Occurrence and predictive factors of restenosis in coronary heart disease patients underwent sirolimus-eluting stent implantation

In-stent restenosis in acute coronary syndrome-a classic and a machine learning approach

Background

In acute coronary syndrome (ACS), a number of previous studies tried to identify the risk factors that are most likely to influence the rate of in-stent restenosis (ISR), but the contribution of these factors to ISR is not clearly defined. Thus, the need for a better way of identifying the independent predictors of ISR, which comes in the form of Machine Learning (ML).

Objectives

The aim of this study is to evaluate the relationship between ISR and risk factors associated with ACS and to develop and validate a nomogram to predict the probability of ISR through the use of ML in patients undergoing percutaneous coronary intervention (PCI).

Methods

Consecutive patients presenting with ACS who were successfully treated with PCI and who had an angiographic follow-up after at least 3 months were included in the study. ISR risk factors considered into the study were demographic, clinical and peri-procedural angiographic lesion risk factors. We explored four ML techniques (Random Forest (RF), support vector machines (SVM), simple linear logistic regression (LLR) and deep neural network (DNN)) to predict the risk of ISR. Overall, 21 features were selected as input variables for the ML algorithms, including continuous, categorical and binary variables.

Results

The total cohort of subjects included 340 subjects, in which the incidence of ISR observed was 17.68% (n = 87). The most performant model in terms of ISR prediction out of the four explored was RF, with an area under the receiver operating characteristic (ROC) curve of 0.726. Across the predictors herein considered, only three predictors were statistically significant, precisely, the number of affected arteries (≥2), stent generation and diameter.

Conclusion

ML models applied in patients after PCI can contribute to a better differentiation of the future risk of ISR.

Fasting blood glucose predicts high risk of in-stent restenosis in patients undergoing primary percutaneous coronary intervention: a cohort study

Objectives. Studies have shown that fasting blood glucose (FBG) is closely associated with poor prognosis in patients with coronary heart disease (CHD) after percutaneous coronary intervention (PCI), but its association with in-stent restenosis (ISR) is still unclear. Therefore, this study was to explore the association between FBG with ISR in patients with CHD after PCI. Design. In this cohort study, we included 531 patients with CHD who underwent PCI. Logistic regression, receiver operating characteristic (ROC), subgroup analysis and restricted cubic spline (RCS) were used to assess the association between FBG with ISR. Results. A total of 124 (23.4%) patients had ISR. Patients with higher levels of FBG had higher incidence of ISR compared to those with lower levels of FBG (p = 0.002). In multivariable logistic regression analyses, higher levels of FBG remained strongly associated with higher risk of ISR (as a categorical variable, OR: 1.89, 95% CI: 1.21-2.94, p = 0.005; as a continuous variable, OR: 1.12, 95% CI: 1.03-1.23, p = 0.011). ROC analysis also showed that FBG might be associated with the occurrence of ISR (AUC = 0.577, 95% CI: 0.52-0.64, p = 0.013). Subgroup analyses showed the association of FBG with ISR was also stable in several subgroups (< 60 years or ≥ 60 years, male, with or without smoking, without diabetes and without hypertension). And RCS analysis showed that FBG was linearly and positively associated with the risk of ISR. Conclusions. Higher levels of FBG were closely associated with higher risk of ISR in patients with CHD after PCI.

Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease

Objective

To analyze the risk factors of in-stent restenosis (ISR) after the first implantation of drug-eluting stent (DES) patients with coronary heart disease (CHD) and to establish a nomogram model to predict the risk of ISR.

Methods

This study retrospectively analyzed the clinical data of patients with CHD who underwent DES treatment for the first time at the Fourth Affiliated Hospital of Zhejiang University School of Medicine from January 2016 to June 2020. Patients were divided into an ISR group and a non-ISR (N-ISR) group according to the results of coronary angiography. The least absolute shrinkage and selection operator (LASSO) regression analysis was performed on the clinical variables to screen out the characteristic variables. Then we constructed the nomogram prediction model using conditional multivariate logistic regression analysis combined with the clinical variables selected in the LASSO regression analysis. Finally, the decision curve analysis, clinical impact curve, area under the receiver operating characteristic curve, and calibration curve were used to evaluate the nomogram prediction model's clinical applicability, validity, discrimination, and consistency. And we double-validate the prediction model using ten-fold cross-validation and bootstrap validation.

Results

In this study, hypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen were all predictive factors for ISR. We successfully constructed a nomogram prediction model using these variables to quantify the risk of ISR. The AUC value of the nomogram prediction model was 0.806 (95%CI: 0.739–0.873), indicating that the model had a good discriminative ability for ISR. The high quality of the calibration curve of the model demonstrated the strong consistency of the model. Moreover, the DCA and CIC curve showed the model's high clinical applicability and effectiveness.

Conclusions

Hypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen are important predictors for ISR. The nomogram prediction model can better identify the high-risk population of ISR and provide practical decision-making information for the follow-up intervention in the high-risk population.

Predicting and Analyzing Restenosis Risk after Endovascular Treatment in Lower Extremity Arterial Disease: Development and Assessment of a Predictive Nomogram

PURPOSE

This study aimed to develop and internally validate nomograms for predicting restenosis after endovascular treatment of lower extremity arterial diseases.

MATERIALS AND METHODS

A total of 181 hospitalized patients with lower extremity arterial disease diagnosed for the first time between 2018 and 2019 were retrospectively collected. Patients were randomly divided into a primary cohort (n=127) and a validation cohort (n=54) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used to optimize the feature selection of the prediction model. Combined with the best characteristics of LASSO regression, the prediction model was established by multivariate Cox regression analysis. The predictive models' identification, calibration, and clinical practicability were evaluated by the C index, calibration curve, and decision curve. The prognosis of patients with different grades was compared by survival analysis. Internal validation of the model used data from the validation cohort.

RESULTS

The predictive factors included in the nomogram were lesion site, use of antiplatelet drugs, application of drug coating technology, calibration, coronary heart disease, and international normalized ratio (INR). The prediction model demonstrated good calibration ability, and the C index was 0.762 (95% confidence interval: 0.691-0.823). The C index of the validation cohort was 0.864 (95% confidence interval: 0.801-0.927), which also showed good calibration ability. The decision curve shows that when the threshold probability of the prediction model is more significant than 2.5%, the patients benefit significantly from our prediction model, and the maximum net benefit rate is 30.9%. Patients were graded according to the nomogram. Survival analysis found that there was a significant difference in the postoperative primary patency rate between patients of different classifications (log-rank p<0.001) in both the primary cohort and the validation cohort.

CONCLUSION

We developed a nomogram to predict the risk of target vessel restenosis after endovascular treatment by considering information on lesion site, postoperative antiplatelet drugs, calcification, coronary heart disease, drug coating technology, and INR.

CLINICAL IMPACT

Clinicians can grade patients after endovascular procedure according to the scores of the nomograms and apply intervention measures of different intensities for people at different risk levels. During the follow-up process, an individualized follow-up plan can be further formulated according to the risk classification. Identifying and analyzing risk factors is essential for making appropriate clinical decisions to prevent restenosis.

Investigation Into the Risk Factors Related to In-stent Restenosis in Elderly Patients With Coronary Heart Disease and Type 2 Diabetes Within 2 Years After the First Drug-Eluting Stent Implantation

Background

The present study aims to explore risk factors related to in-stent restenosis (ISR) in elderly patients with coronary heart disease and type 2 diabetes within 2 years after the first drug-eluting stent (DES) implantation.

Methods

This case-control study retrospectively analyzed the clinical data of patients with coronary heart disease and diabetes undergoing percutaneous coronary intervention (PCI) in Shenzhen Sun Yat-sen Cardiovascular Hospital between January 2010 and March 2020. Univariate and multivariate models were used to assess independent factors for DES-ISR. Categorical principal component analysis of clinical variables was performed to determine important components for DES-ISR. Nomogram was constructed to quantitatively predict the probability of DES-ISR development. The diagnostic potential of clinical variables was determined by receiver operating characteristic curve.

Results

In the derivation cohort, 1,741 cases were included in this study, and a total of 227 pairs of cases and controls were generated by propensity score matching. In the validation cohort, 102 cases were included with 19 cases (18.6%) with DES-ISR. Glomerular filtration rate &lt;60 ml/min/1.73 m2, fasting blood glucose ≥6.5 mmol/L, multivessel coronary artery disease, coronary artery diffuse disease, PCI operation time (≥60 min), emergency PCI were associated with ISR. High Nomogram score was associated with the increased risk of ISR. Further analysis of the validation cohort showed that higher levels of HbA1c-coefficient of variation (CV) were significantly associated with the increased risk of ISR. HbA1c-CV exhibited good predictive ability for ISR in the validation cohort.

Conclusions

In conclusion, the fasting blood glucose level during the perioperative period of emergency PCI and the long-term variation of HbA1c during the follow-up period are related to the incidence of DES-ISR and the degree of stenosis. Reducing blood glucose fluctuations may decrease the risk of DES-ISR.

The Early Predictive Value of Circulating Monocytes and Eosinophils in Coronary DES Restenosis

Background

Monocytes and eosinophils are involved in intracoronary inflammatory responses, aggravating coronary artery plaque instability and in-stent restenosis (ISR).

Aims

To investigate an early prediction of ISR in patients undergoing stenting by circulating monocytes and eosinophils.

Methods

The single-center data of patients undergoing successful drug-eluting stents (DES) implantation from January 1, 2017 to April 30, 2020 were retrospectively analyzed. Of the 4,392 patients assessed, 140 patients with restenosis and 141 patients without restenosis were enrolled. A scheduled postoperative follow-up was proceeded in four sessions: 0-3 months, 3-6 months, 6-12 months, and >12 months. The hematological and biochemical measurement was collected. The angiographic review was completed within two postoperative years.

Results

Significant associations of monocyte count and percentage with ISR were evident [odds ratio (OR): 1.44, 95% CI: 1.23-1.68, P < 0.001; OR: 1.47, 95%CI: 1.24-1.74, P < 0.001, respectively], which began at 3 months postoperatively and persisted throughout the follow-up period. Eosinophil count and percentage were associated with ISR (OR: 1.22, 95%CI: 1.09-1.36, P = 0.001; OR: 1.23, 95%CI: 1.07-1.40, P = 0.003, respectively), with ISR most significantly associated with the baseline eosinophils. The receiver operating characteristic (ROC) curve analysis showed that the cutoff points of monocyte count and percentage in the ISR prediction were 0.46× 10⁹/L and 7.4%, respectively, and those of eosinophil count and percentage were 0.20 × 10⁹/L and 2.5%, respectively.

Conclusion

This study, with a long-term follow-up, first provides evidence that the elevated monocytes at three postoperative months and baseline eosinophils may be strong early predictors of ISR after drug-eluting stent implantation. Persistent elevation of monocytes may also be a signal of ISR after percutaneous coronary intervention (PCI).

Impact of anemia on in-stent restenosis after percutaneous coronary intervention

Background

Anemia is a common risk factor for post-percutaneous coronary intervention (PCI) adverse events; however, data on its association with in-stent restenosis (ISR) is limited.

Methods

538 patients who underwent PCI between January 2017 and September 2019 and follow-up angiography 9–12 months after the initial PCI were enrolled in this study. Baseline clinical and procedural characteristics were compared between the ISR and non-ISR groups, and independent predictors of ISR were determined using propensity score matching.

Results

The incidence of anemia was 53.5% in patients with ISR and 19.0% in those without ISR. Univariable logistic regression analyses showed that anemia (OR, 4.283; 95% CI, 1.949–9.410; P < 0.001), diabetes mellitus (OR, 2.588; 95% CI, 1.176–5.696; P = 0.018), chronic kidney disease (OR, 3.058; 95% CI, 1.289–7.252; P = 0.011), multiple stenting (OR, 2.592; 95% CI, 1.205–5.573; P = 0.015), bifurcation lesion (OR, 2.669; 95% CI, 1.236–5.763; P = 0.012), and calcification (OR, 3.529; 95% CI, 1.131–11.014; P = 0.030) were closely associated with ISR. Low-density lipoprotein cholesterol (LDL-c) levels and stent diameter were also significantly linked to ISR, as was anemia (P = 0.009) after propensity score matching.

Conclusion

Anemia is closely associated with post-PCI ISR, and patients with lower hemoglobin levels are at a higher risk of ISR.

A prediction model based on platelet parameters, lipid levels, and angiographic characteristics to predict in-stent restenosis in coronary artery disease patients implanted with drug-eluting stents

BACKGROUND

The present study was aimed to establish a prediction model for in-stent restenosis (ISR) in subjects who had undergone percutaneous coronary intervention (PCI) with drug-eluting stents (DESs).

MATERIALS AND METHODS

A retrospective cohort study was conducted. From September 2010 to September 2013, we included 968 subjects who had received coronary follow-up angiography after primary PCI. The logistic regression analysis, receiver operator characteristic (ROC) analysis, nomogram analysis, Hosmer-Lemeshow χ2 statistic, and calibration curve were applied to build and evaluate the prediction model.

RESULTS

Fifty-six patients (5.79%) occurred ISR. The platelet distribution width (PDW), total cholesterol (TC), systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), and lesion vessels had significant differences between ISR and non-ISR groups (all P < 0.05). And these variables were independently associated with ISR (all P < 0.05). Furthermore, they were identified as predictors (all AUC > 0.5 and P < 0.05) to establish a prediction model. The prediction model showed a good value of area under curve (AUC) (95%CI): 0.72 (0.64-0.80), and its optimized cut-off was 6.39 with 71% sensitivity and 65% specificity to predict ISR.

CONCLUSION

The incidence of ISR is 5.79% in CAD patients with DES implantation in the Xinjiang population, China. The prediction model based on PDW, SBP, TC, LDL-C, and lesion vessels was an effective model to predict ISR in CAD patients with DESs implantation.

Fisetin Alleviates Neointimal Hyperplasia via PPARγ/PON2 Antioxidative Pathway in SHR Rat Artery Injury Model

The phenotypic transformation of proliferation and migration in vascular smooth muscle cells (VSMCs) from media to intima is the basic pathology of neointimal hyperplasia after angioplasty in hypertensive patients. Angiotensin II (AngII) stimulates oxidative stress in VSMC, inducing VSMC proliferation and migration, which is a critical factor in both developments of hypertension and angioplasty-induced arterial restenosis. Fisetin, a plant flavonoid polyphenol, has been reported to be antioxidative and potent senolytic. It is unknown whether fisetin would inhibit neointimal hyperplasia. Therefore, we investigated the role of fisetin in neointimal formation in vitro and in vivo. The rat thoracic aortic smooth muscle cells (A10 cells) stimulated by AngII were used as the in vitro neointimal hyperplasia model, where AngII significantly induced the proliferation and migration in A10 cells. We found that fisetin could dose-dependently inhibit the effect of AngII via inducing the expression of an antioxidant, paraoxonase-2 (PON2), whose overexpression could inhibit the proliferation and migration of A10 cells and downexpression by siRNA had the opposite effect. Furthermore, we found the mechanism of fisetin's inducing PON2 expression involved PPARγ. Rosiglitazone, a PPARγ agonist, could increase PON2 expression in A10 cells, while the PPARγ inhibitor prevented the effect of fisetin on PON2. The in vivo neointimal hyperplasia model was established 2 weeks after the carotid artery balloon injury in SHR rats. Administration of fisetin (ip 3 mg/kg daily for 2 weeks) right after the injury significantly increased PON2 expression in the artery, inhibiting ROS production, and efficiently reduced carotid neointimal hyperplasia. These results indicate that fisetin increases the expression of antioxidant PON2 via activation of PPARγ, reducing oxidative stress, inhibiting VSMC proliferation and migration, and alleviates neointimal hyperplasia after intimal injury. PON2 may be a potential therapeutic target to reduce arterial remodeling after angioplasty in hypertensive patients.