Deep Phenotyping and Prediction of Long-term Cardiovascular Disease: Optimized by Machine Learning

Zhuang, Xiaodong; Tian, Ting; Liao, Lingjie; Dong, Yuehua; Hao-jin, Zhou; Zhang, Shaozhao; Chen, Wenyi; Du, Zhi-Min; Wang, Xueqin; Liao, Xinxue

doi:10.1016/j.cjca.2022.02.008

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…Therefore, it is widely used in constructing prognostic models for heart failure, arrhythmia, multiple myeloma, and other diseases [ 43 , 46 , 47 ]. In this study, the RSF model demonstrated a better predictive performance than the traditional Cox regression model and Lasso–Cox model for both the men and women in this population, which is a finding that is similar to the findings of the study by Zhang et al [ 37 – 39 ]. This may be related to the characteristics of the RSF model, which has good processing of complex and high-dimensional data.…”

Section: Discussionsupporting

confidence: 90%

Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis

et al. 2023

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Purpose With the increase in aging and cardiovascular risk factors, the morbidity and mortality of atherosclerotic cardiovascular disease (ASCVD), represented by ischemic heart disease and stroke, continue to rise in China. For better prevention and intervention, relevant guidelines recommend using predictive models for early detection of ASCVD high-risk groups. Therefore, this study aims to establish a population ASCVD prediction model in rural areas of Xinjiang using survival analysis. Methods Baseline cohort data were collected from September to December 2016 and followed up till June 2022. A total of 7975 residents (4054 males and 3920 females) aged 30–74 years were included in the analysis. The data set was divided according to different genders, and the training and test sets ratio was 7:3 for different genders. A Cox regression, Lasso-Cox regression, and random survival forest (RSF) model were established in the training set. The model parameters were determined by cross-validation and parameter tuning and then verified in the training set. Traditional ASCVD prediction models (Framingham and China-PAR models) were constructed in the test set. Different models' discrimination and calibration degrees were compared to find the optimal prediction model for this population according to different genders and further analyze the risk factors of ASCVD. Results After 5.79 years of follow-up, 873 ASCVD events with a cumulative incidence of 10.19% were found (7.57% in men and 14.44% in women). By comparing the discrimination and calibration degrees of each model, the RSF showed the best prediction performance in males and females (male: Area Under Curve (AUC) 0.791 (95%CI 0.767,0.813), C statistic 0.780 (95%CI 0.730,0.829), Brier Score (BS):0.060, female: AUC 0.759 (95%CI 0.734,0.783) C statistic was 0.737 (95%CI 0.702,0.771), BS:0.110). Age, systolic blood pressure (SBP), apolipoprotein B (APOB), Visceral Adiposity Index (VAI), hip circumference (HC), and plasma arteriosclerosis index (AIP) are important predictors of ASCVD in the rural population of Xinjiang. Conclusion The performance of the ASCVD prediction model based on the RSF algorithm is better than that based on Cox regression, Lasso-Cox, and the traditional ASCVD prediction model in the rural population of Xinjiang.

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Section: Discussionsupporting

confidence: 90%

Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis

et al. 2023

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“…In addition, the number and types of potential factors included for modeling also varied. For example, while some studies involved hundreds of clinical factors [ 43 , 44 ], the others used a dozen or so demographic and lifestyle factors [ 45 , 46 ]. Despite this, key factors identified in previous studies, including age [ 43 , 47 , 48 ], hypertension [ 43 , 47 , 48 ], exercise frequency [ 43 , 47 ], and dietary pattern [ 43 , 47 ] were confirmed by our current study.…”

Section: Discussionmentioning

confidence: 99%

Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS)

Zhong

et al. 2022

glob health res policy

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Background Identifying factors associated with cardiovascular disease (CVD) is critical for its prevention, but this topic is scarcely investigated in Kashgar prefecture, Xinjiang, northwestern China. We thus explored the CVD epidemiology and identified prominent factors associated with CVD in this region. Methods A total of 1,887,710 adults at baseline (in 2017) of the Kashgar Prospective Cohort Study were included in the analysis. Sixteen candidate factors, including seven demographic factors, 4 lifestyle factors, and 5 clinical factors, were collected from a questionnaire and health examination records. CVD was defined according to International Clinical Diagnosis (ICD-10) codes. We first used logistic regression models to investigate the association between each of the candidate factors and CVD. Then, we employed 3 machine learning methods—Random Forest, Random Ferns, and Extreme Gradient Boosting—to rank and identify prominent factors associated with CVD. Stratification analyses by sex, ethnicity, education level, economic status, and residential setting were also performed to test the consistency of the ranking. Results The prevalence of CVD in Kashgar prefecture was 8.1%. All the 16 candidate factors were confirmed to be significantly associated with CVD (odds ratios ranged from 1.03 to 2.99, all p values < 0.05) in logistic regression models. Further machine learning-based analysis suggested that age, occupation, hypertension, exercise frequency, and dietary pattern were the five most prominent factors associated with CVD. The ranking of relative importance for prominent factors in stratification analyses showed that the factor importance generally followed the same pattern as that in the overall sample. Conclusions CVD is a major public health concern in Kashgar prefecture. Age, occupation, hypertension, exercise frequency, and dietary pattern might be the prominent factors associated with CVD in this region.In the future, these factors should be given priority in preventing CVD in future.

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“…Based on the traditional statistically derived risk prediction models, the correlation among variables, heterogeneity, nonlinearity, and overfitting also restricted the application, especially in multifaceted data sets with large numbers of features. 4 Machine learning (ML) methods can overcome the shortcomings of current prediction risk models. As an important branch of artificial intelligence, the advantage of ML is using computer algorithms to identify characteristics in large data sets with numerous, multidimensional, and nonlinear relationships among clinical features to predict various outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, most risk prediction algorithms were initially developed for unique ethnicities and may not be suitable for other populations. Based on the traditional statistically derived risk prediction models, the correlation among variables, heterogeneity, nonlinearity, and overfitting also restricted the application, especially in multifaceted data sets with large numbers of features 4 …”

Section: Introductionmentioning

confidence: 99%

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Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study

Song,

Li,

Nie

et al. 2023

Clinical Cardiology

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BackgroundUp to 30% of patients with acute coronary syndrome (ACS) die from adverse events, mainly renal failure and myocardial infarction (MI). Accurate prediction of adverse events is therefore essential to improve patient prognosis.HypothesisMachine learning (ML) methods can accurately identify risk factors and predict adverse events.MethodsA total of 5240 patients diagnosed with ACS who underwent PCI were enrolled and followed for 1 year. Support vector machine, extreme gradient boosting, adaptive boosting, K‐nearest neighbors, random forest, decision tree, categorical boosting, and linear discriminant analysis (LDA) were developed with 10‐fold cross‐validation to predict acute kidney injury (AKI), MI during hospitalization, and all‐cause mortality within 1 year. Features with mean Shapley Additive exPlanations score >0.1 were screened by XGBoost method as input for model construction. Accuracy, F1 score, area under curve (AUC), and precision/recall curve were used to evaluate the performance of the models.ResultsOverall, 2.6% of patients died within 1 year, 4.2% had AKI, and 4.7% had MI during hospitalization. The LDA model was superior to the other seven ML models, with an AUC of 0.83, F1 score of 0.90, accuracy of 0.85, recall of 0.85, specificity of 0.68, and precision of 0.99 in predicting all‐cause mortality. For AKI and MI, the LDA model also showed good discriminating capacity with an AUC of 0.74.ConclusionThe LDA model, using easily accessible variables from in‐hospital patients, showed the potential to effectively predict the risk of adverse events and mortality within 1 year in ACS patients after PCI.

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Deep Phenotyping and Prediction of Long-term Cardiovascular Disease: Optimized by Machine Learning

Cited by 10 publications

References 26 publications

Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis

Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis

Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS)

Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study

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