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Zhang F, Song HX, Zheng LH, An YB, Liu P. Long-term clinical efficacy of drug-coated balloon angioplasty for TASCII C/D femoropopliteal lesions in older patients with chronic limb-threatening ischemia: A retrospective study. Medicine (Baltimore) 2024; 103:e39331. [PMID: 39151525 PMCID: PMC11332706 DOI: 10.1097/md.0000000000039331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 04/10/2024] [Accepted: 07/26/2024] [Indexed: 08/19/2024] Open
Abstract
This study aimed to evaluate the long-term clinical outcomes of drug-coated drug (DCB) angioplasty for long femoropopliteal lesions in older patients with chronic limb-threatening ischemia (CLTI). In this multi-center retrospective study, we enrolled 119 patients with CLTI due to Trans-Atlantic Inter-Society Consensus (TASCII) C/D femoropopliteal lesions who underwent DCB angioplasty. A total of 119 patients with 122 limbs (TASCII C = 67, 54.9%; TASCII D = 55, 45.1%) were enrolled. At 36-month follow-up, primary patency, assisted primary patency, secondary patency, and freedom from target lesion revascularization were 47.3%, 49.8%, 59.5%, and 62.7%, respectively, and there was a significant improvement over baseline in Rutherford class (P < .001) and ankle-brachial index measurements (P < .001). Complex target lesions (P = .017) and 1 stenosis-free outflow vessel (P = .001) were risk predictors of freedom from clinically driven target lesion revascularization. Complex target lesions (P = .044), diabetes (P = .007), and 1 stenosis-free outflow vessel (P = .003) were risk predictors of restenosis. At 2 months, the ulcer healing rate was 96.3% (26/27). At 36 months, the limb salvage and survival rates were 85.8% and 83.3%, respectively. DCB angioplasty were safe and effective for older patients with CLTI attributable to femoropopliteal TASCII C/D lesions.
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Affiliation(s)
- Feng Zhang
- Department of Vascular and Endovascular Surgery, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, PR China
| | - Hai-Xia Song
- Department of Neurology, Shijiazhuang People’s Hospital, Shijiazhuang, Hebei, PR China
| | - Li-Hua Zheng
- Department of Vascular and Endovascular Surgery, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, PR China
| | - Yan-Bo An
- Department of Vascular and Endovascular Surgery, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, PR China
| | - Peng Liu
- Department of Vascular and Endovascular Surgery, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, PR China
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Liu S, Yang H, Liu C, Liu Z, Hou J, Wei M, Luo S, Zhou Y, Wang P, Fu Z. A risk score for predicting in-stent restenosis in patients with premature acute myocardial infarction undergoing percutaneous coronary intervention with drug-eluting stent. Heliyon 2024; 10:e34077. [PMID: 39055837 PMCID: PMC11269898 DOI: 10.1016/j.heliyon.2024.e34077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 06/24/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
Background This study aimed at developing and validating a risk score to predict in-stent restenosis (ISR) in patients with premature acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI) with drug-eluting stent (DES). Methods This was a two-center retrospective study. A total of 2185 patients firstly diagnosed with premature AMI (age ≥18 years and <55 years in men, <65 years in women) from Xinjiang cohort were retrospectively analyzed. After filtering by exclusion criteria, patients were randomly divided into training cohort (n = 434) and internal validation cohort (n = 186) at a 7:3 ratio. Several candidate variables associated with ISR in the training cohort were assessed by the least absolute shrinkage and selection operator and logistic regression analysis. The ISR risk nomogram score based on the superior predictors was finally developed, and then validated in the internal validation cohort and in an independent Chengdu external validation cohort (n = 192). The higher total nomogram score, the greater the ISR risk. Results The eight variables in the final risk nomogram score, cardiovascular-kidney-metabolic (CKM) score included age, diabetes mellitus (DM), body mass index (BMI), systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDLC), estimated glomerular filtration rate (eGFR), stent in left anterior descending coronary artery, minimum stent diameter <3 mm. The areas under the curve (AUC) and C-statistics [training cohort: 0.834 (95%CI: 0.787 to 0.882); internal validation cohort: 0.852 (95%CI: 0.784 to 0.921); Chengdu external validation cohort: 0.787 (95%CI: 0.692 to 0.882), respectively)] demonstrated the good discrimination of the CKM score. The Hosmer-Lemeshow test (χ2 = 7.86, P = 0.448; χ2 = 5.17, P = 0.740; χ2 = 6.35, P = 0.608, respectively) and the calibration curve confirmed the good calibration of the CKM score. Decision curve analysis (DCA) testified the clinical net benefit of the CKM score in the training and validation cohort. Conclusion This study provided a well-developed and validated risk nomogram score, the CKM score to predict ISR in patients with premature AMI undergoing PCI with DES. Given that these variables are readily available and practical, the CKM score should be widely adopted for individualized assessment and management of premature AMI.
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Affiliation(s)
- Sen Liu
- Heart Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China
| | - Hong Yang
- Heart Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China
| | - Cheng Liu
- Heart Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China
| | - Ziyang Liu
- Heart Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China
| | - Jixin Hou
- Department of Cardiology, The First Affiliated Hospital, Chengdu Medical College, Chengdu, 610500, Sichuan, China
| | - Mengwei Wei
- Heart Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China
| | - Sifu Luo
- Heart Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China
| | - Yaqi Zhou
- Heart Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China
| | - Peijian Wang
- Department of Cardiology, The First Affiliated Hospital, Chengdu Medical College, Chengdu, 610500, Sichuan, China
| | - Zhenyan Fu
- Heart Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China
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Shahsanaei F, Gharibzadeh A, Behrooj S, Abbaszadeh S, Nourmohammadi M. A systematic review and bioinformatic study on clinical, paraclinical, and genetic factors predisposing to stent restenosis following percutaneous coronary intervention. BMC Cardiovasc Disord 2024; 24:304. [PMID: 38877398 PMCID: PMC11177414 DOI: 10.1186/s12872-024-03955-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 05/22/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Stent restenosis is a relatively common phenomenon among patients with coronary heart disease undergoing percutaneous coronary intervention (PCI). It seems that a set of clinical, laboratory, and even genetic factors make people susceptible to such a phenomenon and in fact, this is multi-factorial. We aimed to first determine the underlying clinical and laboratory risk factors for the occurrence of stent re-stenosis after PCI based on a systematic review study, and after that, through a bioinformatics study, to evaluate the related genes and microRNAs with the occurrence of stent re-stenosis. MAIN TEXT In the first step, the manuscript databases including Medline, Web of Knowledge, Google Scholar, Scopus, and Cochrane were deeply searched by the two blinded investigators for all eligible studies based on the considered keywords to introduce clinical and laboratory determinants of stent re-stenosis. In the bioinformatic phase, and following a review of the literature to identify genes and microRNAs involved in restenosis, the interaction of each gene with other genes associated with stent re-stenosis was determined by GeneMANIA network analysis and Cytoscape software. Overall, 67 articles (including 40,789 patients) on clinical and biochemical predictors for stent restenosis and 25 articles on genetic determinants of this event were eligible for the final analysis. The predictors for this event were categorized into four subgroups patient-based parameters including traditional cardiovascular risk profiles, stent-based parameters including type and diametric characteristics of the stents used, coronary lesion-based parameters including several two target lesions and coronary involvement severity and laboratory-based parameters particularly related to activation of inflammatory processes. In the bioinformatic phase, we uncovered 42 genes that have been described to be involved in such a phenomenon considering a special position for genes encoding inflammatory cytokines. Also, 12 microRNAs have been pointed to be involved in targeting genes involved in stent re-stenosis. CONCLUSIONS The incidence of stent re-stenosis will be the result of a complex interaction of clinical risk factors, laboratory factors mostly related to the activation of inflammatory processes, and a complex network of gene-to-gene interactions.
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Affiliation(s)
- Farzad Shahsanaei
- Cardiovascular Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Abdullah Gharibzadeh
- Cardiovascular Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Soudabeh Behrooj
- Cardiovascular Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Shahin Abbaszadeh
- Cardiovascular Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
| | - Mahboobeh Nourmohammadi
- Cardiovascular Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
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Wang H, Ma A, Wang T. Nomogram to Predict Outcomes After Staged Revascularization in ST-Segment Elevation Myocardial Infarction and Multivessel Coronary Artery Disease. Int J Gen Med 2024; 17:1713-1722. [PMID: 38706752 PMCID: PMC11067940 DOI: 10.2147/ijgm.s457236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 04/20/2024] [Indexed: 05/07/2024] Open
Abstract
Objective Approximately 50% of ST-segment elevation myocardial infarction (STEMI) patients have multivessel coronary artery disease (MVD). The management strategy for these patients remains controversial. This study aimed to develop predictive models and nomogram of outcomes in STEMI patients with MVD for better identification and classification. Methods The least absolute shrinkage and selection operator (LASSO) method was used to select the features most significantly associated with the outcomes. A Cox regression model was built using the selected variables. One nomogram was computed from each model, and individual risk scores were obtained by applying the nomograms to the cohort. After regrouping patients based on nomogram risk scores into low- and high-risk groups, we used the Kaplan-Meier method to perform survival analysis. Results The C-index of the major adverse cardiovascular event (MACE)-free survival model was 0·68 (95% CI 0·62-0·74) and 0·65 [0·62-0·68]) at internal validation, and that of the overall survival model was 0·75 (95% CI 0·66-0·84) and (0·73 [0·65-0·81]). The predictions of both models correlated with the observed outcomes. Low-risk patients had significantly lower probabilities of 1-year or 3-year MACEs (4% versus 11%, P= 0.003; 7% versus 15%, P=0.01, respectively) and 1-year or 3-year all-cause death (1% versus 3%, P=0.048; 2% versus 7%, respectively, P=0.001) than high-risk patients. Conclusion Our nomograms can be used to predict STEMI and MVD outcomes in a simple and practical way for patients who undergo primary PCI for culprit vessels and staged PCI for non-culprit vessels.
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Affiliation(s)
- Huaigen Wang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
| | - Aiqun Ma
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
- Shaanxi Key Laboratory of Molecular Cardiology (Xi’an Jiaotong University), Xi’an, Shaanxi, People’s Republic of China
| | - Tingzhong Wang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China
- Shaanxi Key Laboratory of Molecular Cardiology (Xi’an Jiaotong University), Xi’an, Shaanxi, People’s Republic of China
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Murat B, Murat S, Gorenek B. Comment on the Association of Coronary Artery Severity and Late In-Stent Restenosis: An Angiographic Imaging Study. Angiology 2024:33197241246917. [PMID: 38592143 DOI: 10.1177/00033197241246917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Affiliation(s)
| | - Selda Murat
- Eskisehir Osmangazi University Medical Faculty, Eskisehir, Turkey
| | - Bulent Gorenek
- Eskisehir Osmangazi University Medical Faculty, Eskisehir, Turkey
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Ninno F, Tsui J, Balabani S, Díaz-Zuccarini V. A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries. JVS Vasc Sci 2023; 4:100128. [PMID: 38023962 PMCID: PMC10663814 DOI: 10.1016/j.jvssci.2023.100128] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 09/10/2023] [Indexed: 12/01/2023] Open
Abstract
Objective Restenosis is a significant complication of revascularization treatments in coronary and peripheral arteries, sometimes necessitating repeated intervention. Establishing when restenosis will happen is extremely difficult due to the interplay of multiple variables and factors. Standard clinical and Doppler ultrasound scans surveillance follow-ups are the only tools clinicians can rely on to monitor intervention outcomes. However, implementing efficient surveillance programs is hindered by health care system limitations, patients' comorbidities, and compliance. Predictive models classifying patients according to their risk of developing restenosis over a specific period will allow the development of tailored surveillance, prevention programs, and efficient clinical workflows. This review aims to: (1) summarize the state-of-the-art in predictive models for restenosis in coronary and peripheral arteries; (2) compare their performance in terms of predictive power; and (3) provide an outlook for potentially improved predictive models. Methods We carried out a comprehensive literature review by accessing the PubMed/MEDLINE database according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search strategy consisted of a combination of keywords and included studies focusing on predictive models of restenosis published between January 1993 and April 2023. One author independently screened titles and abstracts and checked for eligibility. The rest of the authors independently confirmed and discussed in case of any disagreement. The search of published literature identified 22 studies providing two perspectives-clinical and biomechanical engineering-on restenosis and comprising distinct methodologies, predictors, and study designs. We compared predictive models' performance on discrimination and calibration aspects. We reported the performance of models simulating reocclusion progression, evaluated by comparison with clinical images. Results Clinical perspective studies consider only routinely collected patient information as restenosis predictors. Our review reveals that clinical models adopting traditional statistics (n = 14) exhibit only modest predictive power. The latter improves when machine learning algorithms (n = 4) are employed. The logistic regression models of the biomechanical engineering perspective (n = 2) show enhanced predictive power when hemodynamic descriptors linked to restenosis are fused with a limited set of clinical risk factors. Biomechanical engineering studies simulating restenosis progression (n = 2) are able to capture its evolution but are computationally expensive and lack risk scoring for individual patients at specific follow-ups. Conclusions Restenosis predictive models, based solely on routine clinical risk factors and using classical statistics, inadequately predict the occurrence of restenosis. Risk stratification models with increased predictive power can be potentially built by adopting machine learning techniques and incorporating critical information regarding vessel hemodynamics arising from biomechanical engineering analyses.
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Affiliation(s)
- Federica Ninno
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Wellcome-EPSRC Centre for Interventional Surgical Sciences, London, United Kingdom
| | - Janice Tsui
- Department of Vascular Surgery, Royal Free Hospital NHS Foundation Trust, London, United Kingdom
- Division of Surgery & Interventional Science, Department of Surgical Biotechnology, Faculty of Medical Sciences, University College London, Royal Free Campus, London, United Kingdom
| | - Stavroula Balabani
- Wellcome-EPSRC Centre for Interventional Surgical Sciences, London, United Kingdom
- Department of Mechanical Engineering, University College London, London, United Kingdom
| | - Vanessa Díaz-Zuccarini
- Wellcome-EPSRC Centre for Interventional Surgical Sciences, London, United Kingdom
- Department of Mechanical Engineering, University College London, London, United Kingdom
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Xi H, Liu J, Xu T, Li Z, Mou X, Jin Y, Xia S. Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease. Front Cardiovasc Med 2023; 10:1117915. [PMID: 36970340 PMCID: PMC10035367 DOI: 10.3389/fcvm.2023.1117915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
ObjectiveTo 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.MethodsThis 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.ResultsIn 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.ConclusionsHypertension, 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.
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Affiliation(s)
- Hongfei Xi
- Department of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, China
| | - Jiasi Liu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Xu
- Department of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, China
| | - Zhe Li
- Department of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, China
| | - Xuanting Mou
- Department of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, China
| | - Yu Jin
- Department of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, China
| | - Shudong Xia
- Department of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, China
- Correspondence: Shudong Xia
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Chen J, Tang Y, Shen Z, Wang W, Hou J, Li J, Chen B, Mei Y, Liu S, Zhang L, Lu S. Predicting and Analyzing Restenosis Risk after Endovascular Treatment in Lower Extremity Arterial Disease: Development and Assessment of a Predictive Nomogram. J Endovasc Ther 2023:15266028231158294. [PMID: 36891634 DOI: 10.1177/15266028231158294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
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.
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Affiliation(s)
- Jinxing Chen
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, Shaanxi, P. R. China
| | - Yanan Tang
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, Shaanxi, P. R. China
| | - Zekun Shen
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, Shaanxi, P. R. China
| | - Weiyi Wang
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, Shaanxi, P. R. China
| | - Jiaxuan Hou
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, Shaanxi, P. R. China
| | - Jiayan Li
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, Shaanxi, P. R. China
| | - Bingyi Chen
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, Shaanxi, P. R. China
| | - Yifan Mei
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, Shaanxi, P. R. China
| | - Shuang Liu
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, Shaanxi, P. R. China
| | - Liwei Zhang
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, Shaanxi, P. R. China
| | - Shaoying Lu
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, Shaanxi, P. R. China
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In silico evaluation of additively manufactured 316L stainless steel stent in a patient-specific coronary artery. Med Eng Phys 2022; 109:103909. [DOI: 10.1016/j.medengphy.2022.103909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/04/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
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10
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Feng Q, Zhao Y, Wang H, Zhao J, Wang X, Shi J. A predictive model involving serum uric acid, C-reactive protein, diabetes, hypercholesteremia, multiple lesions for restenosis risk in everolimus-eluting stent-treated coronary heart disease patients. Front Cardiovasc Med 2022; 9:857922. [PMID: 36035940 PMCID: PMC9403046 DOI: 10.3389/fcvm.2022.857922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeAs a second-generation drug-eluting stent, the restenosis risk factors of the everolimus-eluting stent (EES) lack sufficient evidence. Therefore, the study investigated the in-stent restenosis occurrence and its predictive factors among patients with coronary heart disease (CHD) who underwent percutaneous coronary intervention (PCI) with EES.Materials and methodsTotally, 235 patients with CHD who underwent PCI with EES were included. At 1 year post PCI with EES (or earlier if clinically indicated), coronary angiography was performed to evaluate the in-stent restenosis status.ResultsWithin 1 year post-operation, 20 patients developed in-stent restenosis while 215 patients did not develop in-stent restenosis, resulting in a 1-year in-stent restenosis rate of 8.5%. Diabetes mellitus, hypercholesteremia, hyperuricemia, fasting blood glucose, serum uric acid (SUA), high-sensitivity C-reactive protein (HsCRP), target lesions in the left circumflex artery, patients with two target lesions, length of target lesions and length of stent positively correlated with in-stent restenosis risk, while high-density lipoprotein cholesterol negatively associated with in-stent restenosis risk. Notably, diabetes mellitus, hypercholesteremia, SUA, HsCRP levels, and patients with two target lesions were independent predictive factors for in-stent restenosis risk by multivariate logistic regression analysis. Then, the in-stent restenosis risk prediction model was established based on these independent predictive factors, which exhibited an excellent value in predicting in-stent restenosis risk (area under the curve: 0.863; 95% CI: 0.779–0.848) by receiver operating characteristic analysis.ConclusionIn-stent restenosis risk prediction model, consisting of diabetes mellitus, hypercholesteremia, SUA, HsCRP, and patients with two target lesions, may predict in-stent restenosis risk in patients with CHD who underwent post-PCI with EES.
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Affiliation(s)
- Qiang Feng
- Department of Cardiology, Handan Central Hospital, Handan, China
- *Correspondence: Qiang Feng,
| | - Ying Zhao
- Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Haiyan Wang
- Department of Cardiology, Handan Central Hospital, Handan, China
| | - Jiayu Zhao
- Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xun Wang
- Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jianping Shi
- Department of Cardiology, Handan Central Hospital, Handan, China
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Luo Y, Tan N, Zhao J, Li Y. A Nomogram for Predicting In-Stent Restenosis Risk in Patients Undergoing Percutaneous Coronary Intervention: A Population-Based Analysis. Int J Gen Med 2022; 15:2451-2461. [PMID: 35264881 PMCID: PMC8901259 DOI: 10.2147/ijgm.s357250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/16/2022] [Indexed: 12/13/2022] Open
Abstract
Objective In-stent restenosis (ISR) is a fatal complication of percutaneous coronary intervention (PCI). An early predictive model with the medical history of patients, angiographic characteristics, inflammatory indicators and blood biochemical index is urgently needed to predict ISR events. We aim to establish a risk prediction model for ISR in CAD patients undergoing PCI. Methods A total of 477 CAD patients who underwent PCI with DES (drug-eluting stents) between January 2017 and December 2020 were retrospectively enrolled. And the preoperative factors were compared between the non-ISR and ISR groups. The least absolute shrinkage and selection operator (LASSO) and multi-factor logistic regression were used for statistical analysis. The prediction model was evaluated using receiver operator characteristic (ROC) analysis, the Hosmer–Lemeshow 2 statistic, and the calibration curve. Results In this study, 94 patients developed ISR after PCI. Univariate analysis showed that post-PCI ISR was associated with the underlying disease (COPD), higher Gensini score (GS score), higher LDL-C, higher neutrophil/lymphocyte ratio, and higher remnant cholesterol (RC). The multi-factor logistic regression analysis suggested that remnant cholesterol (odds ratio [OR] = 2.09, 95% confidence interval [CI] [1.40–3.11], P < 0.001), GS score (OR = 1.01, 95% CI [1.00, 1.02], P = 0.002), medical history of COPD (OR = 4.56, 95% CI [1.98, 10.40], P < 0.001), and monocyte (OR = 1.30, 95% CI [1.04, 1.70], P < 0.001) were independent risk factors for ISR. A nomogram was generated and displayed favorable fitting (Hosmer-Lemeshow test P = 0.609), discrimination (area under ROC curve was 0.847), and clinical usefulness by decision curve analysis. Conclusion Patients with certain preoperative characteristics, such as a history of COPD, higher GS scores, higher levels of RC, and monocytes, who undergo PCI may have a higher risk of developing ISR. The predictive nomogram, based on the above predictors, can be used to help identify patients who are at a higher risk of ISR early on, with a view to provide post-PCI health management for patients.
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Affiliation(s)
- Yinhua Luo
- Department of Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Shiyan, Hubei Province, 442000, People’s Republic of China
| | - Ni Tan
- Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Enshi Prefecture, Hubei Province, 445000, People’s Republic of China
| | - Jingbo Zhao
- Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi Prefecture, Hubei Province, 445000, People’s Republic of China
| | - Yuanhong Li
- Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi Prefecture, Hubei Province, 445000, People’s Republic of China
- Correspondence: Yuanhong Li, Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Enshi Prefecture, People’s Republic of China, Email
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Luo Y, Cui S, Zhang C, Huang R, Zhao J, Su K, Luo D, Li Y. Prognostic Role of Fasting Remnant Cholesterol with In-Stent Restenosis After Drug-Eluting Stent Implantation. Int J Gen Med 2022; 15:1733-1742. [PMID: 35221713 PMCID: PMC8864410 DOI: 10.2147/ijgm.s348148] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 01/28/2022] [Indexed: 11/27/2022] Open
Abstract
Objective In-stent restenosis (ISR) is regarded as a critical limiting factor in stenting for coronary heart disease (CHD). Recent research has shown that fasting residual cholesterol (RC) has been shown to have a substantial impact on coronary heart disease. Unfortunately, there have not been much data to bear out the relationship between RC and ISR. Then, the predictive value of RC for in-stent restenosis in patients with coronary heart disease was analyzed. Patients and Methods Aiming to explore the relationship between RC and ISR, we designed a retrospective study of patients with CHD after drug-eluting stent (DES) implantation, combining the data from a public database and selecting the best-fitting model by comparing the optical subset with least absolute shrinkage and selection operator (LASSO) regression. Results Analysis of the abovementioned two models showed that the optical subset optimal subset model, which was based on RC, creatine, history of diabetes, smoking, multi-vessel lesions (2 vessels or more lesions), peripheral vascular lesions (PAD), and blood uric acid, had a better fit (AUC = 0.68), and that RC was an independent risk factor for ISR in the abovementioned two models. Notwithstanding its limitation, this study does suggest that RC has good predictive value for ISR. Conclusion Remnant cholesterol is an independent risk factor for in-stent restenosis after percutaneous coronary intervention (PCI) and is a reliable predictor of ISR.
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Affiliation(s)
- Yinhua Luo
- Department of Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Shiyan, People’s Republic of China
| | - Shengyu Cui
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China
| | - Changjiang Zhang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China
| | - Rui Huang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China
| | - Jinbo Zhao
- Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Enshi Prefecture, People’s Republic of China
| | - Ke Su
- Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Enshi Prefecture, People’s Republic of China
| | - Dan Luo
- Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Enshi Prefecture, People’s Republic of China
| | - Yuanhong Li
- Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Enshi Prefecture, People’s Republic of China
- Correspondence: Yuanhong Li, Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Enshi Prefecture, People’s Republic of China, Email
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