scholarly journals Role of Cardiac Biomarkers in Epidemiology and Risk Outcomes

2020 ◽  
Author(s):  
Paul M Haller ◽  
Benedikt N Beer ◽  
Andrew M Tonkin ◽  
Stefan Blankenberg ◽  
Johannes T Neumann
Keyword(s):  
At Risk ◽  
Risk Prediction ◽  
Natriuretic Peptides ◽  
Cardiovascular Events ◽  
Large Scale ◽  
Prediction Models ◽  
Interleukin 1 ◽  
Cardiac Biomarkers ◽  
Current Evidence ◽  
Precise Identification

Abstract Background The use of biomarkers associated with cardiovascular disease (CVD) is established for diagnostic purposes. Cardiac troponins, as specific markers of myocardial injury, and natriuretic peptides, reflecting myocardial dilation, are routinely used for diagnosis in clinical practice. In addition, a substantial body of research has shed light on the ability of biomarkers to reflect the risk of future major cardiovascular events. Among biomarkers, troponin and members of the natriuretic peptide family have been investigated extensively in the general population, in those at higher risk, and in patients with known CVD. Both biomarkers have been shown to contribute substantially to statistical models describing cardiovascular risk, in addition to and independently of important clinical characteristics. The more precise identification of individuals at risk by appropriate use of biomarkers might lead to an earlier initiation of preventive therapies and potentially avoid significant events. Content We summarize the current evidence concerning risk prediction using cardiac biomarkers at different stages in the development of CVD and provide examples of observational studies and large-scale clinical trials testing such application. Beyond the focus on troponin and natriuretic peptides, we also discuss other important and emerging biomarkers in the field with potential for such application, including growth differentiation factor-15, soluble ST2 (alias for IL1RL1 [interleukin 1 receptor like 1), and galectin-3. Summary Incorporating biomarkers in risk prediction models might allow more precise identification of individuals at risk. Among the various biomarkers, cardiac troponin appears to be the most promising for prediction of future cardiovascular events in a wide variety of patient populations.

scholarly journals Breast Cancer Risks and Risk Prediction Models

Breast Care ◽  
2015 ◽  
Vol 10 (1) ◽  
pp. 7-12 ◽  
Author(s):  
Christoph Engel ◽  
Christine Fischer
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
At Risk ◽  
Risk Prediction ◽  
Clinical Management ◽  
Prediction Models ◽  
Cancer Risks ◽  
Large Variability ◽  
Risk Prediction Models ◽  
Mutation Carriers

BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

scholarly journals Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

2021 ◽  
Vol 22 (19) ◽  
pp. 10291
Author(s):  
Annie M. Westerlund ◽  
Johann S. Hawe ◽  
Matthias Heinig ◽  
Heribert Schunkert
Keyword(s):  
Artificial Intelligence ◽  
Risk Prediction ◽  
Cardiovascular Events ◽  
Regulatory Networks ◽  
Recurrent Events ◽  
Large Scale ◽  
Treatment Strategies ◽  
Molecular Data ◽  
Holistic View ◽  
Prediction And Prevention

Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.

scholarly journals Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events

2021 ◽  
Author(s):  
Michael Schrempf ◽  
Diether Kramer ◽  
Stefanie Jauk ◽  
Sai P. K. Veeranki ◽  
Werner Leodolter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Test Data ◽  
Cardiovascular Events ◽  
Prediction Models ◽  
Early Stage ◽  
Training Data ◽  
Major Adverse Cardiovascular Events ◽  
Future Research ◽  
Patients At Risk

Background: Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented with the right interventions. Objectives: The aim of this study was to develop machine learning-based models for the 5-year risk prediction of MACE. Methods: The data used for modelling included electronic medical records of more than 128,000 patients including 29,262 patients with MACE. A feature selection based on filter and embedded methods resulted in 826 features for modelling. Different machine learning methods were used for modelling on the training data. Results: A random forest model achieved the best calibration and discriminative performance on a separate test data set with an AUROC of 0.88. Conclusion: The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.

scholarly journals Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdowns

2020 ◽  
Author(s):  
Abhinav Gola ◽  
Ravi Kumar Arya ◽  
Animesh Animesh ◽  
Ravi Dugh
Keyword(s):  
Risk Prediction ◽  
Large Scale ◽  
Prediction Models ◽  
Short Term Memory ◽  
Arima Model ◽  
Adverse Outcomes ◽  
Incidence And Mortality ◽  
Exponential Growth Model ◽  
Policy Guidance ◽  
Effective Public Health

Background: COVID-19 is widely spreading across the globe right now. While some countries have flattened the curve, others are struggling to control the spread of the infection. Precise risk prediction modeling is key to accurate prevention and containment of COVID-19 infection, as well as for the preparation of resources needed to deal with the pandemic in different regions. Methods: Given the vast differences in approaches and scenarios used by these models to predict future infection rates, in this study, we compared the accuracy among different models such as regression models, ARIMA model, multilayer perceptron, vector autoregression, susceptible exposed infected recovered (SEIR), susceptible infected recovered (SIR), recurrent neural networks (RNNs), long short term memory networks (LSTM) and exponential growth model in prediction of the total COVID-19 confirmed cases. We did so by comparing the predicted rates of these models with actual rates of COVID-19 in India during the nationwide lockdowns. Results: Few of these models accurately predicted COVID-19 incidence and mortality rates in six weeks, though some provided close results. While advanced warning can help mitigate and prepare for an impending or ongoing epidemic, using poorly fitting models for prediction could lead to substantial adverse outcomes. Implications: As the COVID-19 pandemic continues, accurate risk prediction is key to effective public health interventions. Caution should be taken when choosing different risk prediction models based on specific scenarios and needs. To improve risk prediction of infectious disease such as COVID-19 for policy guidance and recommendations on best practices, both internal (e.g., specific virus characteristics in transmission and mutation) and external factors (e.g., large-scale human behaviors such as school opening, parties, and breaks) should be considered and appropriately weighed.

Abstract 5100: C-Reactive Protein and Parental History Improve Global Cardiovascular Risk Prediction: The Reynolds Risk Score for Men

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Paul M Ridker ◽  
Nina P Paynter ◽  
Nader Rifai ◽  
Michael Gaziano ◽  
Nancy R Cook
Keyword(s):  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Risk Score ◽  
Cardiovascular Events ◽  
Prediction Models ◽  
Prediction Algorithm ◽  
Parental History ◽  
Cardiovascular Risk Prediction ◽  
Study Population ◽  
Reynolds Risk Score

Background. CRP and family history independently associate with future cardiovascular events and have been incorporated into risk prediction models for women (the Reynolds Risk Score for women). However, no cardiovascular risk prediction algorithm incorporating these variables currently exists for men. Methods. Among 10,724 initially healthy American non-diabetic men who were followed prospectively for incident cardiovascular events over a median period of 10.8 years, we developed a cardiovascular risk prediction model that included hsCRP and parental history of myocardial infarction before age 60 years, and compared model fit, discrimination, and reclassification to prediction models limited to age, blood pressure, smoking, total cholesterol, and high-density lipoprotein cholesterol. Results. 1,294 cardiovascular events accrued during study follow-up. Predictive models incorporating hsCRP and parental history (the Reynolds Risk Score for men) had better global fit (P<0.001), a superior (lower) Bayes Information Criterion (BIC)(23008 vs 23048), and larger C-indexes (0.708 vs 0.699, P < 0.001) than did predictive models without these variables. For the endpoint of all cardiovascular events, the Reynolds Risk Score for men reclassified 17.8 percent of the study population into higher- or lower-risk categories with markedly improved accuracy among those reclassified. In models based on the ATP-III preferred endpoint of coronary heart disease and limited to men not taking lipid-lowering therapy, 16.7 percent of the study population were reclassified to higher- or lower-risk groups, again with significantly improved global fit (P<0.001), smaller BIC (13870 vs 13891), larger C-index (0.714 vs 0.704, P < 0.001), and almost perfect accuracy among those reclassified (99.9 percent). For this model, NRI was 8.4 percent and CNRI 15.8 percent (both P-values < 0.001). Conclusion. We developed an improved global risk prediction algorithm for men incorporating hsCRP and parental history that should allow better targeting of preventive therapies to maximize benefit while minimizing toxicity and cost.

Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention

2018 ◽  
Vol 57 (3) ◽  
pp. 547-570 ◽  
Author(s):  
Wanli Xing ◽  
Dongping Du
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
At Risk Students ◽  
Individual Student ◽  
Intervention Design ◽  
Temporal Prediction ◽  
Personalized Intervention

Massive open online courses (MOOCs) show great potential to transform traditional education through the Internet. However, the high attrition rates in MOOCs have often been cited as a scale-efficacy tradeoff. Traditional educational approaches are usually unable to identify such large-scale number of at-risk students in danger of dropping out in time to support effective intervention design. While building dropout prediction models using learning analytics are promising in informing intervention design for these at-risk students, results of the current prediction model construction methods do not enable personalized intervention for these students. In this study, we take an initial step to optimize the dropout prediction model performance toward intervention personalization for at-risk students in MOOCs. Specifically, based on a temporal prediction mechanism, this study proposes to use the deep learning algorithm to construct the dropout prediction model and further produce the predicted individual student dropout probability. By taking advantage of the power of deep learning, this approach not only constructs more accurate dropout prediction models compared with baseline algorithms but also comes up with an approach to personalize and prioritize intervention for at-risk students in MOOCs through using individual drop out probabilities. The findings from this study and implications are then discussed.

Community-Acquired Pneumonia Patients at Risk for Early and Long-term Cardiovascular Events Are Identified by Cardiac Biomarkers

CHEST Journal ◽  
2019 ◽  
Vol 156 (6) ◽  
pp. 1080-1091 ◽  
Author(s):  
Rosario Menéndez ◽  
Raúl Méndez ◽  
Irene Aldás ◽  
Soledad Reyes ◽  
Paula Gonzalez-Jimenez ◽  
...  
Keyword(s):  
At Risk ◽  
Cardiovascular Events ◽  
Community Acquired Pneumonia ◽  
Cardiac Biomarkers ◽  
Patients At Risk

scholarly journals Predicting Major Adverse Cardiovascular Events in Asian Type 2 Diabetes Patients With Lasso-Cox Regression

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A417-A418
Author(s):  
Amanda Yun Rui Lam ◽  
Min Min Chan ◽  
David Carmody ◽  
Ming Ming Teh ◽  
Yong Mong Bee ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Cardiovascular Events ◽  
Prediction Models ◽  
Cox Regression ◽  
Time Dependent ◽  
Major Adverse Cardiovascular Events ◽  
Risk Prediction Models ◽  
Diabetes Registry ◽  
Individual Survival

Abstract Background: South-East Asia has seen a dramatic increase in type 2 diabetes (T2D). Risk prediction models for Major adverse cardiovascular events (MACE) identify patients who may benefit most from intensive prevention strategies. Existing risk prediction models for T2D were developed mainly in Caucasian populations, limiting their generalizability to Asian populations. We developed a Lasso-Cox regression model to predict the 5-year risk of incident MACE in Asian patients with T2DM using data from the largest diabetes registry in Singapore. Methodology: The diabetes registry contained public healthcare data from 9 primary healthcare centers, 4 hospitals and 3 national specialty centers. Data from 120,131 T2D subjects without MACE at baseline, from 2008 to 2018, were used for model development and validation. Patients with less than 5 years of follow-up data were excluded. Lasso-Cox, a semi-parametric variant of the Cox Proportional Hazard Model with l1-regularization, was used to predict individual survival distribution of incident MACE. A total of 69 features within electronic health records, including demographic data, vital signs, laboratory tests, and prescriptions for blood pressure, lipid and glucose-lowering medication were supplied to the model. Regression shrinkage and selection via the lasso method was used to identify variables associated with incident MACE. Identified variables were used to generate individual survival probability curves. Incident MACE was defined as the first occurrence of nonfatal myocardial infarction, nonfatal stroke, and CV disease-related death. Results: A total of 12,535 (10.4%) subjects developed MACE between 2008 and 2018. Model performance was evaluated by time-dependent concordance index and Brier score at 1, 2 and 5 years. The results of 5-fold cross validation shows that the model displayed good discrimination, achieving time-dependent C-statistics of 0.746±0.005, 0.742±0.003 and 0.738±0.002 at 1, 2 and 5 years respectively. The model demonstrated low Brier scores of 0.0355±0.0004, 0.0601±0.0011, 0.104±0.004 at 1, 2 and 5 years respectively, indicating good calibration. Factors most predictive of MACE were age and a history of hypertension and hyperlipidemia. Conclusions: We have developed a risk prediction model for MACE in Asian T2D using a large Singaporean T2D cohort, which can be used to support clinical decision-making. The individual survival probability estimates achieve an average C-statistics of 0.742 and are well-calibrated at 1, 2 and 5 years.

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