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In This Week’s Podcast
For the week ending October 1, 2021, John Mandrola, MD comments on the following news and features stories.
CAC and Exercise
South Korean investigators have published a provocative paper on the association between coronary artery calcium (CAC) and physical activity. Since it involved a seeming paradox, it got a lot of attention.
This was a convenience sample from two hospitals of about 25,000 participants without coronary artery disease (CAD) who happened to have had two CAC scans. The authors used three groups based on self-reporting: Inactive (47%); moderately active (38%); and health-enhancing physical activity or HEPA (15%). HEPA corresponded to the equivalent of jogging 3.5 miles per day. The study looked at baseline CAC and change in CAC over time in the three groups.
But Before I tell you the results, let’s review three basic facts:
First fact: Heart disease is a leading killer because of coronary plaque rupture, which leads to a clot in the vessel and sudden occlusion, then myocardial infarction (MI) or arrhythmia. A basic tenet taught to medical learners, one that has stood the test of time, is that sudden plaque rupture is more likely in less obstructive, younger, less calcified lesions. The 90% calcified lesion is more likely to cause angina; it’s the 20% softer plaques that often cause sudden occlusion. This is why a person can pass a stress test and die the next week: a stress test only picks up 70% or greater lesions, but in general, it’s the non-ischemia-causing 10% to 20% soft friable plaques that are most likely to cause MI.
Second fact: CAC means exactly what you think: hardening of the arteries. It is a surrogate marker for advanced atherosclerosis of the coronary arteries. It can be calculated from a CT scan. But the presence of hardening of the arteries does not necessarily mean someone will have an event and the absence of hardening of the arteries does not inoculate someone from an event.
Third fact: Most, perhaps nearly all, the data on CAC use pertains to its use as a single baseline measurement to guide the use of statins. This study is interesting because it looks at progression of CAC.
The prospective cohort study had two major findings:
Physical activity was associated with a higher prevalence of CAC at baseline and with faster progression over follow-up.
The association was graded across levels of exercise. In other words, the CAC increased in those engaged in moderate activity compared with inactive people, but the increase in CAC was greater in those who reported HEPA.
I talked with my friend Andrew Foy from Penn State about this study, so I want to give him credit for helping me better understand this.
A major caveat pertains to the generalizability of data in a Korean population to North America. In Table 1: the average BMI of inactive folks was 24. The average HDL was 52 mg/dL. Is this a representative sample of the average inactive American? On the other hand, also noteworthy is that 35% of each of the categories were smokers. That seems a bit higher rate than we have here.
Another thing we should say is that these seem to be fairly robust observations. Despite the self-reporting nature of physical activity, the P-values are significant, and the graded association suggests a non-spurious association. Also suggestive is that other studies, cited by the authors, show similar associations. We know from numerous papers that marathon running has a strong correlation with CAC. This is likely a real observation. But we also know that there are oodles and oodles of papers associating exercise with lower rates of cardiac events.
Thus, the paradox here is that, if the group that exercised more had higher CAC levels at baseline and greater increases in CAC, does this mean exercise may have negative effects? Does exercise promote atherosclerosis and increase risk of heart disease?
No, of course; exercise does not elevate risk of coronary artery disease. It may promote CAC progression but that does not mean it increases the rate of cardiac events. This study did not report events, which is the only thing we care about.
To me, the observation that a clearly healthy activity, exercise, which nearly everyone agrees reduces future events, also associates with CAC progression weakens the strength of CAC and CAC progression as a surrogate marker of anything important. And it isn’t the only observation that makes me question CAC progression. Statins also reduce future cardiac events, but multiple studies find an association with statin use and CAC progression.
This study casts serious doubt on the use of serial CAC measures.
If exercise and statins increase CAC but clearly reduce events, measuring CAC more than once seems ill-advised. The main use of CAC is as one-time risk modifier for people who are on the fence about statin use.
Proponents of CAC say the incremental value of knowing CAC is useful—zero CAC portends super low-risk, and high CAC in an intermediate-risk patient may tip the balance to statins. My doubt over this strategy remains. No single risk factor should be taken in isolation. While this study mostly weakens the use of serial CAC, if you combine the associations of being an exerciser or statin-taker with CAC, one wonders whether measurements of this marker tell us much that is useful at all.
The New Syntax Score
The Journal of the American College of Cardiology (JACC) recently published a study looking to validate the newest iteration of the SYNTAX score. The issue at hand is the treatment of patients with severe multivessel or left main CAD. Is it a percutaneous coronary intervention (PCI) and stents or a coronary artery bypass graft (CABG)?
The original SYNTAX score was designed to measure and rank the anatomic complexity of CAD. Simply stated: if the disease was too complex, bypass surgery was better than PCI. Simple focal stenoses not near branches led to lower SYNTAX scores and PCI might be better. But the original score seemed to only predict survival or major adverse cardiac events (MACE) in those having PCI but not having CABG. So smart people thought, let’s incorporate more data points—seven clinical variables: age, creatinine clearance, left ventricular ejection fraction, unprotected left main CAD, peripheral vascular disease, sex, and chronic obstructive pulmonary disease. This seemed to do better.
But now the SYNTAX score proponents want to make it even better, by using data from the 10-year SYNTAX trial comparing PCI to CABG in patients with multivessel or left main coronary artery (LMCA) disease. The twist for this new score, called the SYNTAX II 2020 is that it discards the patient’s sex and adds diabetes and smoking.
Why am I talking about this? In medicine, randomized controlled trials of two therapies—PCI vs CABG, direct-acting oral anticoagulants (DOAC) vs warfarin, target temperature management vs standard care—give us average treatment effects. But within that group of randomized patients, there are patients who do better with one or the other treatments.
When you are in the clinic, trying to decide about your patient, you wonder if she is average, or for some reason, may do better with one of the treatments. Subgroup analyses are limited because they only look at one variable (say male or female, diabetes or no diabetes).
The SYNTAX II 2020 score is a worthy topic because it tries to personalize RCT data. By grouping together lots of characteristics, getting a score, and then seeing whether this score predicts risk and treatment effect, one tries to individualize data from trials.
This score could potentially be used by the Heart Team to help patients with multivessel CAD, or LMCA disease decide on CABG vs PCI. The idea of the Heart Team is that PCI docs are biased towards PCI and surgeons are biased towards CABG. But could the score function as a neutral Martian?
In November 2020, Lancet published the validation of the new SYNTAX II 2020 score. Last week, JACC published external validation of the new SYNTAX score using observational real-world data from Japan.
The short story as I see it is the new SYNTAX score, when applied to this real-world data, did okay at predicting 5-year mortality but for MACE, it “could not recommend a specific treatment with sufficient accuracy.” I worry that using such aa score is fraught.
When you look at MACE vs death, there are more MACE events than deaths. If the score were truly useful, why wouldn’t it predict a more frequently occurring outcome?
The C-statistics here were in the range of 0.7. Well, 0.5 is a coin-toss. So, for mortality, it’s better than a coin toss, but not much.
The editorial, by John Squires and Michael DiMaio, does a good job sounding a cautionary note about making too much out of this score. For example, the Japanese registry followed subjects for 5 years but the SYNTAX score II 2020 was derived to predict 10-year survival. Thus, longer-term validation in diverse populations is required before use of the SYNTAX score II 2020 can be recommended ubiquitously. The problem is that very few trials follow patients for a decade.
Another problem, this one covered in a letter in the Lancet from statistician Nick Freemantle and surgeon Domenico Pagano, was that SYNTAX score authors exerted bias in the choice of unrepresentative and suboptimal datasets for the creation of their new score. Freemantle and Pagano assert that the studies used to validate the score were older. Why not use the EXCEL trial, which had it in its 5-year results a significant mortality advantage to CABG?
I need to learn more about the details of the statistics used, but the quest to do better than the average effects from trials seems like a very very hard one. For if you can say, treatment A looked better than treatment B on average, but for the patient in front of me now, this score or my gut tells me that treatment B will be better, then what is the point of doing RCTs in the first place? Please let me know in the comments what I got wrong here.
Can Health and Obesity Go Together?
A French observational study presented at the Euro Diabetes meeting challenges the often-heard “metabolically healthy” obesity. I see this scenario often: A 350-lb patient comes to see me and tells me that the primary care doctor said all was good. Really? Your doc said all was good? And the patient says, yes, here, my labs are all normal, and so is my blood pressure.
The question is an important one. How large of a risk factor is obesity alone? Many patients who are obese also have metabolic conditions such as diabetes, hypertension, and abnormal lipids. We know these metabolic conditions predispose to future cardiac events, but what about obesity as an independent risk factor?
The French study, which was simultaneously published in the journal Obesity, Diabetes and Metabolism, looked at a 5-year period after discharge of patients with metabolically healthy obesity (MHO). Metabolically healthy was defined by the absence of diabetes (DM), hypertension (HTN), or hyperlipidemia (HL). The comparison group was discharged patients without obesity and DM, HTN, or HL.
The primary outcomes were cardiac events. This was a big study of almost 300,000 patients with obesity and of these, about one-third (roughly 90,000) had MHO. This was the active arm. Patients without obesity or metabolic conditions were the controls. This was a young group, age 49 to 50 years. In all, 30% were male.
The MHO had a statistically significant, age-adjusted, higher rate of new heart failure (HF) and atrial fibrillation (AF).
The MHO did not have an increased risk of MI, cardiovascular disease (CVD), or ischemic stroke.
Men had higher risks of HF and AF; women had lower risks, ranging from 50% to 1% lower.
Given the many caveats of a retrospective observational study using administrative data of hospitalized patients, the trend toward higher risks of HF and AF are highly plausible. The authors cite other studies which find similar results. I’ve co-authored a paper with the Penn State group led by Andrew Foy which also observed obesity as a risk factor for new AF in young people. I wouldn’t want to make any definitive conclusions from this paper, but I do think it helps to counter the notion that if obesity isn’t associated with DM, HTN, or HL, it should be brushed off as an unimportant health condition. The use of the phrase metabolically healthy risks minimizing the potential health consequences of obesity at a young age.
I see the role of clinicians as health advisors for our patients. It seems reasonable, therefore, to counsel patients with obesity that losing weight would independently reduce the risk of two pretty bad outcomes—HF and AF.
My approach is to say something like, ”I know you probably know this, but your weight is a health problem and as your advisor, I wanted to mention that trying to lose weight is super-important. I have some possible tips if you want to hear them.”
Now, back to that observation that MHO seems protective in women. Why would obesity be a risk factor in men but protective in women? Although I am no expert, I suspect there is a form of selection bias here. Specifically, a collider bias.
What do I mean? Remember that the sample is of patients discharged from the hospital. We are comparing non-obese metabolically healthy women to MHO women. What if there was “something else,” some other causal factor, that caused non-obese hospitalized women to have higher risk? Cancer, auto-immune diseases, etc.
A pro-tip is whenever you see a paper touting a paradox, such as obesity or smoking being protective, always think about collider bias. A classic paper on this shows that obesity looked protective for pediatric diabetes—a flawed finding based on selection bias.
FDA Investigating Watchman Complications
During the August 20th episode I covered an observational analysis of Watchman registry data that found that women had a significantly higher rate of inpatient complications. JAMA-Cardiology published the study, with Douglas Darden as first author. Women were more likely than men to experience a major adverse event (4.1% vs 2.0%, odds ratio 2.06; 95% CI, 1.82-2.34; P < .001).
FDA announced its investigation in a letter to healthcare providers. Literally everyone knows I am skeptical of percutaneous left atrial appendage (LAA) closure, but this is very concerning data.
Proponents will say the newer iteration of the device will be better. But that is pure speculation. Recall that one of the main uses of registry data is to investigate real-world complication rates. It’s also likely that the registry surely represents a low-estimate as it only captures in-hospital complications. That women were more than 2-fold more likely to have a major adverse event from the implant is vitally important for decision making.
It’s also plausible that women are at greater risk. Women have higher complication rates from many different procedures; cardiac surgery, and AF ablation, for example.
One of the things that concerns me about this procedure is its use in the real world. Registry data shows that most devices inserted by low-volume operators (and my anecdotal experience seeing what’s happening here locally) are being placed in older sicker patients. Remember friends: trials almost always represent a best-case scenario. When therapies are used in patients with multi-morbidities, it is likely outcomes will be not as good. And in Watchman’s case, the trial results were highly dubious to begin with.
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Cite this: Oct 1, 2021 This Week in Cardiology Podcast - Medscape - Oct 01, 2021.