Aim Based on coronary CT-fractional flow reserve(CT-FFR)combined with machine learning methods,a nomogram prediction model for coronary in-stent restenosis(ISR)was developed to assess the risk of ISR.Methods Retrospective analysis was performed on patients who underwent re-examination after PCI at our hospital from January 2022 to January 2025.According to the exclusion criteria,a total of 210 patients were enrolled,including 100 cases of ISR and 110 cases of non-ISR.The dataset was randomly divided into training and test sets at a 7∶3 ratio.Af-ter univariate analysis to screen potential predictors,LASSO regression was applied to identify feature variables with non-ze-ro coefficients.Subsequently,three machine learning(ML)algorithms including random forest(RF),support vector machine(SVM),and extreme gradient boosting(XGB)were used to rank the importance of the significant factors.The intersection of the top 10 variables from each algorithm was used as input for bidirectional stepwise multivariate Logistic re-gression.An ISR risk score was then constructed and visualized using a nomogram.Results A total of 14 predictive factors were identified through LASSO regression,including diastolic blood pressure,C-reactive protein,triglycerides(TG),N-terminal pro-brain natriuretic peptide(NT-proBNP),low density lipoprotein cholesterol(LDLC),minimum stent diameter＜3 mm,systolic blood pressure,△CT-FFR,CT-FFR,interleukin-6(IL-6),body mass index,glycosylated hemoglobin(HbA1c),history of hypertension,and high density lipoprotein cholesterol(HDLC).Following stepwise screening using three ML algorithms and Logistic regression,six independent risk factors for ISR were identified:elevated△CT-FFR,IL-6,NT-proBNP,TG and CT-FFR values,and minimum stent diameter＜3 mm.The area under the curve for the training set and test set were 0.995(95％CI:0.989～1.000)and 0.965(95％CI:0.927～1.000),respectively.Decision curve analysis demonstrated high net benefit across threshold probabilities of 0～1.00 in the training set and 0～0.92 in the test set.The nomogram integrating these six predictors exhibited high accuracy and clinical utility.Con-clusion The ISR nomogram prediction model based on LASSO-ML combined with CT-FFR technology has high accuracy and clinical utility for ISR.

Aim Based on coronary CT-fractional flow reserve(CT-FFR)combined with machine learning methods,a nomogram prediction model for coronary in-stent restenosis(ISR)was developed to assess the risk of ISR.Methods Retrospective analysis was performed on patients who underwent re-examination after PCI at our hospital from January 2022 to January 2025.According to the exclusion criteria,a total of 210 patients were enrolled,including 100 cases of ISR and 110 cases of non-ISR.The dataset was randomly divided into training and test sets at a 7∶3 ratio.Af-ter univariate analysis to screen potential predictors,LASSO regression was applied to identify feature variables with non-ze-ro coefficients.Subsequently,three machine learning(ML)algorithms including random forest(RF),support vector machine(SVM),and extreme gradient boosting(XGB)were used to rank the importance of the significant factors.The intersection of the top 10 variables from each algorithm was used as input for bidirectional stepwise multivariate Logistic re-gression.An ISR risk score was then constructed and visualized using a nomogram.Results A total of 14 predictive factors were identified through LASSO regression,including diastolic blood pressure,C-reactive protein,triglycerides(TG),N-terminal pro-brain natriuretic peptide(NT-proBNP),low density lipoprotein cholesterol(LDLC),minimum stent diameter＜3 mm,systolic blood pressure,△CT-FFR,CT-FFR,interleukin-6(IL-6),body mass index,glycosylated hemoglobin(HbA1c),history of hypertension,and high density lipoprotein cholesterol(HDLC).Following stepwise screening using three ML algorithms and Logistic regression,six independent risk factors for ISR were identified:elevated△CT-FFR,IL-6,NT-proBNP,TG and CT-FFR values,and minimum stent diameter＜3 mm.The area under the curve for the training set and test set were 0.995(95％CI:0.989～1.000)and 0.965(95％CI:0.927～1.000),respectively.Decision curve analysis demonstrated high net benefit across threshold probabilities of 0～1.00 in the training set and 0～0.92 in the test set.The nomogram integrating these six predictors exhibited high accuracy and clinical utility.Con-clusion The ISR nomogram prediction model based on LASSO-ML combined with CT-FFR technology has high accuracy and clinical utility for ISR.