Cardiology |
47 years of experience
The Mount Sinai Hospital
Dr. Jagat Narula, MD is a cardiologist in New York, NY. He is affiliated with medical facilities Mount Sinai Morningside and Mount Sinai Hospital. He is accepting new patients.
Understand and reduce your heart disease risk with these important tests.
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Follow a traditional heart healthy diet with simple and satisfying dishes
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A cardiovascular risk calculator is a medical tool that estimates your chance of having a heart attack or stroke in the next 10 years.
Think of it as a personalized weather forecast for your heart healthāit combines multiple factors about your health to predict future risk.
Risk calculators are built using data from large medical studies that follow thousands of people over many years.
Researchers track who develops heart disease and identify the common factors that increase risk.
These patterns are then turned into mathematical formulas that can predict individual risk.
Key Components:
Most calculators evaluate these core factors:
Risk Categories:
What Your Number Means: A 10% risk means that out of 100 people exactly like you, about 10 will have a heart attack in the next 10 years. It's a probability, not a certainty.
Most widely-used risk calculators were developed using predominantly white populations.
This creates significant problems for South Asians:
Just as weather forecasts are more accurate when they account for local geography and climate patterns,
heart disease risk assessment is more accurate when it accounts for population-specific health patterns.
A good risk calculator doesn't just give you a numberāit helps you understand:
Risk assessment is most valuable when it guides action:
Modern risk calculators are becoming more sophisticated:
The future of cardiovascular risk assessment is moving toward truly personalized predictions that account for:
Remember These Important Points:
Bottom Line: A good cardiovascular risk calculator is a powerful tool for understanding and preventing heart disease,
but it works best when designed for your specific population and used alongside professional medical care.
This information is for educational purposes only and should not replace professional medical advice.
Always consult with your healthcare provider for proper cardiovascular risk assessment and treatment decisions.
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Primary Foundation Studies
2025 Core Research (Primary Foundation)
INTERHEART Study (Global Foundation)
PREVENT Study (AHA 2023 Guidelines)
Machine Learning Studies for MI Detection & Prediction
High-Performance ML Algorithms (93.53%-99.99% Accuracy)
Advanced AI and Transformer Models
MASALA Study (South Asian Specific)
South Asian Cardiovascular Research
Population-Specific Risk Studies
Risk Calculator Validation Studies
Biomarkers and Advanced Testing
ApoB/ApoA1 and Lipid Research
Coronary Artery Calcium and Advanced Imaging
Dietary and Lifestyle Factors
South Asian Dietary Patterns
Metabolic Syndrome and Obesity
Psychosocial Risk Factors
Key Historical Context
Note: This comprehensive reference list includes 35 peer-reviewed studies that form the scientific foundation for the SACRA Calculator, with emphasis on the latest 2025 machine learning research, South Asian-specific cardiovascular risk factors, and validated global studies like INTERHEART and MASALA. The calculator algorithm incorporates findings from all these studies to provide evidence-based risk assessment tailored specifically for South Asian populations.
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17.9 million annual heart attack deaths globally among South Asians
Heart attacks occur about a decade earlier compared to other populations
40% higher mortality risk from cardiovascular disease
2ā4 times higher baseline risk for heart disease in South Asian populations
These statistics represent millions of families affected by preventable heart diseaseāa crisis that traditional risk assessment tools have failed to adequately address.
NORRISK 2 Study: Traditional scores underestimate risk by 2-fold; misclassify high-risk individuals
WHO Risk Charts: Show misclassification; fail to capture South Asian-specific risk patterns
30,000+ participants across 52 countries
15,152 heart attack patients vs 14,820 controls
Identified the "Big 9" risk factors accounting for over 90% of heart attacks
Big 9 Risk Factors:
Abnormal Cholesterol: 49%
Smoking: 36%
Stress/Depression: 33%
Blood Pressure: 18%
Abdominal Obesity: 20%
Poor Diet: 14%
Inactivity: 12%
Diabetes: 10%
Moderate Alcohol: 7% protective
Innovations:
Kidney Function & Social Determinants
Modern Biomarkers & Ethnic Data
Benefits to South Asians: Better performance across ethnicities, emphasis on early disease onset
Focus: South Asian-specific data, long-term cohort, cardiac imaging
Metabolic Differences: Syndrome at lower BMI, early diabetes
Lipid Profile: High triglycerides, low HDL
Imaging: Early plaque detection via coronary calcium scoring
Big 9 risk factor scoring with South Asian weightings
Lower BMI cutoff: 23 kg/mĀ²
Waist-to-hip ratio emphasis
AI-based prediction with 93.5ā99.9% accuracy
ApoB/ApoA1 prioritization
Advanced diabetes & kidney evaluation
Lp(a), hs-CRP, calcium scoring with percentile mapping
ML models with AUC 0.80ā0.95
Dynamic refinement using new research
Diet: Regional carb intake, preparation style risks
Stress: Cultural, immigration, family pressure stressors
Technology: ML-enhanced cardiac imaging, predictive algorithms
Accuracy: Traditional: 50ā70%, SACRA: 93.5ā99.9%
Clinical Impact: Early detection, accurate treatment, better outcomes
Genetic & Environmental Factor Tracking
Device-based monitoring & pharmacogenomics
Ideal Use: Adults 20ā79 of South Asian ancestry
Clinical Integration: Screening, education, planning
Limitations: Not a diagnostic tool; regular updates needed
Bottom Line: SACRA combines global data, population-specific studies, and modern AI technology to deliver the most accurate cardiovascular risk calculator available for South Asians.
This tool is for educational purposes only. Always consult a medical professional for accurate diagnosis and treatment.
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Free Quick-Start Guide In this guide, youāll learn how to check yourĀ BPĀ correctly:
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