Isolated Cardiac Sarcoidosis in a Patient with a Rare Coronary Anomaly Presenting with MINOCA and Heart Block: The Devil Is in the Detail

Sharma, Gautam; Ahmed, A Shaheer; Talupula, Ram Manohar

doi:10.5543/tkda.2022.21274

Turk Kardiyoloji Dernegi Arsivi-Archives of the Turkish Society 2022

DOI: 10.5543/tkda.2022.21274

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Isolated Cardiac Sarcoidosis in a Patient with a Rare Coronary Anomaly Presenting with MINOCA and Heart Block: The Devil Is in the Detail

Gautam Sharma

A Shaheer Ahmed

Ram Manohar Talupula

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2024

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Use machine learning models to identify and assess risk factors for coronary artery disease

Zhang,

Wang,

Zhao

2024

PLoS ONE

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Accurate prediction of coronary artery disease (CAD) is crucial for enabling early clinical diagnosis and tailoring personalized treatment options. This study attempts to construct a machine learning (ML) model for predicting CAD risk and further elucidate the complex nonlinear interactions between the disease and its risk factors. Employing the Z-Alizadeh Sani dataset, which includes records of 303 patients, univariate analysis and the Boruta algorithm were applied for feature selection, and nine different ML techniques were subsequently deployed to produce predictive models. To elucidate the intricate pathogenesis of CAD, this study harnessed the analytical capabilities of Shapley values, alongside the use of generalized additive models for curve fitting, to probe into the nonlinear interactions between the disease and its associated risk factors. Furthermore, we implemented a piecewise linear regression model to precisely pinpoint inflection points within these complex nonlinear dynamics. The findings of this investigation reveal that logistic regression (LR) stands out as the preeminent predictive model, demonstrating remarkable efficacy, it achieved an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.981 (95% CI: 0.952–1), and an Area Under the Precision-Recall Curve (AUPRC) of 0.993. The utilization of the 14 most pivotal features in constructing a dynamic nomogram. Analysis of the Shapley smoothing curves uncovered distinctive “S”-shaped and “C”-shaped relationships linking age and triglycerides to CAD, respectively. In summary, machine learning models could provide valuable insights for the early diagnosis of CAD. The SHAP method may provide a personalized risk assessment of the relationship between CAD and its risk factors.

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Use machine learning models to identify and assess risk factors for coronary artery disease

Zhang,

Wang,

Zhao

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

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Isolated Cardiac Sarcoidosis in a Patient with a Rare Coronary Anomaly Presenting with MINOCA and Heart Block: The Devil Is in the Detail

Cited by 1 publication

References 0 publications

Use machine learning models to identify and assess risk factors for coronary artery disease

Use machine learning models to identify and assess risk factors for coronary artery disease

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