The Value of the Gensini Score For Prognostic Assessment in Patients with Acute Coronary Syndrome--A Retrospective Cohort Study Based on Machine Learning Methods

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. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial The study was retrospective and did not meet the criteria for a clinical trial.The related ethics have been approved by the Ethics Committee of the Fourth Affiliated Hospital of Zhejiang University. ### Funding Statement This study was supported by National Natural Science Foundation of China (No.81971688) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Ethics Committee of the Fourth Affiliated Hospital of Zhejiang University School of Medicine approved the study.(No.TK2023158) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data that support the findings of this study are available on request from the corresponding author upon reasonable request. * ACS : Acute Coronary Syndrome ALT : Alanine Transaminase APTT : Activated Partial Thromboplastin Time AUC : Area Under the Curve CK : Creatine Kinase CK-MB : Creatine Kinase-Myocardial Band CRP : C-Reactive Protein D-D : D-dimer DT : Decesion Tree GS : Gensini Score K : Kalium KNN : K-Nearest Neighbor LDH : Lactic Dehydrogenase; MACCEs : Major Adverse Cardiac and Cerebrovascular Events ML : Machine Learning NPV : Negative Predictive Value NLR : Neutrophil-to-Lymphocyte Ratio PCI : Percutaneous Coronary Intervention PLR : Platelet-to-Lymphocyte Ratio PPV : Positive Predictive Value PT : Prothrombin time ROC : Receiver Operating Characteristic Curve SGD : Stochastic Gradient Descent SII : Systemic Immune-Inflammation index TT : Thrombin Time