Making Informed CHOICES: The Launch of a “Big Data” Pragmatic Trial to Improve Cholesterol Management and Prevent Heart Disease in Ontario

BACKGROUND Predictions of cardiovascular disease risks based on health records have long attracted broad research interests. Despite extensive efforts, the prediction accuracy has remained unsatisfactory. This raises the question as to whether the data insufficiency, statistical and machine-learning methods, or intrinsic noise have hindered the performance of previous approaches, and how these issues can be alleviated. OBJECTIVE Based on a large population of patients with hypertension in Shenzhen, China, we aimed to establish a high-precision coronary heart disease (CHD) prediction model through big data and machine-learning METHODS Data from a large cohort of 42,676 patients with hypertension, including 20,156 patients with CHD onset, were investigated from electronic health records (EHRs) 1-3 years prior to CHD onset (for CHD-positive cases) or during a disease-free follow-up period of more than 3 years (for CHD-negative cases). The population was divided evenly into independent training and test datasets. Various machine-learning methods were adopted on the training set to achieve high-accuracy prediction models and the results were compared with traditional statistical methods and well-known risk scales. Comparison analyses were performed to investigate the effects of training sample size, factor sets, and modeling approaches on the prediction performance. RESULTS An ensemble method, XGBoost, achieved high accuracy in predicting 3-year CHD onset for the independent test dataset with an area under the receiver operating characteristic curve (AUC) value of 0.943. Comparison analysis showed that nonlinear models (K-nearest neighbor AUC 0.908, random forest AUC 0.938) outperform linear models (logistic regression AUC 0.865) on the same datasets, and machine-learning methods significantly surpassed traditional risk scales or fixed models (eg, Framingham cardiovascular disease risk models). Further analyses revealed that using time-dependent features obtained from multiple records, including both statistical variables and changing-trend variables, helped to improve the performance compared to using only static features. Subpopulation analysis showed that the impact of feature design had a more significant effect on model accuracy than the population size. Marginal effect analysis showed that both traditional and EHR factors exhibited highly nonlinear characteristics with respect to the risk scores. CONCLUSIONS We demonstrated that accurate risk prediction of CHD from EHRs is possible given a sufficiently large population of training data. Sophisticated machine-learning methods played an important role in tackling the heterogeneity and nonlinear nature of disease prediction. Moreover, accumulated EHR data over multiple time points provided additional features that were valuable for risk prediction. Our study highlights the importance of accumulating big data from EHRs for accurate disease predictions.

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A Systematic Framework for Heart Disease Prediction Using Big Data Analytics

10.1007/978-3-030-74150-1_13 ◽

2021 ◽

pp. 277-305

Author(s):

T. Poongodi ◽

R. Indrakumari ◽

S. Janarthanan ◽

P. Suresh

Keyword(s):

Big Data ◽

Heart Disease ◽

Data Analytics ◽

Big Data Analytics ◽

Disease Prediction ◽

Systematic Framework

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Real-time machine learning for early detection of heart disease using big data approach

2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS) ◽

10.1109/wits.2019.8723839 ◽

2019 ◽

Cited By ~ 4

Author(s):

Abderrahmane Ed-Daoudy ◽

Khalil Maalmi

Keyword(s):

Machine Learning ◽

Big Data ◽

Heart Disease ◽

Early Detection ◽

Real Time ◽

Time Machine

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Preventing cardiovascular heart disease: Promising nutraceutical and non-nutraceutical treatments for cholesterol management

Pharmacological Research ◽

10.1016/j.phrs.2017.04.008 ◽

2017 ◽

Vol 120 ◽

pp. 219-225 ◽

Cited By ~ 49

Author(s):

T.P. Johnston ◽

T.A. Korolenko ◽

M. Pirro ◽

A. Sahebkar

Keyword(s):

Heart Disease ◽

Cardiovascular Heart Disease ◽

Cholesterol Management

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P4344An example that big data analysis is ready for the prime time

European Heart Journal ◽

10.1093/eurheartj/ehz745.0752 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Author(s):

C Cortina ◽

M Sarrion ◽

L Mora ◽

V Suberviola ◽

C Beltran ◽

...

Keyword(s):

Big Data ◽

Heart Disease ◽

Data Analysis ◽

Mitral Regurgitation ◽

Aortic Stenosis ◽

Aortic Regurgitation ◽

Relevant Information ◽

Big Data Analysis ◽

Chronic Obstructive ◽

Total Prevalence

Abstract Introduction Data about the epidemiology of valvular heart disease (VHD) is scarce. The increasing aging of the population may cause an augmented prevalence of VHD, with a great number of comorbidities that conveys a higher surgical risk. The aim of this study was to describe the prevalence of VHD in the patients attended at our institution from 2007 until 2017 and to describe the main characteristics of this population. Methods We used a new tool based on EHRead Technology to extract clinical relevant information from Electronic Health Records, designed for descriptive and predictive big data analysis. All medical reports generated at the outpatient clinic, ER or hospitalization ward were examined. Patients with a diagnosis of moderate or severe VHD were selected. The prevalence of VHD was also estimated in 2 quintiles, from 2008 until Feb 2013 and from March 2013 until Dec 2017. Results The total prevalence of VHD in our population was 1.04% (n=3431). Mitral regurgitation was the most frequent valvular lesion (0.4%, n=1318), followed by aortic stenosis (0.3%, n=967) and aortic regurgitation (0.28%, n=938). There was a clear female predominance (63%), and the median age was 76.4. In the 1st quintile the prevalence of VHD was 0.25%, and increased to 0.79% in the 2nd. This trend was consistent in all type of valvular lesions. The prevalence of comorbidities was higher than in other epidemiological studies (Table). Prevalence of comorbidities Severe MR Severe AS Severe AR Euro Heart Valve Survey Hypertension 54,5% 69,1% 47,9% 49% Dyslipidemia 32,2% 40,6% 27,4% 35% Diabetes Mellitus 28,0% 31,5% 16,4% 15% Smoking (current) 5,6% 5,4% 13,7% 39% Coronary heart disease 12,0% 17,0% 12,3% 13% Stroke 7,0% 8,9% 5,5% 7% Chronic kidney disease 18,9% 16,9% 20,5% 15% Chronic obstructive pulmonary disease 11,2% 9,9% 11,0% 15% MR: Mitral regurgitation, AS: aortic stenosis, AR: aortic regurgitation, MS: mitral stenosis. Sex Distribution Conclusions The older age and greater number of comorbidities seen in our series over the past ten years, compared to the Euroheart Valve Survey reinforce the idea that the percutaneous valvular therapies should play a major role in the treatment of patients with VHD. Although, the prevalence of VHD may be underestimated in our population, due to the methodology, it reflects an ever-growing pathology in an older and sicker population.

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