Key result
Decision tree combining Gensini score, age, and CK-MB predicts 2-year MACCEs in ACS with ~0.77 AUC.
Why the study?
The Gensini score assesses coronary plate loading, but its clinical significance has been little explored.
Does the Gensini score combined with machine learning models predict the occurrence of MACCEs in patients with acute coronary syndrome?
Cohort (n=361)
No
Does the Gensini score combined with machine learning models predict the occurrence of MACCEs in patients with acute coronary syndrome?
Answers are grounded in this paper and cite their sources.
The Gensini score, when combined with age and CK-MB in a decision tree model, provides a useful prognostic tool for predicting MACCEs in patients with acute coronary syndrome.
Background The Gensini score (GS) provides a good assessment of the degree of coronary plate loading. However, its clinical significance has been little explored. Methods In this retrospective cohort study, we implemented model development and performance comparison on database of The Fourth Affiliated Hospital of Zhejiang University School of Medicine (2019.1-2020.12). The patients were followed up for 2 years. Follow-up endpoint was the occurrence of MACCEs. We extracted clinical baseline data from each ACS patient within 24 hours of hospital admission and randomly divided the datasets into 70% for model training and 30% for model validation. Area under the curve (AUC) was used to compare the prediction performance of XGBoost, SGD and KNN. A decision tree model was constructed to predict the probability of MACCEs using a combination of weight features picked by XGBoost and clinical significance. Results A total of 361 ACS patients who met the study criteria were included in this study. It could be observed that the probability of a recurrent MACCEs within 2 years was 25.2%. XGboost had the best predictive efficacy (AUC:0.97). GS has high clinical significance. Then we used GS, Age and CK-MB to construct a decision tree model to predict the probability model of MACCEs reoccurring, and the final AUC value reached 0.771. Conclusions GS is a powerful indicator for assessing the prognosis of patients with ACS. The cut-off value of GS in the decision tree model provides a reference standard for grading the risk level of patients with ACS.
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Chen et al. (2023) conducted a cohort in Acute Coronary Syndrome (n=361). Gensini Score was evaluated on Occurrence of Major Adverse Cardiac and Cerebrovascular Events (MACCEs) within 2 years (AUC 0.771). A decision tree model combining the Gensini score, age, and CK-MB effectively predicted the 2-year occurrence of major adverse cardiac and cerebrovascular events in ACS patients with an AUC of 0.771.
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