Abstract 196: Development and Validation of Machine Learning-based Race-specific Models to Predict 10-year Risk of Heart Failure: A Multi-cohort Analysis

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Matthew W Segar ◽  
Byron Jaeger ◽  
Kershaw V Patel ◽  
Vijay Nambi ◽  
Chiadi E Ndumele ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Heart Failure ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Superior Performance ◽  
Risk Models ◽  
Black And White ◽  
White Adults

Introduction: Heart failure (HF) risk and the underlying biological risk factors vary by race. Machine learning (ML) may improve race-specific HF risk prediction but this has not been fully evaluated. Methods: The study included participants from 4 cohorts (ARIC, DHS, JHS, and MESA) aged > 40 years, free of baseline HF, and with adjudicated HF event follow-up. Black adults from JHS and white adults from ARIC were used to derive race-specific ML models to predict 10-year HF risk. The ML models were externally validated in subgroups of black and white adults from ARIC (excluding JHS participants) and pooled MESA/DHS cohorts and compared to prior established HF risk scores developed in ARIC and MESA. Harrell’s C-index and Greenwood-Nam-D’Agostino chi-square were used to assess discrimination and calibration, respectively. Results: In the derivation cohorts, 288 of 4141 (7.0%) black and 391 of 8242 (4.7%) white adults developed HF over 10 years. The ML models had excellent discrimination in both black and white participants (C-indices = 0.88 and 0.89). In the external validation cohorts for black participants from ARIC (excluding JHS, N = 1072) and MESA/DHS pooled cohorts (N = 2821), 131 (12.2%) and 115 (4.1%) developed HF. The ML model had adequate calibration and demonstrated superior discrimination compared to established HF risk models (Fig A). A consistent pattern was also observed in the external validation cohorts of white participants from the MESA/DHS pooled cohorts (N=3236; 100 [3.1%] HF events) (Fig A). The most important predictors of HF in both races were NP levels. Cardiac biomarkers and glycemic parameters were most important among blacks while LV hypertrophy and prevalent CVD and traditional CV risk factors were the strongest predictors among whites (Fig B). Conclusions: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance when compared to traditional risk prediction models.

scholarly journals Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multi-Cohort Analysis

Circulation ◽  
2021 ◽  
Author(s):  
Matthew W. Segar ◽  
Byron C. Jaeger ◽  
Kershaw V. Patel ◽  
Vijay Nambi ◽  
Chiadi E. Ndumele ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Heart Failure ◽  
Risk Prediction ◽  
Prediction Models ◽  
Cohort Analysis ◽  
Specific Model ◽  
Superior Performance ◽  
Risk Models ◽  
White Adults

Background: Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine learning (ML) may offer advantages over traditional modeling techniques to develop race-specific HF risk prediction models and elucidate important contributors of HF development across races. Methods: We performed a retrospective analysis of four large, community cohort studies (ARIC, DHS, JHS, and MESA) with adjudicated HF events. Participants were aged >40 years and free of HF at baseline. Race-specific ML models for HF risk prediction were developed in the JHS cohort (for Black race-specific model) and White adults from ARIC (for White rate-specific model). The models included 39 candidate variables across demographic, anthropometric, medical history, laboratory, and electrocardiographic domains. The ML models were externally validated and compared with prior established traditional and non-race specific ML models in race-specific subgroups of the pooled MESA/DHS cohort and Black participants of ARIC. Harrell's C-index and Greenwood-Nam-D'Agostino chi-square tests were used to assess discrimination and calibration, respectively. Results: The ML models had excellent discrimination in the derivation cohorts for Black (N=4,141 in JHS, C-index=0.88) and White (N=7,858 in ARIC, C-index=0.89) participants. In the external validation cohorts, the race-specific ML model demonstrated adequate calibration and superior discrimination (C-indices=0.80-0.83 [for Black individuals] and 0.82 [for White individuals]) compared with established HF risk models or with non-race specific ML models derived using race as a covariate. Among the risk factors, natriuretic peptide levels were the most important predictor of HF risk across both races, followed by troponin levels in Black and EKG-based Cornell voltage in White individuals. Other key predictors of HF risk among Black individuals were glycemic parameters and socioeconomic factors. In contrast, prevalent cardiovascular (CV) disease and traditional CV risk factors were stronger predictors of HF risk in White adults. Conclusions: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance when compared with traditional HF risk and non-race specific ML models. This approach identifies distinct race-specific contributors of HF.

scholarly journals Deep Neural Survival Networks for Cardiovascular Risk Prediction: The Multi-Ethnic Study of Atherosclerosis (MESA)

2021 ◽  
Author(s):  
Quincy A Hathaway ◽  
Naveena Yanamala ◽  
Matthew J Budoff ◽  
Partho P Sengupta ◽  
Irfan Zeb
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Deep Learning ◽  
Risk Prediction ◽  
External Validation ◽  
Imaging Biomarkers ◽  
Atherosclerotic Cardiovascular Disease ◽  
Ethnic Study ◽  
Ascvd Risk ◽  
Traditional Risk Factors

Background: There is growing interest in utilizing machine learning techniques for routine atherosclerotic cardiovascular disease (ASCVD) risk prediction. We investigated whether novel deep learning survival models can augment ASCVD risk prediction over existing statistical and machine learning approaches. Methods: 6,814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) were followed over 16 years to assess incidence of all-cause mortality (mortality) or a composite of major adverse events (MAE). Features were evaluated within the categories of traditional risk factors, inflammatory biomarkers, and imaging markers. Data was split into an internal training/testing (four centers) and external validation (two centers). Both machine learning (COXPH, RSF, and lSVM) and deep learning (nMTLR and DeepSurv) models were evaluated. Results: In comparison to the COXPH model, DeepSurv significantly improved ASCVD risk prediction for MAE (AUC: 0.82 vs. 0.79, P≤0.001) and mortality (AUC: 0.86 vs. 0.80, P≤0.001) with traditional risk factors alone. Implementing non-categorical NRI, we noted a 65% increase in correct reclassification compared to the COXPH model for both MAE and mortality (P≤0.05). Assessing the relative risk of participants, DeepSurv was the only learning algorithm to develop a significantly improved risk score criteria, which outcompeted COXPH for both MAE (4.07 vs. 2.66, P≤0.001) and mortality (6.28 vs. 4.67, P=0.014). The addition of inflammatory or imaging biomarkers to traditional risk factors showed minimal/no significant improvement in model prediction. Conclusion: DeepSurv can leverage simple office-based clinical features alone to accurately predict ASCVD risk and cardiovascular outcomes, without the need for additional features, such as inflammatory and imaging biomarkers.

Abstract P280: Revisiting CVD Risk Prediction Using Machine Learning Approaches: A Case Study

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Hesam Dashti ◽  
Yanyan Liu ◽  
Robert J Glynn ◽  
Paul M Ridker ◽  
Samia Mora ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Risk Prediction ◽  
Prediction Models ◽  
Baseline Risk ◽  
Case Group ◽  
Ann Model ◽  
Biomedical Image Processing ◽  
Learning Methods ◽  
Machine Learning Methods

Introduction: Applications of machine learning (ML) methods have been demonstrated by the recent FDA approval of new ML-based biomedical image processing methods. In this study, we examine applications of ML, specifically artificial neural networks (ANN), for predicting risk of cardiovascular (CV) events. Hypothesis: We hypothesized that using the same CV risk factors, ML-based CV prediction models can improve the performance of current predictive models. Methods: Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER; NCT00239681) is a multi-ethnic trial that randomized non-diabetic participants with LDL-C<130 mg/dL and hsCRP≥2 mg/L to rosuvastatin versus placebo. We restricted the analysis to white and black participants allocated to the placebo arm, and estimated the race- and sex-specific Pooled Cohorts Equations (PCE) 5-year risk score using race, sex, age, HDL-C, total cholesterol, systolic BP, antihypertensive medications, and smoking. A total of 218 incident CV cases occurred (maximum follow-up 5 years). For every participant in the case group, we randomly selected 4 controls from the placebo arm after stratifying for the baseline risk factors (Table 1). The risk factors from a total of n=1,090 participants were used to train and test the ANN model. We used 80% of the participants (n=872) for designing the network and left out 20% of the data (n=218) for testing the predictive model. We used the TensorFlow software to design, train, and evaluate the ANN model. Results: We compared the performances of the ANN and the PCE score on the 218 test subjects (Figure 1). The high AUC of the neural network (0.85; 95% CI 0.78-0.91) on this dataset suggests advantages of machine learning methods compared to the current methods. Conclusions: This result demonstrates the potential of machine learning methods for enhancing and improving the current techniques used in cardiovascular risk prediction and should be evaluated in other cohorts.

scholarly journals Risk Prediction Models for Melanoma: A Systematic Review on the Heterogeneity in Model Development and Validation

2020 ◽  
Vol 17 (21) ◽  
pp. 7919
Author(s):  
Isabelle Kaiser ◽  
Annette B. Pfahlberg ◽  
Wolfgang Uter ◽  
Markus V. Heppt ◽  
Marit B. Veierød ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Risk Prediction Models ◽  
Screening Programs ◽  
Development And Validation ◽  
Selection And Assessment

The rising incidence of cutaneous melanoma over the past few decades has prompted substantial efforts to develop risk prediction models identifying people at high risk of developing melanoma to facilitate targeted screening programs. We review these models, regarding study characteristics, differences in risk factor selection and assessment, evaluation, and validation methods. Our systematic literature search revealed 40 studies comprising 46 different risk prediction models eligible for the review. Altogether, 35 different risk factors were part of the models with nevi being the most common one (n = 35, 78%); little consistency in other risk factors was observed. Results of an internal validation were reported for less than half of the studies (n = 18, 45%), and only 6 performed external validation. In terms of model performance, 29 studies assessed the discriminative ability of their models; other performance measures, e.g., regarding calibration or clinical usefulness, were rarely reported. Due to the substantial heterogeneity in risk factor selection and assessment as well as methodologic aspects of model development, direct comparisons between models are hardly possible. Uniform methodologic standards for the development and validation of risk prediction models for melanoma and reporting standards for the accompanying publications are necessary and need to be obligatory for that reason.

scholarly journals Developing an Explainable Machine Learning-Based Personalised Dementia Risk Prediction Model: A Transfer Learning Approach With Ensemble Learning Algorithms

2021 ◽  
Vol 4 ◽  
Author(s):  
Samuel O. Danso ◽  
Zhanhang Zeng ◽  
Graciela Muniz-Terrera ◽  
Craig W. Ritchie
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Transfer Learning ◽  
Risk Prediction ◽  
Clinical Utility ◽  
Prediction Models ◽  
Source Model ◽  
Geometric Accuracy ◽  
Risk Prediction Models ◽  
Dementia Risk

Alzheimer's disease (AD) has its onset many decades before dementia develops, and work is ongoing to characterise individuals at risk of decline on the basis of early detection through biomarker and cognitive testing as well as the presence/absence of identified risk factors. Risk prediction models for AD based on various computational approaches, including machine learning, are being developed with promising results. However, these approaches have been criticised as they are unable to generalise due to over-reliance on one data source, poor internal and external validations, and lack of understanding of prediction models, thereby limiting the clinical utility of these prediction models. We propose a framework that employs a transfer-learning paradigm with ensemble learning algorithms to develop explainable personalised risk prediction models for dementia. Our prediction models, known as source models, are initially trained and tested using a publicly available dataset (n = 84,856, mean age = 69 years) with 14 years of follow-up samples to predict the individual risk of developing dementia. The decision boundaries of the best source model are further updated by using an alternative dataset from a different and much younger population (n = 473, mean age = 52 years) to obtain an additional prediction model known as the target model. We further apply the SHapely Additive exPlanation (SHAP) algorithm to visualise the risk factors responsible for the prediction at both population and individual levels. The best source model achieves a geometric accuracy of 87%, specificity of 99%, and sensitivity of 76%. In comparison to a baseline model, our target model achieves better performance across several performance metrics, within an increase in geometric accuracy of 16.9%, specificity of 2.7%, and sensitivity of 19.1%, an area under the receiver operating curve (AUROC) of 11% and a transfer learning efficacy rate of 20.6%. The strength of our approach is the large sample size used in training the source model, transferring and applying the “knowledge” to another dataset from a different and undiagnosed population for the early detection and prediction of dementia risk, and the ability to visualise the interaction of the risk factors that drive the prediction. This approach has direct clinical utility.

scholarly journals Machine Learning-Based Prediction of Air Quality

2020 ◽  
Vol 10 (24) ◽  
pp. 9151
Author(s):  
Yun-Chia Liang ◽  
Yona Maimury ◽  
Angela Hsiang-Ling Chen ◽  
Josue Rodolfo Cuevas Juarez
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Prediction Models ◽  
Superior Performance ◽  
Support Vector ◽  
Economic Activities ◽  
Adaptive Boosting ◽  
Series Of Experiments ◽  
Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

scholarly journals Evaluating risk prediction models for adults with heart failure: A systematic literature review

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0224135 ◽  
Author(s):  
Gian Luca Di Tanna ◽  
Heidi Wirtz ◽  
Karen L. Burrows ◽  
Gary Globe
Keyword(s):  
Heart Failure ◽  
Literature Review ◽  
Risk Prediction ◽  
Systematic Literature Review ◽  
Prediction Models ◽  
Risk Prediction Models

scholarly journals Risk Factors and Prediction Models for Venous Thromboembolism in Ambulatory Patients with Lung Cancer

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 778
Author(s):  
Ann-Rong Yan ◽  
Indira Samarawickrema ◽  
Mark Naunton ◽  
Gregory M. Peterson ◽  
Desmond Yip ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Prediction Models ◽  
Poor Performance ◽  
Risk Models ◽  
Related Risk ◽  
Wide Range ◽  
Ambulatory Patients

Venous thromboembolism (VTE) is a significant cause of mortality in patients with lung cancer. Despite the availability of a wide range of anticoagulants to help prevent thrombosis, thromboprophylaxis in ambulatory patients is a challenge due to its associated risk of haemorrhage. As a result, anticoagulation is only recommended in patients with a relatively high risk of VTE. Efforts have been made to develop predictive models for VTE risk assessment in cancer patients, but the availability of a reliable predictive model for ambulate patients with lung cancer is unclear. We have analysed the latest information on this topic, with a focus on the lung cancer-related risk factors for VTE, and risk prediction models developed and validated in this group of patients. The existing risk models, such as the Khorana score, the PROTECHT score and the CONKO score, have shown poor performance in external validations, failing to identify many high-risk individuals. Some of the newly developed and updated models may be promising, but their further validation is needed.

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