Materiały źródłowe

• #1 New Progress in Early Diagnosis of Atherosclerosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9409135/

  Coronary atherosclerosis is a potentially chronic circulatory condition that endangers human health. […] The progression and severity of cardiovascular disease are correlated with various levels of calcium accumulation in the coronary artery. The therapy and diagnosis of coronary atherosclerosis benefit from the initial assessment of the size and degree of calcification. […] Calcification is a key cause of coronary atherosclerosis and a good marker to forecast future heart problems. Heart disease worsens and spreads at different rates depending on how much calcium builds up in the body. […] Coronary atherosclerosis is treated and has a favorable prognosis when the amount and extent of calcification are determined early. […] It is viable to assess coronary atherosclerosis risk using genes and trace elements. […] One of the therapy methods for coronary artery disease is the detection of trace elements, which is important for prognosis.

• #2 Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5640485/

  Machine learning may be useful to characterize cardiovascular risk, predict outcomes and identify biomarkers in population studies. […] To test the ability of random survival forests (RF), a machine learning technique, to predict six cardiovascular outcomes in comparison to standard cardiovascular risk scores. […] MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of CV disease. […] Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary artery calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function, and cardiac troponin-T were among the top predictors for incident heart failure.

• #3 New Progress in Early Diagnosis of Atherosclerosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9409135/

  Coronary atherosclerosis is a potentially chronic circulatory condition that endangers human health. […] The progression and severity of cardiovascular disease are correlated with various levels of calcium accumulation in the coronary artery. The therapy and diagnosis of coronary atherosclerosis benefit from the initial assessment of the size and degree of calcification. […] Calcification is a key cause of coronary atherosclerosis and a good marker to forecast future heart problems. Heart disease worsens and spreads at different rates depending on how much calcium builds up in the body. […] Coronary atherosclerosis is treated and has a favorable prognosis when the amount and extent of calcification are determined early. […] It is viable to assess coronary atherosclerosis risk using genes and trace elements. […] One of the therapy methods for coronary artery disease is the detection of trace elements, which is important for prognosis.

• #4 Prediction of cardiovascular outcomes by imaging coronary atherosclerosis – Pathan – Cardiovascular Diagnosis and Therapy

  https://cdt.amegroups.org/article/view/9218/html

  Over the last two decades, several invasive and non-invasive coronary atherosclerosis imaging modalities have emerged as predictors of cardiovascular outcomes in at-risk population. […] The main outcomes can be divided into long-term outcomes, such subsequent major adverse cardiovascular event (MACE); and more immediate peri-procedural outcomes such as distal embolization, peri-procedural myocardial infraction and no reflow. […] The subsequent discussion about imaging modalities and their role in prediction of cardiovascular outcomes is heavily influenced by several factors including: pre-test probability (e.g., patient populations or characteristics), study design (duration of follow-up in cohort study, e.g., 30-day, years or a decade), definition of outcome [and the distribution of each component of outcomes if composite endpoint (e.g., MACE) is used], as well as the technology under investigation.

• #5 Sonographic assessment of carotid atherosclerosis: preferred risk indicator for future cardiovascular events?

  https://smw.ch/index.php/smw/article/download/2709/4341?inline=1

  The inclusion of carotid plaque quantification into cardiovascular risk prediction has significantly improved discrimination and reclassification of subjects in primary care. […] Cardiovascular risk increases with the carotid plaque burden quantified as TPA. […] Given the excellent predictive accuracy for cardiovascular events of carotid TPV, which is indeed comparable to presence and extent of coronary calcifications, it might be argued that carotid TPV should be used instead of carotid TPA. […] The American College of Cardiology Foundation (ACCF) / American Heart Association (AHA) 2010 guidelines for assessment of cardiovascular risk in asymptomatic adults issued a class IIa recommendation for coronary calcium assessment in subjects at intermediate risk. […] According to the latest Joint European Society of Cardiology (ESC) guidelines, carotid plaque but not carotid IMT was categorised as a very high-risk finding. […] The identification of subjects at increased risk for cardiovascular events using traditional risk factors is established in clinical practice. However, about 40% of low-risk subjects are classified as having low risk, despite the presence of prognostically relevant atherosclerosis.

• #6 Sonographic assessment of carotid atherosclerosis: preferred risk indicator for future cardiovascular events?

  https://smw.ch/index.php/smw/article/download/2709/4341?inline=1

  Spence showed in 2002 that TPA was predictive for cardiovascular events with increasing risk in the higher quartiles of TPA. […] In 2003, Hollander reported on 6913 prospectively followed healthy subjects assessed in the Rotterdam study and found that carotid IMT (with inclusion of regions with carotid plaque) was a stronger predictor for ensuing stroke than carotid plaque alone. […] In 2004, Van der Meer reported on 6389 subjects from the Rotterdam study with assessments of carotid structural changes and found equal predictive power for ensuing myocardial infarction. […] In 2007, Stein et al. reported on 6226 originally healthy subjects assessed in the Norwegian Troms study and found TPA to be a stronger, statistically significant marker for incident myocardial infarction than IMT. […] In 2011, Polak reported that in 2965 members of the Framingham Offspring Study cohort evaluated for cardiovascular outcome during a follow-up of 7 years, both internal carotid IMT and internal carotid plaque formation improved the AUC significantly when compared with the Framingham risk equation.

• #7 Sonographic assessment of carotid atherosclerosis: preferred risk indicator for future cardiovascular events?

  https://smw.ch/index.php/smw/article/download/2709/4341?inline=1

  The inclusion of carotid plaque quantification into cardiovascular risk prediction has significantly improved discrimination and reclassification of subjects in primary care. […] Cardiovascular risk increases with the carotid plaque burden quantified as TPA. […] Given the excellent predictive accuracy for cardiovascular events of carotid TPV, which is indeed comparable to presence and extent of coronary calcifications, it might be argued that carotid TPV should be used instead of carotid TPA. […] The American College of Cardiology Foundation (ACCF) / American Heart Association (AHA) 2010 guidelines for assessment of cardiovascular risk in asymptomatic adults issued a class IIa recommendation for coronary calcium assessment in subjects at intermediate risk. […] According to the latest Joint European Society of Cardiology (ESC) guidelines, carotid plaque but not carotid IMT was categorised as a very high-risk finding. […] The identification of subjects at increased risk for cardiovascular events using traditional risk factors is established in clinical practice. However, about 40% of low-risk subjects are classified as having low risk, despite the presence of prognostically relevant atherosclerosis.

• #8 Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5640485/

  Machine learning in conjunction with deep phenotyping improve prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. […] The RF technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 1025%). […] For incident heart failure as the endpoint, cardiac chamber stress (increased LV volume, and increased NT-proBNP levels), and decreased LV function from MRI were the most important markers. […] For incident atrial fibrillation as the endpoint, inflammation, higher levels of creatinine, atherosclerosis (CAC and ABI), and repolarization abnormalities were the most important markers. […] The results of this study suggest that machine learning methods are well-suited for meaningful risk prediction in extensively phenotyped large-scale epidemiological studies. […] The RF based method of risk prediction provided better event prediction over standard risk scores. […] Inflammation, subclinical atherosclerosis, myocardial damage, and cardiac chamber stress were among the most important predictors across all outcomes.

• #9 Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5640485/

  Machine learning may be useful to characterize cardiovascular risk, predict outcomes and identify biomarkers in population studies. […] To test the ability of random survival forests (RF), a machine learning technique, to predict six cardiovascular outcomes in comparison to standard cardiovascular risk scores. […] MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of CV disease. […] Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary artery calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function, and cardiac troponin-T were among the top predictors for incident heart failure.

• #10 Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis | Scientific Reports

  https://www.nature.com/articles/s41598-023-42338-0

  Large-artery atherosclerosis (LAA) is a leading cause of cerebrovascular disease. However, LAA diagnosis is costly and needs professional identification. […] Therefore, there is an urgent clinical need to identify novel and more efficient biomarkers for predicting the risk of LAA, which can be achieved through the general blood tests. […] Our study has demonstrated the effectiveness of combining machine learning algorithms with recursive feature elimination and cross-validation methods for biomarker identification. […] We found that 27 features were present among the five adopted models that could provide good results. If these 27 features were used in the LR model, an AUC value of 0.93 could be achieved. […] We found (1) The combination of clinical factor and metabolite profile provides stability to data set shifts; (2) with feature selection method we improved the model performance from an AUC of 0.89 to 0.92; (3) the shared features had predictive power equivalent to 67 features, suggesting their clinical importance in identifying patients with LAA. […] The performance metrics could be improved if a feature selection step was added and both clinical factors and metabolites could be combined for disease prediction. […] Our study may help to early predict and prevent the progression of LAA.

• #11 Credibility assessment of a mechanistic model of atherosclerosis to predict cardiovascular outcomes under lipid-lowering therapy | npj Digital Medicine

  https://www.nature.com/articles/s41746-025-01557-7

  Demonstrating cardiovascular (CV) benefits with lipid-lowering therapy (LLT) requires long-term randomized clinical trials (RCTs) with thousands of patients. […] A mechanistic computational model of atherosclerotic cardiovascular disease (ASCVD) was built from knowledge, describing lipoprotein homeostasis, LLT effects, and the progression of atherosclerotic plaques leading to myocardial infarction, ischemic stroke, major acute limb event and CV death. […] This enables the future use of the model to conduct the SIRIUS programme, which intends to predict CV event reduction with inclisiran, an siRNA targeting hepatic PCSK9 mRNA. […] The ASCVD model was successfully calibrated and validated, and reproduced LLT effects observed in selected RCTs (ORION-10 and FOURIER for calibration; ORION-11, ODYSSEY-OUTCOMES and FOURIER-OLE for validation) on lipoproteins and ASCVD event incidence at both population and subgroup levels.

• #12 Credibility assessment of a mechanistic model of atherosclerosis to predict cardiovascular outcomes under lipid-lowering therapy | npj Digital Medicine

  https://www.nature.com/articles/s41746-025-01557-7

  The ASCVD model credibility was successfully established according to a predefined protocol, demonstrating its predictive potential to conduct future in silico modeling of cardiovascular outcomes trial predicting 3P-MACE cumulative incidence over 5 years in virtual populations of patients with established ASCVD and elevated LDL-C levels under background LLT. […] The calibrated ASCVD model and VPop reproduced most population-level and subgroup-level data from FOURIER and ORION-10 RCTs in terms of patient characteristics, baseline lipoprotein profiles and ASCVD risks as well as biological effects of evolocumab and inclisiran and clinical effect of evolocumab on MACE over a 2.2-year median follow-up. […] The ASCVD model will be used to perform the SIRIUS programme (NCT05974345) aiming to predict the long-term clinical benefit of inclisiran on an ASCVD population.

• #13 Credibility assessment of a mechanistic model of atherosclerosis to predict cardiovascular outcomes under lipid-lowering therapy | npj Digital Medicine

  https://www.nature.com/articles/s41746-025-01557-7

  The ASCVD model credibility was successfully established according to a predefined protocol, demonstrating its predictive potential to conduct future in silico modeling of cardiovascular outcomes trial predicting 3P-MACE cumulative incidence over 5 years in virtual populations of patients with established ASCVD and elevated LDL-C levels under background LLT. […] The calibrated ASCVD model and VPop reproduced most population-level and subgroup-level data from FOURIER and ORION-10 RCTs in terms of patient characteristics, baseline lipoprotein profiles and ASCVD risks as well as biological effects of evolocumab and inclisiran and clinical effect of evolocumab on MACE over a 2.2-year median follow-up. […] The ASCVD model will be used to perform the SIRIUS programme (NCT05974345) aiming to predict the long-term clinical benefit of inclisiran on an ASCVD population.

• #14 Comprehensive biomechanical and anatomical atherosclerotic plaque metrics predict major adverse cardiovascular events: A new tool for clinical decision making

  https://www.repository.cam.ac.uk/items/856128b5-7242-4b42-9fce-c9db37a2b4cc

  Background and aims: Anatomical imaging alone of coronary atherosclerotic plaques is insufficient to identify risk of future adverse events and guide management of non-culprit lesions. […] We determined whether combining multiple complementary, biomechanical and anatomical plaque characteristics improves outcome prediction sufficiently to inform clinical decision-making. […] High PSS HI (hazard ratio [HR] 3.9, p=0.006), high ESSG (HR 3.4, p=0.007) and plaque burden70% (HR 2.6, p=0.02) were independent outcome predictors in multivariate analysis. […] Combining complementary biomechanical and anatomical metrics significantly improves risk-stratification of individual coronary lesions. If confirmed from larger prospective studies, our results may inform targeted revascularisation vs. conservative management strategies.

• #15 Comprehensive biomechanical and anatomical atherosclerotic plaque metrics predict major adverse cardiovascular events: A new tool for clinical decision making

  https://www.repository.cam.ac.uk/items/856128b5-7242-4b42-9fce-c9db37a2b4cc

  Background and aims: Anatomical imaging alone of coronary atherosclerotic plaques is insufficient to identify risk of future adverse events and guide management of non-culprit lesions. […] We determined whether combining multiple complementary, biomechanical and anatomical plaque characteristics improves outcome prediction sufficiently to inform clinical decision-making. […] High PSS HI (hazard ratio [HR] 3.9, p=0.006), high ESSG (HR 3.4, p=0.007) and plaque burden70% (HR 2.6, p=0.02) were independent outcome predictors in multivariate analysis. […] Combining complementary biomechanical and anatomical metrics significantly improves risk-stratification of individual coronary lesions. If confirmed from larger prospective studies, our results may inform targeted revascularisation vs. conservative management strategies.

• #16 Physical Activity, Atherosclerosis Levels Predict Mortality Outcome Among Patients With Cardiovascular Disease

  https://www.ajmc.com/view/physical-activity-atherosclerosis-levels-predict-mortality-outcome-among-patients-with-cardiovascular-disease

  Self-reported high levels of physical activity shared a positive correlation with improved mortality and care for patients who underwent chest CT scans for atherosclerosis. […] Overall results show that more physical activity was linked to an almost 71% lower annual mortality rate compared with less physical activity: 1.7% vs 2.9%. […] Our study showed that simply asking patients to rate their level of physical activity, while using a test to look at the plaque in their coronary arteries, markedly improved our ability to predict patients risk for dying over their next decade of life, stated Alan Rozanski, MD, director of nuclear cardiology and cardiac stress testing at the Icahn School of Medicine at Mount Sinai and the studys first author.

• #17 Development of a multidimensional prediction model for long-term prognostic risk in patients with acute coronary syndromes after percutaneous coronary intervention: A retrospective observational cohort study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0318445

  The aim of this study is to examine the critical variables that impact the long-term prognosis of patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI) and to create a multidimensional predictive risk assessment model that can serve as a theoretical basis for accurate cardiac rehabilitation. […] We found white blood cell count (WBC) (OR: 4.110) and the effective average number of daily steps (ANS) (OR: 2.689) as independent prognostic risk factors for acute myocardial infarction (AMI). The independent risk factors for unstable angina prognosis were white blood cell count (OR: 6.257), VO2 at anaerobic threshold (OR: 4.294), and effective autonomic nervous system function (OR: 4.097). […] This study developed a multimodal predictive model that integrates the inflammatory response after onset, physical performance and exercise tolerance before discharge, and daily activity after discharge to predict the long-term prognosis of patients with ACS. The multidimensional model is more effective than the single-factor model for assessing risk in ACS patients.

• #18 Development of a multidimensional prediction model for long-term prognostic risk in patients with acute coronary syndromes after percutaneous coronary intervention: A retrospective observational cohort study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0318445

  This study found that the inflammatory response after onset, physical performance and exercise tolerance before discharge, and daily activity after discharge were the independent risk factors for predicting the long-term prognosis of patients with ACS. The multidimensional prognostic model to risk-stratify for the patients with ACS, was better than the single factor model. This study also provides a theoretical basis that the prognosis of potentially high-risk patients can be improved by precise and rational exercise prescription.

• #19 Prediction of atherosclerotic cardiovascular disease mortality in a nationally representative cohort using a set of risk factors from pooled cohort risk equations | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175822

  The American College of Cardiology/American Heart Association developed Pooled Cohort equations to estimate atherosclerotic cardiovascular disease (ASCVD) risk. […] We estimated the discrimination and calibration for each sex-race-ethnicity model. […] The Pooled Cohort equations demonstrated moderate to good discrimination for ASCVD mortality, with modified C-statistics of 0.716 (95% CI 0.6630.770), 0.794 (0.7340.854), and 0.733 (0.6540.811) for NHW, NHB and MA men, respectively. […] The calibration was inadequate for NHW and NHB women (p0.05). […] In this nationally representative cohort, the Pooled Cohort equations performed adequately to predict 10-year ASCVD mortality for NHW and NHB men, and MA population, but not for NHW and NHB women. […] Our findings suggested that the set of risk factors and their interactions used in the Pooled Cohort risk equations had reasonable discrimination ability for 10-year ASCVD mortality by sex and race/ethnicity, and the calibration of the predicted models was adequate for NHW, NHB and Mexican American men and Mexican American women, but inadequate for NHW and NHB women.

• #20 Prediction of atherosclerotic cardiovascular disease mortality in a nationally representative cohort using a set of risk factors from pooled cohort risk equations | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175822

  The American College of Cardiology/American Heart Association developed Pooled Cohort equations to estimate atherosclerotic cardiovascular disease (ASCVD) risk. […] We estimated the discrimination and calibration for each sex-race-ethnicity model. […] The Pooled Cohort equations demonstrated moderate to good discrimination for ASCVD mortality, with modified C-statistics of 0.716 (95% CI 0.6630.770), 0.794 (0.7340.854), and 0.733 (0.6540.811) for NHW, NHB and MA men, respectively. […] The calibration was inadequate for NHW and NHB women (p0.05). […] In this nationally representative cohort, the Pooled Cohort equations performed adequately to predict 10-year ASCVD mortality for NHW and NHB men, and MA population, but not for NHW and NHB women. […] Our findings suggested that the set of risk factors and their interactions used in the Pooled Cohort risk equations had reasonable discrimination ability for 10-year ASCVD mortality by sex and race/ethnicity, and the calibration of the predicted models was adequate for NHW, NHB and Mexican American men and Mexican American women, but inadequate for NHW and NHB women.

• #21 Prediction of atherosclerotic cardiovascular disease mortality in a nationally representative cohort using a set of risk factors from pooled cohort risk equations | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175822

  Our calibration analyses showed reasonably good agreement between predicted and actual ASCVD mortality rates for NHW and NHB men, and Mexican American men and women, but not for NHW and NHB women. […] The Pooled Cohort equations prediction functions underestimated ASCVD mortality, particularly in the high predicted-risk group. […] Our findings suggest that the same set of risk factors and their interactions from the Pooled Cohort equations perform adequately to predict 10-year risk of ASCVD mortality among adult NHW and NHB men, and MA population in the United States, but not for NHW and NHB women.

• #22 Prediction of atherosclerotic cardiovascular disease mortality in a nationally representative cohort using a set of risk factors from pooled cohort risk equations | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175822

  Our calibration analyses showed reasonably good agreement between predicted and actual ASCVD mortality rates for NHW and NHB men, and Mexican American men and women, but not for NHW and NHB women. […] The Pooled Cohort equations prediction functions underestimated ASCVD mortality, particularly in the high predicted-risk group. […] Our findings suggest that the same set of risk factors and their interactions from the Pooled Cohort equations perform adequately to predict 10-year risk of ASCVD mortality among adult NHW and NHB men, and MA population in the United States, but not for NHW and NHB women.

• #23

  https://link.springer.com/article/10.1007/s10143-024-03051-2

  Cerebral revascularization for the treatment of atherosclerotic steno-occlusive disease (ASOD) was found to have no benefit compared with medical treatment. […] Poor outcomes were predicted using machine learning (ML) models, and Shapley additive explanation (SHAP) values and feature importance of each model were analyzed. […] The number of lesions with stenosis50% (odds ratio [OR] 5.77), age (OR 1.13), and coronary artery disease (OR 5.73) were consistent risk factors for poor outcome. […] We demonstrated an acceptable long-term outcome of cerebral revascularization surgery for patients with hemodynamically insufficient and symptomatic ASOD. […] Multicenter studies are encouraged to predict poor outcomes and suitable patients with large numbers of quantitative and qualitative data.

• #24

  https://link.springer.com/article/10.1007/s10143-024-03051-2

  Cerebral revascularization for the treatment of atherosclerotic steno-occlusive disease (ASOD) was found to have no benefit compared with medical treatment. […] Poor outcomes were predicted using machine learning (ML) models, and Shapley additive explanation (SHAP) values and feature importance of each model were analyzed. […] The number of lesions with stenosis50% (odds ratio [OR] 5.77), age (OR 1.13), and coronary artery disease (OR 5.73) were consistent risk factors for poor outcome. […] We demonstrated an acceptable long-term outcome of cerebral revascularization surgery for patients with hemodynamically insufficient and symptomatic ASOD. […] Multicenter studies are encouraged to predict poor outcomes and suitable patients with large numbers of quantitative and qualitative data.

• #25 Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5640485/

  Machine learning may be useful to characterize cardiovascular risk, predict outcomes and identify biomarkers in population studies. […] To test the ability of random survival forests (RF), a machine learning technique, to predict six cardiovascular outcomes in comparison to standard cardiovascular risk scores. […] MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of CV disease. […] Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary artery calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function, and cardiac troponin-T were among the top predictors for incident heart failure.

• #26 Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5640485/

  Machine learning in conjunction with deep phenotyping improve prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. […] The RF technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 1025%). […] For incident heart failure as the endpoint, cardiac chamber stress (increased LV volume, and increased NT-proBNP levels), and decreased LV function from MRI were the most important markers. […] For incident atrial fibrillation as the endpoint, inflammation, higher levels of creatinine, atherosclerosis (CAC and ABI), and repolarization abnormalities were the most important markers. […] The results of this study suggest that machine learning methods are well-suited for meaningful risk prediction in extensively phenotyped large-scale epidemiological studies. […] The RF based method of risk prediction provided better event prediction over standard risk scores. […] Inflammation, subclinical atherosclerosis, myocardial damage, and cardiac chamber stress were among the most important predictors across all outcomes.

• #27 Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5640485/

  Machine learning in conjunction with deep phenotyping improve prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. […] The RF technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 1025%). […] For incident heart failure as the endpoint, cardiac chamber stress (increased LV volume, and increased NT-proBNP levels), and decreased LV function from MRI were the most important markers. […] For incident atrial fibrillation as the endpoint, inflammation, higher levels of creatinine, atherosclerosis (CAC and ABI), and repolarization abnormalities were the most important markers. […] The results of this study suggest that machine learning methods are well-suited for meaningful risk prediction in extensively phenotyped large-scale epidemiological studies. […] The RF based method of risk prediction provided better event prediction over standard risk scores. […] Inflammation, subclinical atherosclerosis, myocardial damage, and cardiac chamber stress were among the most important predictors across all outcomes.

• #28 Sonographic assessment of carotid atherosclerosis: preferred risk indicator for future cardiovascular events?

  https://smw.ch/index.php/smw/article/download/2709/4341?inline=1

  Spence showed in 2002 that TPA was predictive for cardiovascular events with increasing risk in the higher quartiles of TPA. […] In 2003, Hollander reported on 6913 prospectively followed healthy subjects assessed in the Rotterdam study and found that carotid IMT (with inclusion of regions with carotid plaque) was a stronger predictor for ensuing stroke than carotid plaque alone. […] In 2004, Van der Meer reported on 6389 subjects from the Rotterdam study with assessments of carotid structural changes and found equal predictive power for ensuing myocardial infarction. […] In 2007, Stein et al. reported on 6226 originally healthy subjects assessed in the Norwegian Troms study and found TPA to be a stronger, statistically significant marker for incident myocardial infarction than IMT. […] In 2011, Polak reported that in 2965 members of the Framingham Offspring Study cohort evaluated for cardiovascular outcome during a follow-up of 7 years, both internal carotid IMT and internal carotid plaque formation improved the AUC significantly when compared with the Framingham risk equation.

• #29 Physical Activity, Atherosclerosis Levels Predict Mortality Outcome Among Patients With Cardiovascular Disease

  https://www.ajmc.com/view/physical-activity-atherosclerosis-levels-predict-mortality-outcome-among-patients-with-cardiovascular-disease

  Self-reported high levels of physical activity shared a positive correlation with improved mortality and care for patients who underwent chest CT scans for atherosclerosis. […] Overall results show that more physical activity was linked to an almost 71% lower annual mortality rate compared with less physical activity: 1.7% vs 2.9%. […] Our study showed that simply asking patients to rate their level of physical activity, while using a test to look at the plaque in their coronary arteries, markedly improved our ability to predict patients risk for dying over their next decade of life, stated Alan Rozanski, MD, director of nuclear cardiology and cardiac stress testing at the Icahn School of Medicine at Mount Sinai and the studys first author.

• #30 Development of a multidimensional prediction model for long-term prognostic risk in patients with acute coronary syndromes after percutaneous coronary intervention: A retrospective observational cohort study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0318445

  The aim of this study is to examine the critical variables that impact the long-term prognosis of patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI) and to create a multidimensional predictive risk assessment model that can serve as a theoretical basis for accurate cardiac rehabilitation. […] We found white blood cell count (WBC) (OR: 4.110) and the effective average number of daily steps (ANS) (OR: 2.689) as independent prognostic risk factors for acute myocardial infarction (AMI). The independent risk factors for unstable angina prognosis were white blood cell count (OR: 6.257), VO2 at anaerobic threshold (OR: 4.294), and effective autonomic nervous system function (OR: 4.097). […] This study developed a multimodal predictive model that integrates the inflammatory response after onset, physical performance and exercise tolerance before discharge, and daily activity after discharge to predict the long-term prognosis of patients with ACS. The multidimensional model is more effective than the single-factor model for assessing risk in ACS patients.

• #31 Development of a multidimensional prediction model for long-term prognostic risk in patients with acute coronary syndromes after percutaneous coronary intervention: A retrospective observational cohort study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0318445

  The aim of this study is to examine the critical variables that impact the long-term prognosis of patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI) and to create a multidimensional predictive risk assessment model that can serve as a theoretical basis for accurate cardiac rehabilitation. […] We found white blood cell count (WBC) (OR: 4.110) and the effective average number of daily steps (ANS) (OR: 2.689) as independent prognostic risk factors for acute myocardial infarction (AMI). The independent risk factors for unstable angina prognosis were white blood cell count (OR: 6.257), VO2 at anaerobic threshold (OR: 4.294), and effective autonomic nervous system function (OR: 4.097). […] This study developed a multimodal predictive model that integrates the inflammatory response after onset, physical performance and exercise tolerance before discharge, and daily activity after discharge to predict the long-term prognosis of patients with ACS. The multidimensional model is more effective than the single-factor model for assessing risk in ACS patients.

• #32 Development of a multidimensional prediction model for long-term prognostic risk in patients with acute coronary syndromes after percutaneous coronary intervention: A retrospective observational cohort study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0318445

  The aim of this study is to examine the critical variables that impact the long-term prognosis of patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI) and to create a multidimensional predictive risk assessment model that can serve as a theoretical basis for accurate cardiac rehabilitation. […] We found white blood cell count (WBC) (OR: 4.110) and the effective average number of daily steps (ANS) (OR: 2.689) as independent prognostic risk factors for acute myocardial infarction (AMI). The independent risk factors for unstable angina prognosis were white blood cell count (OR: 6.257), VO2 at anaerobic threshold (OR: 4.294), and effective autonomic nervous system function (OR: 4.097). […] This study developed a multimodal predictive model that integrates the inflammatory response after onset, physical performance and exercise tolerance before discharge, and daily activity after discharge to predict the long-term prognosis of patients with ACS. The multidimensional model is more effective than the single-factor model for assessing risk in ACS patients.

• #33 Sonographic assessment of carotid atherosclerosis: preferred risk indicator for future cardiovascular events?

  https://smw.ch/index.php/smw/article/download/2709/4341?inline=1

  The inclusion of carotid plaque quantification into cardiovascular risk prediction has significantly improved discrimination and reclassification of subjects in primary care. […] Cardiovascular risk increases with the carotid plaque burden quantified as TPA. […] Given the excellent predictive accuracy for cardiovascular events of carotid TPV, which is indeed comparable to presence and extent of coronary calcifications, it might be argued that carotid TPV should be used instead of carotid TPA. […] The American College of Cardiology Foundation (ACCF) / American Heart Association (AHA) 2010 guidelines for assessment of cardiovascular risk in asymptomatic adults issued a class IIa recommendation for coronary calcium assessment in subjects at intermediate risk. […] According to the latest Joint European Society of Cardiology (ESC) guidelines, carotid plaque but not carotid IMT was categorised as a very high-risk finding. […] The identification of subjects at increased risk for cardiovascular events using traditional risk factors is established in clinical practice. However, about 40% of low-risk subjects are classified as having low risk, despite the presence of prognostically relevant atherosclerosis.

• #34 Sonographic assessment of carotid atherosclerosis: preferred risk indicator for future cardiovascular events?

  https://smw.ch/index.php/smw/article/download/2709/4341?inline=1

  The inclusion of carotid plaque quantification into cardiovascular risk prediction has significantly improved discrimination and reclassification of subjects in primary care. […] Cardiovascular risk increases with the carotid plaque burden quantified as TPA. […] Given the excellent predictive accuracy for cardiovascular events of carotid TPV, which is indeed comparable to presence and extent of coronary calcifications, it might be argued that carotid TPV should be used instead of carotid TPA. […] The American College of Cardiology Foundation (ACCF) / American Heart Association (AHA) 2010 guidelines for assessment of cardiovascular risk in asymptomatic adults issued a class IIa recommendation for coronary calcium assessment in subjects at intermediate risk. […] According to the latest Joint European Society of Cardiology (ESC) guidelines, carotid plaque but not carotid IMT was categorised as a very high-risk finding. […] The identification of subjects at increased risk for cardiovascular events using traditional risk factors is established in clinical practice. However, about 40% of low-risk subjects are classified as having low risk, despite the presence of prognostically relevant atherosclerosis.

• #35 Comprehensive biomechanical and anatomical atherosclerotic plaque metrics predict major adverse cardiovascular events: A new tool for clinical decision making

  https://www.repository.cam.ac.uk/items/856128b5-7242-4b42-9fce-c9db37a2b4cc

  Background and aims: Anatomical imaging alone of coronary atherosclerotic plaques is insufficient to identify risk of future adverse events and guide management of non-culprit lesions. […] We determined whether combining multiple complementary, biomechanical and anatomical plaque characteristics improves outcome prediction sufficiently to inform clinical decision-making. […] High PSS HI (hazard ratio [HR] 3.9, p=0.006), high ESSG (HR 3.4, p=0.007) and plaque burden70% (HR 2.6, p=0.02) were independent outcome predictors in multivariate analysis. […] Combining complementary biomechanical and anatomical metrics significantly improves risk-stratification of individual coronary lesions. If confirmed from larger prospective studies, our results may inform targeted revascularisation vs. conservative management strategies.

• #36 Prediction of cardiovascular outcomes by imaging coronary atherosclerosis – Pathan – Cardiovascular Diagnosis and Therapy

  https://cdt.amegroups.org/article/view/9218/html

  The common outcomes reported in the coronary atherosclerosis are summarized in Tables 1 and 2, which illustrate significant heterogeneity in the definition of outcomes, not only the variety in definitions of MACE but the inclusion or exclusion of all-cause mortality and unstable angina in various trials. […] Given predictive value of a test is clearly influenced by the outcome measured it should serve as a caution pertaining to the subsequent discussion and indeed in any assessment of outcome measures. […] The evaluation of CTCA for long-term prognosis (5222 months) was studied by Andreini, demonstrating cumulative event-free survival of 100% for patients with normal coronary arteries, 88% for hard events in patients with non-obstructive CAD, and 54% in patients with obstructive CAD. […] The presence of significant stenosis on CMR was associated with a HR of 20.78 (95% CI, 2.65162; P=0.001).

• #37 Prediction of cardiovascular outcomes by imaging coronary atherosclerosis – Pathan – Cardiovascular Diagnosis and Therapy

  https://cdt.amegroups.org/article/view/9218/html

  The strength of IVUS is to facilitate real time decision making. […] The PROSPECT trial, which enrolled 697 ACS patients (secondary prevention), identified three characteristics predictive of subsequent cumulative MACE rate: plaque burden 70% (HR: 5.03; 95% CI, 2.5110.11, P0.001), a minimal lumen diameter (MLA) 4.0 (HR: 3.21; 95% CI, 1.616.42, P=0.001) or TCFA identified by RF IVUS (HR: 3.35; 95% CI, 1.776.36, P0.001). […] The strengths and limitations of invasive modalities have been summarized. Knowledge gap in the literature here is systematic implementation studies, which maximize the benefits of each diagnostic modality and minimize the drawbacks and costs.

• #38 Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5640485/

  Machine learning may be useful to characterize cardiovascular risk, predict outcomes and identify biomarkers in population studies. […] To test the ability of random survival forests (RF), a machine learning technique, to predict six cardiovascular outcomes in comparison to standard cardiovascular risk scores. […] MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of CV disease. […] Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary artery calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function, and cardiac troponin-T were among the top predictors for incident heart failure.

• #39 Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5640485/

  Machine learning in conjunction with deep phenotyping improve prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. […] The RF technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 1025%). […] For incident heart failure as the endpoint, cardiac chamber stress (increased LV volume, and increased NT-proBNP levels), and decreased LV function from MRI were the most important markers. […] For incident atrial fibrillation as the endpoint, inflammation, higher levels of creatinine, atherosclerosis (CAC and ABI), and repolarization abnormalities were the most important markers. […] The results of this study suggest that machine learning methods are well-suited for meaningful risk prediction in extensively phenotyped large-scale epidemiological studies. […] The RF based method of risk prediction provided better event prediction over standard risk scores. […] Inflammation, subclinical atherosclerosis, myocardial damage, and cardiac chamber stress were among the most important predictors across all outcomes.

• #40 Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5640485/

  Machine learning in conjunction with deep phenotyping improve prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. […] The RF technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 1025%). […] For incident heart failure as the endpoint, cardiac chamber stress (increased LV volume, and increased NT-proBNP levels), and decreased LV function from MRI were the most important markers. […] For incident atrial fibrillation as the endpoint, inflammation, higher levels of creatinine, atherosclerosis (CAC and ABI), and repolarization abnormalities were the most important markers. […] The results of this study suggest that machine learning methods are well-suited for meaningful risk prediction in extensively phenotyped large-scale epidemiological studies. […] The RF based method of risk prediction provided better event prediction over standard risk scores. […] Inflammation, subclinical atherosclerosis, myocardial damage, and cardiac chamber stress were among the most important predictors across all outcomes.

• #41 Development of a multidimensional prediction model for long-term prognostic risk in patients with acute coronary syndromes after percutaneous coronary intervention: A retrospective observational cohort study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0318445

  The aim of this study is to examine the critical variables that impact the long-term prognosis of patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI) and to create a multidimensional predictive risk assessment model that can serve as a theoretical basis for accurate cardiac rehabilitation. […] We found white blood cell count (WBC) (OR: 4.110) and the effective average number of daily steps (ANS) (OR: 2.689) as independent prognostic risk factors for acute myocardial infarction (AMI). The independent risk factors for unstable angina prognosis were white blood cell count (OR: 6.257), VO2 at anaerobic threshold (OR: 4.294), and effective autonomic nervous system function (OR: 4.097). […] This study developed a multimodal predictive model that integrates the inflammatory response after onset, physical performance and exercise tolerance before discharge, and daily activity after discharge to predict the long-term prognosis of patients with ACS. The multidimensional model is more effective than the single-factor model for assessing risk in ACS patients.

• #42 Development of a multidimensional prediction model for long-term prognostic risk in patients with acute coronary syndromes after percutaneous coronary intervention: A retrospective observational cohort study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0318445

  This study found that the inflammatory response after onset, physical performance and exercise tolerance before discharge, and daily activity after discharge were the independent risk factors for predicting the long-term prognosis of patients with ACS. The multidimensional prognostic model to risk-stratify for the patients with ACS, was better than the single factor model. This study also provides a theoretical basis that the prognosis of potentially high-risk patients can be improved by precise and rational exercise prescription.

• #43 Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis | Scientific Reports

  https://www.nature.com/articles/s41598-023-42338-0

  Large-artery atherosclerosis (LAA) is a leading cause of cerebrovascular disease. However, LAA diagnosis is costly and needs professional identification. […] Therefore, there is an urgent clinical need to identify novel and more efficient biomarkers for predicting the risk of LAA, which can be achieved through the general blood tests. […] Our study has demonstrated the effectiveness of combining machine learning algorithms with recursive feature elimination and cross-validation methods for biomarker identification. […] We found that 27 features were present among the five adopted models that could provide good results. If these 27 features were used in the LR model, an AUC value of 0.93 could be achieved. […] We found (1) The combination of clinical factor and metabolite profile provides stability to data set shifts; (2) with feature selection method we improved the model performance from an AUC of 0.89 to 0.92; (3) the shared features had predictive power equivalent to 67 features, suggesting their clinical importance in identifying patients with LAA. […] The performance metrics could be improved if a feature selection step was added and both clinical factors and metabolites could be combined for disease prediction. […] Our study may help to early predict and prevent the progression of LAA.

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