基于决策树算法构建急性心肌梗死患者PCI术后血运重建的风险预测方案OA

Objective To construct a risk prediction model for revascularization in patients with acute myocardial infarction(AMI)after percutaneous coronary intervention(PCI)using decision tree algorithm.Methods The clinical data of 203 patients with AMI who underwent PCI in the Second Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine from January 2021 to January 2023 were retrospectively analyzed,and they were divided into a revascularization group(60 cases)and a non-revascularization group(143 cases)accordingto whether they underwent revascularization within 1 year after operation.In the revascularization group,there were 41 males and 19 females,aged(62.75±10.32)years.In the non-revascularization group,there were 94 males and 49 females,aged(61.47±10.07)years.Multivariate logistic regression analysis was used to investigate the influencing factors of revascularization in AMI patients after PCI.According to the ratio of 7:3,203 patients were randomly divided into a training set(142 cases)and a test set(61 cases).The decision tree model was constructed based on the training set data,and the prediction efficiency of the decision tree model was verified based on the test set data.x2 test and t test were used for statistical analysis.Results In the revascularization group,the proportions of diabetes mellitus,low density lipoprotein cholesterol(LDL-C)≥3.4 mmol/L,uric acid＞420 μmol/L,hypersensitive C-reactive protein(hs-CRP)＞10 mg/L,number of lesions ≥2,and number of stents ≥3 before PCI,and residual SYNTAX score(rSS)＞5 points after PCI were higher than those in the non-revascularization group[23.33％(14/60)vs.11.19％(16/143),36.67％(22/60)vs.20.98％(30/143),38.33％(23/60)vs.20.98％(30/143),33.33％(20/60)vs.17.48％(25/143),70.00％(42/60)vs.53.85％(77/143),61.67％(37/60)vs.45.45％(65/143),38.33％(23/60)vs.21.68％(31/143)],with statistically significant differences(all P＜0.05).Multivariate logistic regression analysis showed that diabetes mellitus,LDL-C ≥3.4 mmol/L,uric acid＞420 pmol/L,hs-CRP＞10 mg/L,and number of lesions ≥2 before PCI,and rSS＞5 points after PCI were all risk factors for revascularization in AMI patients after PCI(all P＜0.05).Based on the training set data,a decision tree risk prediction model for revascularization in AMI patients after PCI was established.The model contained 6 explanatory variables,namely diabetes mellitus,LDL-C level,uric acid level,hs-CRP level,and number of lesions before PCI,and rSS after PCI.A total of 7 classification rules were extracted,among which the uric acid level before PCI was the primary influencing factor of the model.The decision tree model was verified based on the test set data,and the sensitivity,specificity,and accuracy of the decision tree model for predicting revascularization in AMI patients after PCI were 88.89％,83.72％,and 85.25％,respectively.Conclusion The decision tree risk model of revascularization in AMI patients after PCI includes 6 variables,namely diabetes mellitus,LDL-C level,uric acid level,hs-CRP level,and number of lesions before PCI,and rSS after PCI,among which the uric acid level before PCI is the primary influencing factor of the model.