A recent study published in the journal Frontiers in Physiology highlights the need for an upgrade in the Framingham Risk Score (FRS) system to improve heart disease diagnosis in women. The FRS currently analyzes six factors to predict heart attack or stroke risk, but does not account for gender-specific risk factors in women. Researchers suggest leveraging big data and incorporating machine learning to tailor the scoring system to better identify cardiovascular conditions that are often under-diagnosed in women.
According to the study authors, the current design of the FRS overlooks multiple cardiovascular conditions in women, leading to delayed diagnosis and more severe symptoms compared to men. The lack of sex-specific diagnostic criteria, along with medical misogyny and misconceptions about heart attacks being primarily a risk in men, contribute to the under-diagnosis of women in cardiovascular health. The researchers propose utilizing large data sets and machine learning to improve the accuracy of the FRS in diagnosing heart disease in women, taking into account specific risk factors that affect female heart health.
Cardiologist Dr. Evelina Grayver emphasizes the physiological and anatomical differences between men and women, highlighting the need for gender-specific studies like this to address the under-treatment and under-representation of women in cardiovascular health. She points out that heart disease kills more women annually than all cancers combined, yet misconceptions lead to delays in seeking treatment and biases from healthcare professionals. Grayver stresses the importance of considering non-traditional risk factors in women’s heart health, such as estrogen levels, pregnancy complications, autoimmune diseases, family history, and breast tissue.
Symptoms of heart attacks in women can differ from those in men, with subtler signs such as shortness of breath, reflux, nausea, vomiting, and tightness in the chest. Dismissing these symptoms can lead to delays in treatment, increasing the risk of complications from a heart attack. Grayver advocates for a more comprehensive approach to assessing a woman’s risk of heart disease, including considerations of estrogen levels, pregnancy complications, and mammogram results in addition to traditional risk factors. She emphasizes the importance of not overlooking women’s symptoms and providing tailored healthcare that considers individual risk factors for heart disease.
An upgraded FRS system that accounts for gender-specific risk factors in women could revolutionize how medical professionals diagnose and treat heart disease in female patients. By incorporating new data sets and machine learning techniques, the FRS could better predict cardiovascular conditions that are currently under-diagnosed in women. Dr. Grayver’s advocacy for a more holistic approach to assessing a woman’s heart health underscores the importance of personalized medicine that considers individual risk factors beyond traditional criteria. Improving the FRS could lead to better healthcare outcomes for women and challenge misconceptions about heart disease being primarily a male risk.
