Introduction and Context
Heart failure (HF) has emerged as one of the most significant public health challenges of the 21st century. Despite advancements in the treatment of acute coronary syndromes and hypertension, the prevalence of heart failure continues to rise, driven by an aging population and the increasing prevalence of obesity and type 2 diabetes mellitus (T2DM). Traditionally, cardiovascular risk assessment tools, such as the original SCORE and the more recent SCORE2/SCORE2-OP models, have focused primarily on predicting atherosclerotic cardiovascular disease (ASCVD), including myocardial infarction and stroke. However, heart failure often follows a distinct pathophysiological pathway, frequently occurring in the absence of prior clinical ASCVD.
The development of the SCORE2-HF model represents a pivotal shift in preventive cardiology. By shifting the focus toward the early identification of individuals at high risk for incident heart failure, the European Society of Cardiology’s (ESC) Cardiovascular Risk Collaboration (CRC) Unit aims to provide clinicians with a tool that can guide more aggressive primary prevention strategies. This consensus-driven model is particularly timely as new therapies, such as SGLT2 inhibitors and GLP-1 receptor agonists, have demonstrated significant benefits in preventing heart failure hospitalization in high-risk patients.
New Guideline Highlights
The SCORE2-HF model is not merely a single-algorithm tool but a comprehensive framework designed to estimate both 10-year and 30-year risks of incident heart failure in adults over the age of 40 who do not have a prior diagnosis of cardiovascular disease.
Key takeaways for clinicians include:
- Multi-Factorial Assessment: Unlike older models, SCORE2-HF integrates Body Mass Index (BMI), kidney function (eGFR), and detailed diabetes metrics (HbA1c and age at diagnosis) as core predictors.
- Regional Customization: Recognizing the vast differences in HF incidence across Europe, the model provides recalibrated risk estimates for four distinct European risk regions (Low, Moderate, High, and Very High).
- Long-Term Perspective: By offering a 30-year risk estimation, the model helps identify younger individuals with a low 10-year risk but an exceptionally high lifetime risk, allowing for earlier lifestyle and pharmacological interventions.
Updated Recommendations and Key Changes
The transition from standard ASCVD risk models to the SCORE2-HF framework marks several critical updates in clinical practice. The primary reason for this update is the clinical gap where patients at low risk for stroke or heart attack were nonetheless developing debilitating heart failure.
| Feature | Previous Models (SCORE2/ASCVD) | SCORE2-HF Model |
| :— | :— | :— |
| **Primary Outcome** | Myocardial Infarction, Stroke, CV Death | Incident Heart Failure (Hospitalization or Death) |
| **Inclusion of BMI** | Often optional or not included | Essential core variable for HF prediction |
| **Kidney Function** | Limited integration | eGFR integrated as a primary risk driver |
| **Diabetes Detail** | Binary (Yes/No) | Includes HbA1c levels and age at T2DM diagnosis |
| **Risk Windows** | Primarily 10-year | 10-year and 30-year (Lifetime) risk |
These changes are driven by robust evidence from 25 prospective cohorts involving over 600,000 individuals. The data highlights that while cholesterol levels are paramount for atherosclerotic risk, factors like obesity and renal dysfunction are much stronger predictors of heart failure.
Topic-by-Topic Recommendations
1. Diagnostic Criteria and Risk Variables
Clinicians should utilize the following specific variables when inputting data into the SCORE2-HF calculator:
- Demographics: Age and sex-specific models are utilized.
- Clinical Metrics: Systolic blood pressure (SBP) and antihypertensive treatment status.
- Metabolic Factors: Smoking status and Body Mass Index (BMI).
- Renal Function: Estimated Glomerular Filtration Rate (eGFR) is crucial, as heart failure and chronic kidney disease are deeply intertwined.
- Diabetes Stratification: If T2DM is present, the clinician must input the HbA1c level and the patient’s age at the time of diagnosis.
2. Regional Risk Stratification
The model categorizes European countries into four risk regions based on WHO statistics. A 70-year-old man in a Low-risk region with multiple risk factors may have a 24% 10-year risk, whereas the same profile in a Very High-risk region could result in a staggering 59% risk. Clinicians must ensure they are using the chart calibrated for their specific geographic population.
3. Clinical Pathways and Staging
Individuals identified as “High Risk” by SCORE2-HF should undergo further screening for structural heart disease (Stage B Heart Failure), even if asymptomatic. This includes:
- Natriuretic peptide testing (BNP or NT-proBNP).
- Echocardiography to assess left ventricular hypertrophy or diastolic dysfunction.
- Aggressive management of SBP and weight.
A Patient Vignette: Applying the Model
Consider “James,” a 56-year-old male living in a Moderate-risk European region. James is a non-smoker with a BMI of 32 kg/m², a systolic blood pressure of 145 mmHg (on medication), and an eGFR of 75 mL/min/1.73m². He was diagnosed with T2DM at age 50, and his current HbA1c is 7.5%.
Using traditional ASCVD models, James might appear to have a moderate risk of heart attack. However, when the SCORE2-HF model is applied, his 10-year risk of heart failure is significantly elevated due to the combination of his BMI and kidney function. This realization prompts his physician to not only manage his lipids but also to initiate an SGLT2 inhibitor, which is specifically recommended for heart failure prevention in diabetic patients with these risk profiles.
Expert Commentary and Insights
The SCORE2 Writing Group emphasized that the predictive power of this model (C-indices up to 0.87 in validation cohorts) significantly outperforms generic tools. Experts suggest that the inclusion of BMI and eGFR is the most vital improvement, as these factors are often the “silent drivers” of heart failure in the general population.
Controversy remains regarding the frequency of re-screening. While the consensus suggests re-calculating risk every 5 years for those at low risk, some experts argue that for patients with borderline metabolic health, annual assessments are necessary to catch the rapid progression from Stage A (at risk) to Stage B (pre-heart failure).
Future trends likely involve the integration of this model into Electronic Health Records (EHR) to provide automated alerts to primary care physicians when a patient’s risk threshold is crossed. There is also ongoing research into adding biomarkers like high-sensitivity troponin to further refine the model’s accuracy.
Practical Implications
For the healthcare system, the implementation of SCORE2-HF allows for a “precision prevention” approach. By identifying those most likely to develop heart failure, healthcare providers can allocate resources—such as specialized weight management clinics and advanced pharmaceutical therapies—to those who will benefit most. For the patient, it provides a clearer picture of their long-term health trajectory, often serving as a powerful motivator for lifestyle changes that traditional risk scores might have overlooked.
References
1. SCORE2 Writing Group, European Society of Cardiology’s Cardiovascular Risk Collaboration (CRC) Unit. Prediction of incident heart failure in individuals without prior cardiovascular disease: the SCORE2-HF risk model. European Heart Journal. 2026-03-11. PMID: 41810943.
2. McDonagh TA, et al. 2023 Focused Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. European Heart Journal. 2023;44(37):3627-3639.
3. Visseren FLJ, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. European Heart Journal. 2021;42(34):3227-3337.
4. Heidenreich PA, et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Journal of the American College of Cardiology. 2022;79(17):e263-e421.
