Management, hospital outcome and revascularization trends in non-ST elevation myocardial infarction with high GRACE risk score

Akhter, Zohaib; Rind, Irfan Ali; Aijaz, Saba; Sattar, Saadia; Malik, Rehan; Pathan, Asad

doi:10.5455/jpma.22228

Cited by 2 publications

(4 citation statements)

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“…Current guidelines recommend the GRACE risk score to perform risk stratification in CAD, especially for patients with acute coronary syndrome [ 12 ]. Even though the GRACE score is easy to apply, the score in isolation was associated with significant over and undertreatment, suggesting the need for more accurate assessments using a wider range of clinical variables [ 3 , 4 ]. However, integrating a patient's various clinical information for risk scoring is a challenge for cardiovascular physicians.…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, for some patients with serious chest pain, the coronary arteries might become more severely blocked and require a revascularization procedure as soon as possible. Nevertheless, there are data suggesting that patients at low risk of developing ischemic complications are treated overly aggressively, generating the so-called “treatment-risk paradox” [ 3 – 5 ]. For this reason, there is a need for prediction models in addition to the GRACE risk score [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

Zheng

Guo

Wang

et al. 2022

Journal of Healthcare Engineering

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Objective. The study aimed to use machine learning algorithms to predict the need for revascularization in patients presenting with chest pain in the emergency department. Methods. We obtained data from 581 patients with chest pain, 264 who underwent revascularization, and the other 317 were treated with medication alone for 3 months. Using standard algorithms, linear discriminant analysis, and standard algorithms, we analyzed 41 features relevant to coronary artery disease (CAD). Results. We identified seven robust predictive features. The combination of these predictors gave an area under the curve (AUC) of 0.830 to predict the need for revascularization. By contrast, the GRACE score gave an AUC of 0.68. Conclusions. This machine learning-based approach predicts the need for revascularization in patients with chest pain.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

Zheng

Guo

Wang

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Current guidelines recommend the GRACE risk score to perform risk strati cation in CAD, especially for patients with acute coronary syndrome [14] . Even though the GRACE score is easy to implement, the score in isolation was associated with signi cant over-and under-treatment, suggesting the need for more accurate assessments using a wider range of clinical variables [3,4] .…”

Section: Discussionmentioning

confidence: 99%

“…Current guidelines call for aggressive management, including revascularization in at-risk patiens [2] . Nevertheless, there are data suggesting that patients at low risk of developing ischemic complications are treated overly aggressively, generating the so-called 'treatment-risk" paradox' [3][4][5] . For this reason, there is a need for prediction models in addition to the GRACE risk score [6] .…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

Zheng

Wang

Jiang

et al. 2021

Preprint

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Background: The study aimed to use machine learning algorithms to predict the need for revascularization in patients presenting with chest pain to the emergency department.Methods: We obtained data from 581 patients with chest pain, 264 who underwent revascularization, and the other 317 were treated with medication alone at 3 months. Using standard algorithms, linear discriminant analysis, and standard algorithms, we analyzed 41 features relevant to coronary artery disease (CAD). Results: We identified seven robust predictive features. The combination of these predictors gave an area under the curve (AUC) of 0.830 to predict the need for revascularization. By contrast, the GRACE score gave an AUC of 0.68.Conclusions: This machine learning-based approach predicts the need for revascularization in patients with CAD.

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Management, hospital outcome and revascularization trends in non-ST elevation myocardial infarction with high GRACE risk score

Cited by 2 publications

References 0 publications

Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

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