Machine Learning Predicts Cardiovascular Events in Patients With Diabetes: The Silesia Diabetes-Heart Project

Nabrdalik, Katarzyna; Kwiendacz, Hanna; Drożdż, Karolina; Irlik, Krzysztof; Hendel, Mirela; Wijata, Agata M.; Nalepa, Jakub; Correa, Elon; Hajzler, Weronika; Janota, Oliwia; Wójcik, Wiktoria; Gumprecht, Janusz; Lip, Gregory Y. H.

doi:10.1016/j.cpcardiol.2023.101694

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…This study stands out for its utilization of a machine learning algorithm to investigate intricate relationships without assuming linearity between micronutrient intake and CVD risk. While previous research has made valuable contributions to identifying risk factors for CVD or other metabolic diseases using machine learning algorithms, [36][37][38][39] our study uniquely focuses on uncovering the connection between micronutrient intake and CVD risk. In the dynamic field of CVD research, studies have predominantly concentrated on pinpointing risk factors and developing high-performance prediction models through advanced computational techniques.…”

Section: Discussionmentioning

confidence: 99%

Dietary intake of potassium, vitamin E, and vitamin C emerges as the most significant predictors of cardiovascular disease risk in adults

Wang,

Han,

Ling

et al. 2024

Medicine

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Prediction models were developed to assess the risk of cardiovascular disease (CVD) based on micronutrient intake, utilizing data from 90,167 UK Biobank participants. Four machine learning models were employed to predict CVD risk, with performance evaluation metrics including area under the receiver operating characteristic curve (AUC), accuracy, recall, specificity, and F1-score. The eXtreme Gradient Boosting (XGBoost) model was utilized to rank the importance of 11 micronutrients in cardiovascular health. Results indicated that vitamin E, calcium, vitamin C, and potassium intake were associated with a reduced risk of CVD. The XGBoost model demonstrated the highest performance with an AUC of 0.952, highlighting potassium, vitamin E, and vitamin C as key predictors of CVD risk. Subgroup analysis revealed a stronger correlation between calcium intake and CVD risk in older adults and those with higher BMI, while vitamin B6 intake showed a link to CVD risk in women. Overall, the XGBoost model emphasized the significance of potassium, vitamin E, and vitamin C intake as primary predictors of CVD risk in adults, with age, sex, and BMI potentially influencing the importance of micronutrient intake in predicting CVD risk.

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Section: Discussionmentioning

confidence: 99%

Dietary intake of potassium, vitamin E, and vitamin C emerges as the most significant predictors of cardiovascular disease risk in adults

Wang,

Han,

Ling

et al. 2024

Medicine

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“…Further studies involving the use of artificial intelligence (AI)-based algorithms and end-to-end classification of CCM images [7] may strengthen the utility of CCM. Recently, machine learning algorithm was used to develop systems based on several discriminative patient parameters, helping identify patients at high risk of cardiovascular events [33].…”

Section: Discussionmentioning

confidence: 99%

Corneal Confocal Microscopy Predicts Cardiovascular and Cerebrovascular Events and Demonstrates Greater Peripheral Neuropathy in Patients with Type 1 Diabetes and Foot Ulcers

Lim,

Burgess,

Ooi

et al. 2023

Diagnostics

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Objective: In this study, we evaluate small and large nerve fibre pathology in relation to diabetic foot ulceration (DFU) and incident cardiovascular and cerebrovascular events in type 1 diabetes (T1D). Methods: A prospective observational study was conducted on people with T1D without diabetic peripheral neuropathy (DPN) (n = 25), T1D with DPN (n = 28), T1D with DFU (n = 25) and 32 healthy volunteers. ROC analysis of parameters was conducted to diagnose DPN and DFU, and multivariate Cox regression analysis was performed to evaluate the predictive ability of corneal nerves for cardiac and cerebrovascular events over 3 years. Results: Corneal nerve fibre length (CNFL), fibre density (CNFD) and branch density (CNBD) were lower in T1D-DPN and T1D-DFU vs. T1D (all p < 0.001). In ROC analysis, CNFD (sensitivity 88%, specificity 87%; AUC 0.93; p < 0.001; optimal cut-off 7.35 no/mm2) and CNFL (sensitivity 76%, specificity 77%; AUC 0.90; p < 0.001; optimal cut-off 7.01 mm/mm2) had good ability to differentiate T1D with and without DFU. Incident cardiovascular events (p < 0.001) and cerebrovascular events (p < 0.001) were significantly higher in T1D-DPN and T1D-DFU. Corneal nerve loss, specifically CNFD predicted incident cardiovascular (HR 1.67, 95% CI 1.12 to 2.50, p = 0.01) and cerebrovascular (HR 1.55, 95% CI 1.06 to 2.26, p = 0.02) events. Conclusions: Our study provides threshold values for corneal nerve fibre metrics for neuropathic foot at risk of DFU and further demonstrates that lower CNFD predicts incident cardiovascular and cerebrovascular events in T1D.

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“…Notably, all the ML models tested outperformed the risk calculators built by traditional statistic methods. In the Silesia Diabetes‐Heart Project, an end‐to‐end ML technique was proposed to develop a model with an area under the receiver operating characteristic curve (AUC) of 0.72 to predict cardiovascular events in diabetes mellitus patients 18 . These studies showed the powerful ability of ML in the ASCVD risk prediction.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in Chinese type 2 diabetes mellitus patients

Ding,

Luo,

Shi

et al. 2023

J of Diabetes Invest

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Aims/IntroductionClinical guidelines for the management of individuals with type 2 diabetes mellitus endorse the systematic assessment of atherosclerotic cardiovascular disease risk for early interventions. In this study, we aimed to develop machine learning models to predict 3‐year atherosclerotic cardiovascular disease risk in Chinese type 2 diabetes mellitus patients.Materials and MethodsClinical records of 4,722 individuals with type 2 diabetes mellitus admitted to 94 hospitals were used. The features included demographic information, disease histories, laboratory tests and physical examinations. Logistic regression, support vector machine, gradient boosting decision tree, random forest and adaptive boosting were applied for model construction. The performance of these models was evaluated using the area under the receiver operating characteristic curve. Additionally, we applied SHapley Additive exPlanation values to explain the prediction model.ResultsAll five models achieved good performance in both internal and external test sets (area under the receiver operating characteristic curve >0.8). Random forest showed the highest discrimination ability, with sensitivity and specificity being 0.838 and 0.814, respectively. The SHapley Additive exPlanation analyses showed that previous history of diabetic peripheral vascular disease, older populations and longer diabetes duration were the three most influential predictors.ConclusionsThe prediction models offer opportunities to personalize treatment and maximize the benefits of these medical interventions.

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Machine Learning Predicts Cardiovascular Events in Patients With Diabetes: The Silesia Diabetes-Heart Project

Cited by 9 publications

References 26 publications

Dietary intake of potassium, vitamin E, and vitamin C emerges as the most significant predictors of cardiovascular disease risk in adults

Dietary intake of potassium, vitamin E, and vitamin C emerges as the most significant predictors of cardiovascular disease risk in adults

Corneal Confocal Microscopy Predicts Cardiovascular and Cerebrovascular Events and Demonstrates Greater Peripheral Neuropathy in Patients with Type 1 Diabetes and Foot Ulcers

Machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in Chinese type 2 diabetes mellitus patients

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