Prognostic value of multiple emerging biomarkers in cardiovascular risk prediction in patients with stable cardiovascular disease

2013 ◽  
Vol 228 (2) ◽  
pp. 478-484 ◽  
Author(s):  
Namanjeet Ahluwalia ◽  
Jacques Blacher ◽  
Fabien Szabo de Edelenyi ◽  
Patrice Faure ◽  
Chantal Julia ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Prognostic Value ◽  
Cardiovascular Risk Prediction

scholarly journals Multimarker Approach in Cardiovascular Risk Prediction

2009 ◽  
Vol 26 (5-6) ◽  
pp. 273-285 ◽  
Author(s):  
Ignatios Ikonomidis ◽  
Christos A. Michalakeas ◽  
John Lekakis ◽  
Ioannis Paraskevaidis ◽  
Dimitrios Th. Kremastinos
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Prognostic Value ◽  
Prognostic Information ◽  
Chronic Coronary Artery Disease ◽  
Myocardial Stress ◽  
Current Thinking ◽  
Cardiovascular Risk Prediction ◽  
Coronary Syndromes ◽  
Artery Disease

Various biomarkers express different pathways and pathophysiologic mechanisms of cardiovascular disease, such as inflammation, oxidative stress, myocardial injury, activation of the neurohormonal pathways, myocardial stress and renal function. Current thinking supports the notion that the combination of these biomarkers could increase their diagnostic and prognostic value. The multimarker approach offers benefits since it increases the diagnostic and prognostic information and may help in the design of a strategy for prevention or management of cardiovascular diseases. The purpose of the current review is to describe the characteristics of promising biomarkers which have shown an important additive value in the assessment of cardiovascular risk. Also, an extended reference is made regarding studies that address the prognostic value of multimarker models in the settings of primary prevention of cardiovascular disease and secondary prevention for patients with acute coronary syndromes, chronic coronary artery disease and heart failure.

scholarly journals Long-term prognostic value of morphological plaque features on coronary computed tomography angiography

2019 ◽  
Author(s):  
Tom Finck ◽  
Antonija Stojanovic ◽  
Albrecht Will ◽  
Eva Hendrich ◽  
Stefan Martinoff ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Computed Tomography Angiography ◽  
Coronary Stenosis ◽  
Prognostic Value ◽  
Coronary Computed Tomography Angiography ◽  
Coronary Computed Tomography ◽  
Clinical Risk ◽  
Cardiovascular Risk Prediction

Abstract Aims To investigate the incremental prognostic value of morphological plaque features beyond clinical risk and coronary stenosis levels. Although associated with the degree of coronary stenosis, most cardiac events occur on the basis of ruptured non-obstructive plaques and consecutive vessel thrombosis. As such, identification of vulnerable plaques is paramount for cardiovascular risk prediction and treatment decisions. Methods and results A total of 1615 patients with suspected but not previously diagnosed coronary artery disease (CAD) were examined by coronary computed tomography angiography and morphological plaque features were assessed. Mean follow-up was 10.5 (interquartile range 9.2–11.4) years. Cox proportional hazards analysis was used for the composite endpoint of cardiac death and non-fatal myocardial infarction. The study endpoint was reached in 51 patients (36 cardiac deaths, 15 non-fatal myocardial infarctions). In addition to quantitative parameters (presence of any calcified/non-calcified plaque or elevated plaque load), morphologic plaque features such as a spotty or gross calcification pattern and napkin-ring sign (NRS) were predictive for events. However, only spotty calcified plaques and NRS could confer additive prognostic value beyond clinical risk and coronary stenosis level. In a stepwise approach, endpoint prediction beyond clinical risk (Morise score) could be improved by inclusion of CAD severity (χ2 of 27.5, P < 0.001) and further discrimination for spotty calcified plaques (χ2 of 3.89, P = 0.049). Conclusion Improved cardiovascular risk prediction beyond clinical risk and coronary stenosis levels can be made by discriminating for the presence of spotty calcified plaques. Thus, an intensified prophylactic anti-atherosclerotic treatment appears to be warranted in patients with coronary plaques that show spotty calcifications.

scholarly journals Pre-existing and machine learning-based models for cardiovascular risk prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sang-Yeong Cho ◽  
Sun-Hwa Kim ◽  
Si-Hyuck Kang ◽  
Kyong Joon Lee ◽  
Dongjun Choi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Lipid Lowering ◽  
Lipid Lowering Therapy ◽  
Korean Adults ◽  
Cardiovascular Risk Prediction

AbstractPredicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-based prediction algorithms. This study included 222,998 Korean adults aged 40–79 years, naïve to lipid-lowering therapy, had no history of cardiovascular disease. Pre-existing models showed moderate to good discrimination in predicting future cardiovascular events (C-statistics 0.70–0.80). Pooled cohort equation (PCE) specifically showed C-statistics of 0.738. Among other machine learning models such as logistic regression, treebag, random forest, and adaboost, the neural network model showed the greatest C-statistic (0.751), which was significantly higher than that for PCE. It also showed improved agreement between the predicted risk and observed outcomes (Hosmer–Lemeshow χ2 = 86.1, P < 0.001) than PCE for whites did (Hosmer–Lemeshow χ2 = 171.1, P < 0.001). Similar improvements were observed for Framingham risk score, systematic coronary risk evaluation, and QRISK3. This study demonstrated that machine learning-based algorithms could improve performance in cardiovascular risk prediction over contemporary cardiovascular risk models in statin-naïve healthy Korean adults without cardiovascular disease. The model can be easily adopted for risk assessment and clinical decision making.

scholarly journals Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care setting

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
K D Dziopa ◽  
F W A Asselbergs ◽  
J G Gratton ◽  
N C Chaturvedi ◽  
A F S Schmidt
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
General Population ◽  
Risk Prediction ◽  
Risk Scores ◽  
Cvd Risk ◽  
C Statistic ◽  
Cardiovascular Risk Prediction

Abstract   People with type 2 diabetes (T2DM) remain at high risk for cardiovascular disease (CVD) CVD treatment initiation and intensification are guided by risk prediction algorithms. The majority of CVD risk prediction tools have not been validated in T2DM. We compared the performance of general and diabetes specific cardiovascular risk prediction scores for cardiovascular disease (CVD ie coronary heart disease and stroke), CVD+ (including atrial fibrillation and heart failure), and their individual components, in type 2 diabetes patients (T2DM). Scores were identified through a systematic review and included irrespective of the type of predicted CVD, or inclusion of T2DM patients. Performance was assessed in a contemporary sample of 203,172 UK T2DM. We identified 22 scores: 11 derived in the general population, 9 in T2DM patients, and 2 excluded T2DM patients. Over 10 years follow-up, 63,000 events occurred. The RECODE score, derived in people with T2DM, performed best for both CVD (c-statistic 0.731 (0.728,0.734), and CVD+ (0.732 (0.729,0.735)). Calibration slopes (1 indicates perfect calibration) ranged from 0.38 (95% CI 0.37; 0.39) to 1.05 (95% CI 1.03; 1.07). A simple, population specific recalibration process considerably improved performance, now ranging between 0.98 and 1.03. Risk scores performed badly in people with pre-existing CVD (c-statistic ∼0.55). CVD risk prediction scores performed worse in T2DM than in the general population, irrespective of derivation population, and of original predicted outcome. Scores performed especially poorly in patients with established CVD. A simple population specific recalibration markedly improved score performance and is recommended for future use. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): NPIF programme

Cardiovascular Risk Prediction using JBS3 Tool: A Kerala based Study

2020 ◽  
Vol 16 ◽  
Author(s):  
Paulin Paul ◽  
Noel George ◽  
B. Priestly Shan
Keyword(s):  
Logistic Regression ◽  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Goodness Of Fit ◽  
Significant Risk ◽  
Lifetime Risk ◽  
British Society ◽  
Cvd Risk ◽  
South Indian ◽  
Cardiovascular Risk Prediction

Background: The accuracy of Joint British Society calculator3 (JBS3) cardiovascular risk prediction may vary within Indian population, and is not yet studied using south Indian Kerala based population data. Objectives: To evaluate the cardiovascular disease (CV) risk estimation using the traditional CVD risk factors (TRF) in Kerala based population. Methods: This cross sectional study has 977 subjects aged between 30 and 80 years. The traditional CVD risk markers are recorded from the medical archives of clinical locations at Ernakulum district, in Kerala The 10 year risk categories used are low (<7.5%), intermediate (≥7.5% and <20%), and high (≥20%). The lifetime classifications low lifetime (≤39%) and high lifetime (≥40%) are used. The study was evaluated using statistical analysis. Chi-square test was done for dependent and categorical CVD risk variable comparison. Multivariate ordinal logistic regression for 10-year risk model and odds logistic regression analysis for lifetime model was used to identify significant risk variables. Results: The mean age of the study population is 52.56±11.43 years. The risk predictions has 39.1% in low, 25.0% in intermediate, and 35.9% had high 10-year risk. The low lifetime risk had 41.1% and 58.9% is high lifetime risk. Reclassifications to high lifetime are higher from intermediate 10-year risk category. The Hosmer-Lemeshow goodness-of-fit statistics indicates a good model fit. Conclusion: The risk prediction and timely intervention with appropriate therapeutic and lifestyle modification is useful in primary prevention. Avoiding short-term incidences and reclassifications to high lifetime can reduce the CVD mortality rates.

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