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COMMENTARY

Jan 10, 2025 This Week in Cardiology Podcast

John M. Mandrola, MD

Disclosures

January 10, 2025

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Please note that the text below is not a full transcript and has not been copyedited. For more insight and commentary on these stories, subscribe to the This Week in Cardiology podcast, download the Medscape app or subscribe on Apple Podcasts, Spotify, or your preferred podcast provider. This podcast is intended for healthcare professionals only.

In This Week’s Podcast

For the week ending January 10, 2025, John Mandrola, MD, comments on the following news and features storiesAlcohol (ETOH) and atrial fibrillation (AF), GLP-1 agonist trial representativeness, diagnosing coronary artery disease (CAD), changing stroke rates in patients with AF, and blanking periods after AF ablation.

Surgeon General Causes Hoopla Regarding ETOH Warning

While we were all on holiday, the US Surgeon General made news by warning about the risks of ETOH consumption. The specifics of the note was a “direct” link between ETOH and increased cancer risk.

Alcohol consumption is the third leading preventable cause of cancer in the United States, after tobacco and obesity, increasing risk for at least seven types of cancer. While scientific evidence for this connection has been growing over the past four decades, less than half of Americans recognize it as a risk factor for cancer. For breast cancer specifically, 16.4% of total breast cancer cases are attributable to alcohol consumption.

A robust online discussion then ensued, specifically on X. Economist and professional explainer Emily Oster tried to sort it out in 1200 words. Her post and others went to a big Lancet meta-analysis published in 2018, “Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.”

I cite this paper because it is a classic example wherein combining hundreds of biased studies do not remove bias.

The authors attempt to describe the population attributable risk of ETOH. In other words, what would be the effect on mortality if ETOH was removed from the population.

Their methods were complex, including nearly 700 data sources of individual and population-level ETOH consumption, plus nearly 600 observational studies.

The authors then made correlations between ETOH intake or non-intake and 23 specific health outcomes.

The main thing to say about this paper involves the graphs. Gosh did they make impressive-looking graphs.

Figure 4, for instance, plots standard drinks per day on the X-axis and relative risk on the Y-axis. Each observational study is a point. They draw a correlation line, but it looks like noise.

The authors even list their limitations:

  • Time course is only a year. People change their intake patterns.

  • People estimate their own use; any doctor knows this is ridiculous.

  • People differ in other characteristics that are associated with drinking. No amount of adjustment fixes this.

  • Combining all these biased studies does not remove the bias. You could combine 1000 observational studies. It doesn’t matter. In fact, that is perhaps the most important lesson I have learned about observational studies: the number of individuals does not fix bias. Unlike randomized controlled trials (RCTs), where large numbers of events help reduce uncertainty, in biased observational studies, big numbers do nothing to remove bias.

As far as ETOH and health go, observational studies can’t really sort out general health correlations.

We can probably say two things: don’t start drinking ETOH for health; the French paradox is also an exercise in confounding. Second: avoid doses of ETOH that lead to drunkenness.

That said I don’t agree with the Surgeon General’s warning. The observational studies are not definitive enough for general health warnings.

One thing we can say, however, is that we have good data on ETOH and AF.

Bradford Hill would be happy because we have three lines of data supporting a causal role of ETOH in AF. There are population-level data showing associations between AF and ETOH intake, mechanistic data from the UCSF group showing ETOH has pro-arrhythmic effects in the atrium, and Alex Voskobonick’s RCT showing that patients randomly assigned to ETOH abstinence had less AF than those who did not abstain.

If patients have AF, I tell them about the data on ETOH. I state that I am not a preacher, just a teacher and evidence-based advisor. The data on ETOH and AF is exemplary and patients ought to know about it.

GLP-1 Agonists for Obesity : Trial vs Real World

JAMA-Internal Medicine published a neat research letter from Drs. Bessette and Anderson from the University of Pittsburgh.

Their question was, how generalizable are the clinical trials of GLP-1 drugs when used for weight loss in non-diabetic patients?

I like the concept because applying clinical trials is one of the toughest jobs for the modern clinician. In fact, I find the misapplication of trial data to patients not in trials to be one of the most common mistakes in evidence-based medicine.

It turns out that the weight loss trials have numerous exclusion criteria such as chronic medical and psychiatric conditions.

  • They took a representative sample from a National Health and Nutrition Examination Survey (NHANES) sample of about 9000 patients with obesity.

  • They then applied inclusion/exclusion criteria from the trials. For each medication, they calculated the proportion of individuals meeting the US Food and Drug Administration (FDA) label indication who also met trial exclusion criteria.

  • Of the approximately 9000 non-diabetic patients with obesity in their sample about 90% met FDA label criteria.

  • But of those “eligible,” approximately one in three patients also met exclusion criteria. The proportion meeting exclusion criteria was higher in adults older than 60 years.

Major depressive disorder, malignant neoplasms, liver disease (tirzepatide only), and uncontrolled hypertension were the most common exclusion criteria.

Comments. I cover this paper for two reasons. One is the specific nature of GLP-1 prescribing, which many of us will soon be doing because obesity surely is (or will become) a cardiac disease.

But the main reason I cover it is that trial generalizability is a core concept in evidence translation.

It’s important to think not only about who was excluded from trials, but also who was included.

For example, we are frequently asked to consider implanted cardioverter-defibrillators (ICDs) in patients with low ejection fractions (EFs) and heart failure (HF), who also have advanced kidney disease. Advanced severe chronic kidney disease (CKD) was an exclusion criterion for the clinical trials. What’s more, subgroup analyses find that any degree of CKD associates with no ICD benefit, likely because of competing risks and increased treatment-related harm. Therefore, I think it is a mistake to apply trial-level evidence for ICD benefit to patients with advanced CKD.

But it is also true that not all trial exclusion criteria are created equally.

For instance, in the ICD trials, patients were excluded based on timing — 90 days from revascularization or 40 days post myocardial infarction (MI). However, we now have at least some evidence that some patients with HF and low EF who received an ICD within this time frame can still benefit from the ICD. The Heart Rhythm Society, American College of Cardiology, and American Heart Association published a document outlining ICD implantation in patients not well represented in clinical trials. For instance, consider a patient who previously qualified for an ICD based on low EF who gets a percutaneous coronary intervention of a distal circumflex artery, which will not change the EF. This patient meets exclusion criteria but likely gets the same benefit. 

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