ECG abnormalities are strongly associated with CVD outcomes in low-risk individuals using the PREVENT risk equation

This study demonstrates that ECG abnormalities are significantly associated with increased cardiovascular disease risk across all PREVENT risk categories, suggesting that incorporating ECG evaluation into risk assessment could help identify low-risk individuals who may benefit from more aggressive primary prevention strategies.

The Gist

Imagine your heart is like a car engine, and your doctor is the mechanic. For years, the standard way to check if your engine is at risk of breaking down was to look at the "dashboard gauges": your age, blood pressure, cholesterol, and whether you smoke. These are the PREVENT equations mentioned in the study. They give you a "risk score" telling you how likely you are to have a heart attack or stroke in the next 10 years.

However, there's a problem. Sometimes, a car looks perfect on the dashboard (low risk), but the engine has a hidden rattle or a loose wire that the gauges can't see. This study asks a simple question: What if we listen to the engine with a stethoscope (an ECG) to find those hidden problems, even if the dashboard says you're safe?

Here is the breakdown of the study in plain English:

1. The Big Idea: The "Hidden Rattle"

The researchers looked at nearly 20,000 people who had no history of heart disease. They calculated their risk using the standard "dashboard" method (the PREVENT score).

• The Surprise: They found that 43% of the people who were considered "low risk" by the dashboard actually had a "rattle" in their engine.
• The Rattle: This "rattle" was an abnormality on their resting Electrocardiogram (ECG). An ECG is a simple test where you stick stickers on your chest to see how your heart's electrical signals are firing.

2. The Experiment: Listening to the Heart

The team divided the participants into three groups based on their heart's electrical signals:

• Group A (The Smooth Operators): Perfectly normal ECGs.
• Group B (The Slight Hiccups): Minor electrical glitches (like a tiny skipped beat or a slight wobble in the signal).
• Group C (The Major Glitches): Serious electrical problems (like signs of past silent heart damage or major blockages).

They then waited about 13 years to see who actually had heart trouble.

3. The Results: The Dashboard Wasn't Enough

The results were eye-opening. Even among the people the dashboard labeled as "Low Risk":

• Those with Major Glitches (Group C) were almost twice as likely to have a heart attack or stroke compared to the Smooth Operators.
• Even those with Slight Hiccups (Group B) had a slightly higher risk than those with perfect signals.

The Analogy: Imagine two cars.

• Car 1 has a clean dashboard (Low Risk score) but a loose wire in the engine (Major ECG abnormality).
• Car 2 has a clean dashboard and a clean engine.
  This study found that Car 1 is much more likely to break down on the highway than Car 2, even though the dashboard said they were both fine.

4. Why This Matters

Currently, doctors generally do not recommend giving everyone a resting ECG just to check for heart disease. The guidelines say it's not worth the cost or the false alarms.

But this study suggests that for the "Low Risk" group, the ECG might be a secret weapon. It acts like a spotlight in a dark room.

• If you are "Low Risk" but have a Major ECG abnormality, you might actually be "Medium" or "High" risk.
• This could change how doctors treat you. Instead of just saying, "You're fine, keep eating your veggies," they might say, "Your dashboard looks okay, but your heart's electrical signal is warning us. Let's start medication or make stricter lifestyle changes just to be safe."

5. The Bottom Line

Think of the PREVENT score as a weather forecast based on satellite data. It's usually pretty good.
Think of the ECG as looking out the window.
Sometimes the satellite says "Sunny," but you look out the window and see dark storm clouds gathering (an ECG abnormality).

This study tells us that if you see those dark clouds (an ECG abnormality), you should take them seriously, even if the satellite forecast says it's a beautiful day. Adding the ECG to the standard check-up could help catch the people who are secretly at risk before it's too late.

In short: Don't ignore the "rattle" in your engine just because the dashboard says you're fine. A simple, cheap test (the ECG) might be the key to keeping you safe on the road.

Technical Summary

1. Problem Statement

Cardiovascular disease (CVD) remains the leading cause of death globally. While risk stratification tools like the 2023 PREVENT equations (Predicting Risk of Cardiovascular Disease Events) are standard for estimating 10-year CVD risk, they may fail to identify high-risk subgroups among individuals classified as "low-risk" (<7.5% 10-year risk).

• The Gap: Approximately 15% of patients experiencing acute myocardial infarction have no traditional risk factors. Current guidelines (e.g., USPSTF 2018) do not recommend resting ECG screening for asymptomatic low-risk adults due to insufficient evidence of benefit.
• The Hypothesis: The authors hypothesize that resting ECG abnormalities, even minor ones, serve as independent markers of subclinical cardiovascular pathology and can identify a subset of "low-risk" individuals who are actually at elevated risk for future CVD events, thereby offering incremental value to current risk stratification models.

2. Methodology

Study Design & Population:

• Data Source: Secondary analysis of the REGARDS (REasons for Geographic And Racial Differences in Stroke) prospective cohort.
• Cohort: 19,173 participants (mean age 63.7 years; 57.8% female; 40.2% Black) free of baseline CVD (stroke, MI, revascularization, or heart failure).
• Follow-up: Median of 13.3 years (through December 31, 2021).
• Exclusion: Participants with baseline CVD history or old MI on ECG were excluded.

Exposure Assessment (ECG):

• Classification: Baseline ECGs were centrally interpreted by trained analysts blinded to clinical data using the Minnesota Code (MC) system.
• Groups:
  1. Normal: No abnormalities.
  2. Any Minor Abnormality: Includes minor Q-QS waves, minor ST-T abnormalities, high R waves, incomplete RBBB, etc.
  3. Any Major Abnormality: Includes definite/probable MI, major ventricular conduction defects, major ST-T abnormalities, LVH with ST-T changes, major arrhythmias, etc. (Presence of a major abnormality overrides minor ones).

Risk Stratification:

• Participants were categorized into three risk strata using the 2023 PREVENT base equation:
  • Low Risk: <7.5% 10-year risk.
  • Intermediate Risk: 7.5% – 20%.
  • High Risk: >20%.

Outcomes:

• Primary Outcome: Incident CVD events, defined as a composite of fatal/non-fatal Coronary Heart Disease (CHD), Heart Failure (HF), Stroke, or CVD death.
• Adjudication: Events were rigorously adjudicated by experts using medical records, biomarkers, and imaging.

Statistical Analysis:

• Models: Cox proportional hazards regression models were used to calculate Hazard Ratios (HR) and 95% Confidence Intervals (CI).
• Adjustments: Models were adjusted for age, sex, and a comprehensive set of covariates (sociodemographics, behaviors, comorbidities, inflammatory markers) excluding variables already present in the PREVENT equation to avoid double-adjustment.
• Stratification: Analyses were stratified by PREVENT risk groups, age, sex, and race.
• Competing Risks: Fine and Gray methods were used to account for non-CVD death.
• Missing Data: Handled via multiple imputation by chained equations.

3. Key Results

Baseline Characteristics:

• ECG Distribution: 47.0% had normal ECGs, 44.0% had minor abnormalities, and 9.0% had major abnormalities.
• Risk Stratification: 39.4% were low-risk, 44.6% intermediate-risk, and 16.0% high-risk.
• Intersection: Among the low-risk (<7.5%) group, 43.6% had at least one ECG abnormality (38.6% minor, 5.0% major).

Incident CVD Events:

• Overall: 15.6% of the cohort experienced a CVD event.
• By ECG Status:
  • Normal ECG: 12.4% event rate.
  • Minor Abnormality: 17.0% event rate.
  • Major Abnormality: 25.4% event rate.

Hazard Ratios (Fully Adjusted Models):

• Overall Population:
  • Any Minor Abnormality: HR 1.19 (95% CI 1.10–1.29).
  • Any Major Abnormality: HR 1.53 (95% CI 1.36–1.72).
• Low-Risk Group (<7.5% PREVENT Score):
  • Major Abnormality: HR 1.87 (95% CI 1.34–2.62). This indicates nearly a doubling of risk for low-risk individuals with major ECG findings compared to those with normal ECGs.
  • Minor Abnormality: HR 1.13 (95% CI 0.93–1.37) – Not statistically significant in this specific subgroup.
• Intermediate/High-Risk Groups: Both minor and major abnormalities remained significant predictors, though the relative magnitude of risk for major abnormalities was most pronounced in the low-risk group.

4. Key Contributions

1. Validation in a US Cohort: This study validates recent findings (e.g., Yagi et al., 2024 in Japan) that ECG abnormalities predict CVD outcomes in a large, diverse, US-based population.
2. Reclassification of Low-Risk Patients: It demonstrates that a substantial proportion (43.6%) of individuals deemed "low-risk" by the modern PREVENT equation harbor ECG abnormalities, with major abnormalities conferring a significantly elevated risk (HR ~1.87).
3. Incremental Value of ECG: The findings suggest that adding ECG evaluation to the PREVENT equation could improve risk discrimination, specifically by identifying "hidden" high-risk individuals within the low-risk category who might otherwise be denied aggressive primary prevention therapies (e.g., statins).
4. Differentiation of Abnormality Severity: The study highlights that while major abnormalities are potent predictors across all risk groups, minor abnormalities show a more nuanced relationship, being significant in the overall and intermediate/high-risk populations but less distinct in the lowest-risk stratum.

5. Significance and Implications

• Clinical Practice: The results challenge the 2018 USPSTF recommendation against ECG screening in low-risk adults. The authors argue that ECGs could serve as a cost-effective, non-invasive "risk enhancer" to guide shared decision-making for preventive interventions in low-risk patients.
• Public Health: Given the aging US population and the rising burden of CVD, identifying subclinical disease in younger or lower-risk demographics is crucial. ECG abnormalities may represent markers of subclinical atherosclerosis or prior myocardial injury not captured by traditional risk factors.
• Future Directions: The authors suggest that integrating ECG data (potentially via AI-driven automated interpretation of Minnesota Codes) into risk calculators could refine primary prevention strategies.
• Limitations: The study is observational (cannot prove causality), lacks Latino representation, and relies on self-reported data for some covariates. However, the rigorous adjudication of outcomes and long follow-up period strengthen the validity of the associations.

Conclusion: ECG abnormalities are strongly associated with incident CVD events across all PREVENT risk strata. Most notably, major ECG abnormalities identify a high-risk subgroup within the "low-risk" population, supporting the potential utility of routine ECG screening to refine cardiovascular risk assessment and optimize primary prevention.