha’s concluding statement still rings true today: “Despite considerable evidence from many studies of decision aids, few of these tools are used routinely in practice.”

There are several challenges to using decision tools in practice. Decision aids should be brief and easily incorporated into the workflow of clinical practice, as well as easily accessible in the public domain. In 2015, CMS implemented a policy around healthcare for atrial fibrillation patients requiring all non-implanting physicians to use evidence-based decision tools and SDM practices. A 2017 article by Megan Coylewright and David Holmes in Circulation caution against this CMS mandate for SDM for patients with atrial fibrillation. The authors cite reasons reminiscent of Sepucha’s conclusions in 2012. Coylewright and Holmes point out that there is limited guidance on navigating SDM for patients with atrial fibrillation at risk for stroke. Specifically, while research consistently shows that “decision aids improve patient outcomes including knowledge, engagement, and satisfaction”, SDM has yet to become an integral part of clinical practice for a variety of reasons, both on the patient side and the clinician side. For example, barriers to use of SDM by physicians include insufficient training in the skillsets necessary to implement SDM, such as “inadequate assessment of patient preferences,” in addition to logistic challenges to implementation, whether at the institutional level, the lack of publicly available decision aids, or the time availability during patient-clinician interactions to thoroughly discuss patient preferences.

Atrial Fibrillation
Atrial fibrillation presents a particularly challenging case for optimizing treatment, due to low prescription, low adherence due to side effects and frequent testing, as well as increased risk of negative reactions due to polypharmacy. Thomson et al. found that atrial fibrillation patients at risk of stroke may deicide to forgo treatment with anticoagulants to avoid the medications’ adverse effects, with the knowledge that their stroke risk later on increased. However, professional guidelines on treatment of atrial fibrillation promote SDM and use of evidence-based decision tools while recognizing that inviting patients to participate in care decisions can be challenging to physicians.

A recent systematic review, “Availability of Patient Decision Aids for Stroke Prevention in Atrial Fibrillation” by O’Neill and colleagues summarize the current state of decision aids for patients with atrial fibrillation at risk for stroke and their treating physicians. The current landscape of pharmacologic therapy for stroke reduction includes multiple DOACs, implantable LAA closure devices, aspirin, and warfarin – each treatment with benefits and trade-offs that must be considered “in the context of individual values and preferences and willingness to adhere to therapy”. Many of the decision aids in the studies reviewed by O’Neill et al. did not display all therapeutic choices, were delivered to patients primarily by nonphysicians, and were not publicly available.

Depression and CAD
SDM and decision tools pair nicely with assessment of depression in heart disease patients. In a recent paper, “Identifying and Managing Depression in Patients with CAD,” Aimee Salzer Pragle and Susan Salashor discuss the epidemiology and risk factors, clinical presentation, assessment tools, and treatment options for depression in patients with heart disease. In a 2011 review, Christopher Celano and Jeff Huffman estimated that 20-40% of patients with CAD may suffer from depression, and despite the availability of screening tools brief enough for clinical practice (such as the Patient Health Questionnaire-9 or -2), depression often goes unrecognized in cardiac care. Depression in patients with CAD is associated with progression of heart disease, poor quality of life and physical functioning, repeat cardiac events, and 2-2.5 fold increased risk of mortality [link]. Risk factors for depression in patients with CAD include “younger age, female sex, a history of depression, social isolation, previous cardiac events, and diabetes” and depression can increase risk of suicide, homicide, and substance abuse (which can exacerbate cardiac symptoms and promote further degeneration).

Innovative Frameworks – A Segue to Implementing SDM?
From the clinician side, disease-specific evaluation frameworks inclusive of all relevant aspects of a patient’s health provide another avenue towards optimizing patient-clinician communication. For example, in a recent paper, Gorodeski et al. present a multi-domain framework for managing heart failure and the associated questionnaires and tools that go with each task.

Gorodeski image
These tools include the Mini Nutritional Assessment Short Form (MNA-SF), the Mini-Cog and PHQ-2 (Patient Health Questionnaire) for mental and emotional state, gait speed, timed Up and Go test, FRAIL questionnaire, and ADL/IADL discussion for assessing physical function, as well as inquiries to social support at home, adaptable and safe environment plans, access to nutrition and transportation, as well as abilities and support in medication management (Table 1, Gorodeski et al).

 Does your department or institution use shared decision making or decision aids in practice?

 What barriers to implementation do you see in incorporating these tools into everyday patient interactions?

SDM and Decision Aids Resources
The Ottawa Hospital Research Institute hosts an up-to-date A to Z inventory of patient decision aids, and includes tools for angina, atrial fibrillation, CABG and other cardiac procedures, blood pressure medications, peripheral artery disease surgery, type 2 diabetes, depression, kidney disease and dialysis, and many others. Many of the tools geared towards cardiac care were in the form of informative articles for patients to read, or interactive tutorial-style pages for patients. None that I found were visual or brief enough to be used in clinical practice. Additionally, purely text-based “decision aids” are not useful for low-literacy populations, and online-only resources are not easily accessible for patients who are less tech-savvy. However, the Heart to Heart tool developed by the University of North Carolina General Internal Medicine department provides a user-friendly interface, even for the less technologically-savvy patients, that guides the user through several slides on how to join the discussion of their own treatment with their doctor. Similarly, the AHA Rise above Heart Failure initiative has two resources heart failure patients and physicians might find particularly useful: a guide to overcoming barriers to shared-decision making and a list of questions to ask the doctor. If you are a physician, reviewing these materials provides you with the guidance of what questions and topics to address during the clinic visit.

Quick links to select decision aids related to heart disease:


Bailey DeBarmore Headshot

Bailey DeBarmore is a cardiovascular epidemiology PhD student at the University of North Carolina at Chapel Hill. Her research focuses on diabetes, stroke, and heart failure. She tweets @BaileyDeBarmore and blogs at baileydebarmore.com. Find her on LinkedIn and Facebook.