Risk Factors Associated With Major Cardiovascular Events 1 Year After Acute Myocardial Infarction

Wang, Yun; Li, Jing; Zheng, Xin; Jiang, Zihan; Hu, Shuang; Wadhera, Rishi K.; Bai, Xueke; Lu, Jiapeng; Wang, Qianying; Li, Yetong; Wu, Chaoqun; Xing, Chao; Normand, Sharon‐Lise T.; Krumholz, Harlan M.; Jiang, Lixin

doi:10.1001/jamanetworkopen.2018.1079

Cited by 50 publications

(51 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although there have been dramatic improvements in public health that have spurred a decline in CVD related deaths over the past several decades, incidence of CVD mortality has remained steady in recent years. As a result, considerable effort has been placed in improving risk prediction of CVD-related events [2,3]. Despite this, clinical practice has remained largely unchanged, relying on traditional scoring metrics such as Thrombolysis in Myocardial Infarction (TIMI) Risk Score [4] for risk stratification.…”

Section: Introductionmentioning

confidence: 99%

Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data

Mandair

Tiwari

Simon

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only ‘known’ risk factors in predicting incident myocardial infarction (MI) from harmonized EHR data. Methods Large-scale case-control study with outcome of 6-month incident MI, conducted using the top 800, from an initial 52 k procedures, diagnoses, and medications within the UCHealth system, harmonized to the Observational Medical Outcomes Partnership common data model, performed on 2.27 million patients. We compared several over- and under- sampling techniques to address the imbalance in the dataset. We compared regularized logistics regression, random forest, boosted gradient machines, and shallow and deep neural networks. A baseline model for comparison was a logistic regression using a limited set of ‘known’ risk factors for MI. Hyper-parameters were identified using 10-fold cross-validation. Results Twenty thousand Five hundred and ninety-one patients were diagnosed with MI compared with 2.25 million who did not. A deep neural network with random undersampling provided superior classification compared with other methods. However, the benefit of the deep neural network was only moderate, showing an F1 Score of 0.092 and AUC of 0.835, compared to a logistic regression model using only ‘known’ risk factors. Calibration for all models was poor despite adequate discrimination, due to overfitting from low frequency of the event of interest. Conclusions Our study suggests that DNN may not offer substantial benefit when trained on harmonized data, compared to traditional methods using established risk factors for MI.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data

Mandair

Tiwari

Simon

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…In another study the mean (SD) age of the cohort was found as 60.8+11.8 years and 994 of 4227 patients (23.5%) were female (Wang et al 2018). 14 In this study the prevalence rate of major risk factors of cardiovascular disease was found as obesity 32.5%, smoking 18.0%, diabetes 6.1% and hypertension 29.0%. Another study conducted in residents of Saronikos municipality (Attica region) in 2014 found the prevalence of major risk factors as obesity 21.6%, current smoking 38.9%, diabetes 11.1%, and hypertension 28.8%.…”

Section: Discussionmentioning

confidence: 55%

Factors Associated with 10 year Risk of Myocardial Infarction and Stroke in Rural Areas of Varanasi, India

Shankar¹,

Singh²

2019

IJCMR

View full text Add to dashboard Cite

Introduction: Cardiovascular diseases (CVD) are a group of diseases that involves mainly Coronary Artery Disease (CAD) also known as Myocardial Infarction (MI) and Stroke. This may be caused by both Physiological and behavioural risk factors. High blood pressure, hyperglycaemia, dyslepidemia and obesity are physiological risk factors, while smoking, lack of exercise, unhealthy dietary practices and excessive alcohol consumption are behavioural risk factors. Study objective was to find out the risk factors associated with occurrence of 10 year total risk of cardiovascular events (MI and Stroke) in rural areas of Varanasi District. Material and methods: This was a community based cross sectional study conducted in Varanasi district of Uttar Pradesh. All population aged 40 years and above of both genders were included in this study. Interview schedule was used to collect the baseline data and an anthropometric measurement and World Health Organization/ International Society of Hypertension (WHO/ISH) risk prediction chart for Indian people was used for risk assessment of MI and Stroke. Results: The prevalence of various risk factors among participants was as obesity, 32.5% (95% CI: 30.4%-34.7%); hypertension, 29.0% (95% CI: 27.0%-31.1%); diabetes 6.1% (95% CI: 5.1%-7.3%) and smoking, 18.3% (95% CI: 16.6%-20.1%). The overall prevalence of 10 year risk of MI and Stroke was found 26%. Out of these 16% (95% CI: 14.0%-17.3%) and 6% (95% CI: 4.9%-7.1%) respondents were in moderate and high risk respectively. While 4% (95% CI: 3.5%-5.3%) respondents had very high risk of MI and Stroke. Age and sex were found significantly associated with the risk of MI and Stroke. Risk factors like hypertension (P<0.05, χ2=516.39), diabetes (P<0.05, χ2=53.74) and smoking (P<0.05, χ2=41.06), were also significantly associated with the development of MI and Stroke. Conclusion: This study shows the positive significant association between several risk factors (i.e. age, gender, hypertension, diabetes and smoking) with 10 year risk for developing MI and Stroke.

show abstract

“…We fit a logistic model with Markov Chain Monte Carlo (MCMC) simulation using the training cohort that containing all candidate risk factors. Subsequently, a posterior probability for each factor was further calculated to assess the association strength between risk factor and the clinical outcome [14,15]. Generally, the factor with a posterior probability > 0.95 (or < 0.05 for factors with estimates < 0.0) was thought to be significant for predicting the prognosis and finally included in the risk factor list [16].…”

Section: Statistical Analyses Risk Factor Selection and Evaluationmentioning

confidence: 99%

“…Further, data from training group were used to evaluate the selected risk factors. Latent class analysis was conducted by an unsupervised machine learning algorithm that does not require an outcome [14]. Generally, if the selected risk factors are significantly associated with the clinical outcome, they will have the ability to assign patients to risk class with unsupervised learning algorithms.…”

Section: Statistical Analyses Risk Factor Selection and Evaluationmentioning

confidence: 99%

A Novel and Simple Risk Model Predicts the Prognosis for Patients with Paraquat Poisoning

Gao

Liu

Li³

et al. 2020

SSRN Journal

View full text Add to dashboard Cite

Background: Acute paraquat (PQ) poisoning is characterized by multi-organ failure and lacking effective therapies. Therefore, identifying risk factors and developing model that could predict early prognosis for patients with PQ poisoning is of great importance. Methods: This was a retrospective cohort study employed with patients suffered from acute PQ poisoning (n=1199). Patients (n=913) with PQ poisoning from 2011 to 2018 were randomly divided into 2 mutually exclusive groups: training (609 patients) and test (304 patients). Another 2 external cohorts containing 207 cases from Zhengzhou 2019 were used as validation from different time and 79 from Shenyang as validation from different site. Risk factors were identified by a logistic model with Markov Chain Monte Carlo (MCMC) simulation and further evaluated by a latent class analysis. The prediction score of this model was developed based on the training sample and was further evaluated using the testing and validation samples. Results: Eight risk factors including age, ingestion volume, CK-MB, platelet (PLT), white blood cell (WBC), neutrophil counts (N), gamma-glutamyl transferase (GGT) and serum creatinine (Cr) were identified as in dependent risk indicators of in-hospital death events. The risk model had a C statistic of 0.895 (95% CI 0.855-0.928), 0.891 (95% CI 0.848-0.932) and 0.829 (95% CI 0.455-1.000) and a predictive range of 4.6%-98.2%, 2.3%-94.9% and 0%-12.5% for the test, validation_time and validation_site group, respectively. In the training group, the risk model classified 18.4%, 59.9% and 21.7% of patients into the high, average and low-risk groups, with corresponding probabilities of 0.985, 0.365, and 0.03 for in-hospital death events. Conclusion: Eight risk factors were identified in this study. And we developed and evaluated a simple risk model to predict the prognosis of patients with acute PQ poisoning. This simple and reliable risk score system could be helpful in recognizing high-risk patients and reducing in-hospital death rate due to PQ poisoning.

show abstract

Risk Factors Associated With Major Cardiovascular Events 1 Year After Acute Myocardial Infarction

Cited by 50 publications

References 38 publications

Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data

Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data

Factors Associated with 10 year Risk of Myocardial Infarction and Stroke in Rural Areas of Varanasi, India

A Novel and Simple Risk Model Predicts the Prognosis for Patients with Paraquat Poisoning

Contact Info

Product

Resources

About