Prediction of Cardiovascular Risk to Guide Primary Prevention

Curfman, Gregory D.

doi:10.1001/jamainternmed.2018.3131

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…CVD is the leading cause of death worldwide and a major public health concern. Its risk assessment is crucial to many existing treatment and control guidelines [14][15][16] . Although the effect of primary health care in controlling CVD has been demonstrated before, controversies still exist on the benefits of applying risk prediction compared with those of the risk factor approach in population-based interventions 17,18 .…”

Section: Discussionmentioning

confidence: 99%

Study of cardiovascular disease prediction model based on random forest in eastern China

Yang

Wu²,

Jin

et al. 2020

Sci Rep

235

133

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cardiovascular disease (cVD) is the leading cause of death worldwide and a major public health concern. CVD prediction is one of the most effective measures for CVD control. In this study, 29930 subjects with high-risk of CVD were selected from 101056 people in 2014, regular follow-up was conducted using electronic health record system. Logistic regression analysis showed that nearly 30 indicators were related to CVD, including male, old age, family income, smoking, drinking, obesity, excessive waist circumference, abnormal cholesterol, abnormal low-density lipoprotein, abnormal fasting blood glucose and else. Several methods were used to build prediction model including multivariate regression model, classification and regression tree (CART), Naïve Bayes, Bagged trees, Ada Boost and Random forest. We used the multivariate regression model as a benchmark for performance evaluation (Area under the curve, AUC = 0.7143). The results showed that the Random Forest was superior to other methods with an AUC of 0.787 and achieved a significant improvement over the benchmark. We provided a CVD prediction model for 3-year risk assessment of CVD. It was based on a large population with high risk of CVD in eastern China using Random Forest algorithm, which would provide reference for the work of cVD prediction and treatment in china. Cardiovascular disease (CVD) is a series of diseases involving the circulatory system, including angina pectoris, myocardial infarction, coronary heart disease, heart failure, arrhythmia and else, which is generally related to atherosclerosis. With the social economy development, the population aging and the urbanization acceleration in China, some changes have taken place in national lifestyles, which leading to a rise of CVD prevalence. In 2016, there were more than 290 million cases of CVD in China, and 4.344 million deaths from it, including 2.098 million deaths from stroke and 1.736 million deaths from coronary heart disease, which bringing heavy social and economic burden 1. CVD is a disease that can be prevented and controlled, and early intervention can effectively control its progress 2. In recent years, many achievements have been made in the study of CVD risk prediction model, but the effect of epidemiological risk factors and biomarkers may be different in different populations, the CVD model has certain population specificity. In addition, there has been no study on CVD risk prediction model based on large cohort population in eastern China. At the same time, a large number of the existing CVD prediction models use multivariable regression method to build prediction models in a linear fashion, but it generally exhibit modest predictive performance, especially for certain sub-populations 3,4. Machine learning (ML) such as random forest (RF) can improve the performance of risk predictions by exploiting large data repositories to identify novel risk predictors and more complex interactions between them 3. In this study, we conducted a CVD prediction model research based on a specific c...

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Section: Discussionmentioning

confidence: 99%

Study of cardiovascular disease prediction model based on random forest in eastern China

Yang

Wu²,

Jin

et al. 2020

Sci Rep

235

133

View full text Add to dashboard Cite

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“…11,12 Based on the increased prevalence of subclinical coronary artery disease demonstrated in masters endurance athletes with low conventional risk-factor profiles, coronary artery calcium scores are recommended over conventional calculators as an independent predictor of cardiac events to guide use of additional measures for primary prevention. [13][14][15][16] The inverse correlation of coronary artery plaque volume with short-term cardiac event-free survival, and documentation of coronary atherosclerosis as the cause of race-related sudden cardiac deaths even in the most experienced runners attest to the morbidity and mortality associated with what has been called by some "hearts of stone" of undetermined significance. [17][18][19][20][21][22][23] An explanation for increased coronary atherosclerosis in experienced endurance athletes compared with matched control persons is that inflammation during repetitive bouts of exertional rhabdomyolysis promotes calcification through recurrent injury to vascular endothelium.…”

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confidence: 99%

Aspirin to Prevent Sudden Cardiac Death in Athletes with High Coronary Artery Calcium Scores

Siegel

Noakes

2019

The American Journal of Medicine

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While proficient cardiac resuscitation has improved survival following cardiac arrest during road races in Japan, this accomplishment does not address coronary artery disease as the underlying cause of an increasing frequency of cardiac arrest in middle-aged men during marathons and ironman triathlons in the United States since the year 2000. Based on the high prevalence of subclinical coronary artery disease by cardiac computed tomography in endurance athletes with low conventional cardiac risk-factor profiles, we recommend coronary artery calcium scores as a more reliable and independent predictor of incident cardiac events, including death, as validated among adults aged 30-46 years. Scores of over 100 Agatston units indicate a 10-year cardiac risk of 7.5%, at which additional measures for primary prevention are recommended, including aspirin, as shown conclusively to reduce first myocardial infarctions in same-aged men in a prospective double-blind controlled trial. Targeted screening for subclinical coronary atherosclerosis with coronary artery calcium scores is prudent to guide appropriately dosed aspirin use to mitigate the increasing frequency of sports-related sudden cardiac death due to plaque rupture.

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