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Structural Equation Modeling with Longitudinal Data
Published in Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle, Structural Equation Modeling for Health and Medicine, 2021
Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle
Age-related risk can be quantified for an individual in a given study by computing a risk score. A risk score is a calculated value that reflects the severity of a risk due to some factors. For example, the Framingham risk score is a well-known risk score to forecast 10-year risk of heart attack [52]. An online calculator for the Framingham risk score2 asks an individual to enter age, sex, smoker status, total cholesterol, HDL cholesterol, systolic BP and if one is being treated with an antihypertensive and will output a risk score. The Framingham risk score is an example in which an external prognostic model is used to calculate the risk score.
Introduction to Artificial Intelligence and Deep Learning with a Case Study in Analyzing Electronic Health Records for Drug Development
Published in Harry Yang, Binbing Yu, Real-World Evidence in Drug Development and Evaluation, 2021
Similar to the previous section, there are linear AFT models, DNNs, CNNs, and CNNs with text information in the comparison. Randomforest is left out because it is not straightforward enough to adapt tree-based methods for survival analysis. Table 8.2 shows the performance in terms of AUC, concordance, and concordance on patients with cardiovascular events. To give a reference, the famous Framingham Risk Score only has a concordance of 0.69. Several conclusions can be drawn from the table. First, it is relatively easy to tell cardiovascular patients from non-cardiovascular patients. Second, it is more difficult to tell high-risk cardiovascular patients from low-risk cardiovascular patients. Finally, DNN/CNNs provide a more accurate prediction of whether a patient will have events by a specific time.
Artificial Intelligence
Published in Lawrence S. Chan, William C. Tang, Engineering-Medicine, 2019
Several cardiovascular risk calculators exist. One of the first was the Framingham risk score (Lloyd-Jones et al. 2004, Pencina et al. 2009). The Framingham risk score provides a number that indicates the likelihood of the patient developing cardiovascular disease within the next ten years. The factors considered are age, sex, total blood cholesterol, history of cigarette smoking, HDL cholesterol levels in blood, and systolic blood pressure. Taken together, these factors can predict cardiovascular disease with an area under the receiver operator characteristic curve AUC of ROC of about 0.8 (Günaydin et al. 2016).
Developing a predictive equation of cardiovascular age to evaluate cardiovascular health in Chinese community-dwelling women
Published in Health Care for Women International, 2023
There are multiple risk assessment methods to evaluate cardiovascular health or cardiovascular risks in various populations. A simple and direct method is to monitor the cardiovascular risk factors and then compare and contrast these values with the normal ranges. Furthermore, a well-known risk assessment method is the Framingham Risk Score, which was to predict a person’s 10-year risk of a cardiovascular disease event (D’Agostino et al., 2008; Jahangiry et al., 2017). Similarly, the American College of Cardiology and the American Heart Association constructed Pooled Cohort Risk Equations to estimate atherosclerotic cardiovascular disease in individuals in a period of 10-years (Edwards et al., 2018; Goff et al., 2014). Recently, vascular age has been considered to be an independent factor responsible for the development of cardiovascular disease (Groenewegen et al., 2016; Lin et al., 2020). Vascular age is an easily understood concept, which represents age adjusted for an individual’s arterial stiffness parameters (Dakik, 2019).
Clinical application of personalized rheumatoid arthritis risk information: translational epidemiology leading to precision medicine
Published in Expert Review of Precision Medicine and Drug Development, 2021
A number of important clinical risk tools, such as the Framingham Risk Score for cardiovascular disease [6], have been successfully implemented. While these tools have been validated, they may be inaccurate on an individual level [7] or in distinct populations [6] and may have no ability to incorporate novel risk factors or consider interactions between factors [8]. These tools were typically developed for clinicians to help risk-stratify, screen, treat, or prognosticate; they were less oriented toward assisting in diagnosis. While genetic factors are increasingly tested in clinical practice, interpretation is usually not easily integrated with other risk factors. The effects of traditional clinical risk tools on the patient’s willingness to accept interventions, optimizing health behaviors, and psychologic impact have not been the focus. Thus, traditional clinical risk tools are not directly transferable toward a precision medicine framework.
Association between the liver fat score (LFS) and cardiovascular diseases in the national health and nutrition examination survey 1999–2016
Published in Annals of Medicine, 2021
Chun-On Lee, Hang-Long Li, Man-Fung Tsoi, Ching-Lung Cheung, Bernard Man Yung Cheung
Despite being rather benign, NAFLD has been associated with all-cause mortality and the main cause of death, CVD [10,17–20]. Whether there is a direct causal relationship between NAFLD and CV mortality is unclear, but if so, it is likely to be mediated by dyslipidaemia, inflammation, atherosclerosis and ventricular dysfunction. Conventional cardiovascular risk assessment models may not be good at identifying NAFLD-related CVDs. They do not account for insulin resistance, which has an important pathological role in NAFLD. The Framingham Risk Score is known to underestimate the CVD risk in patients with metabolic syndrome . This means that some people whose CVD risks are underestimated are neither on treatment nor under surveillance. The use of a non-invasive score to detect NAFLD may alert the clinician to the need to assess the CV risk of the patient more carefully and act accordingly.