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Deng W, Wang D, Wan Y, Lai S, Ding Y, Wang X. Prediction models for major adverse cardiovascular events after percutaneous coronary intervention: a systematic review. Front Cardiovasc Med 2024; 10:1287434. [PMID: 38259313 PMCID: PMC10800829 DOI: 10.3389/fcvm.2023.1287434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
Background The number of models developed for predicting major adverse cardiovascular events (MACE) in patients undergoing percutaneous coronary intervention (PCI) is increasing, but the performance of these models is unknown. The purpose of this systematic review is to evaluate, describe, and compare existing models and analyze the factors that can predict outcomes. Methods We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 during the execution of this review. Databases including Embase, PubMed, The Cochrane Library, Web of Science, CNKI, Wanfang Data, VIP, and SINOMED were comprehensively searched for identifying studies published from 1977 to 19 May 2023. Model development studies specifically designed for assessing the occurrence of MACE after PCI with or without external validation were included. Bias and transparency were evaluated by the Prediction Model Risk Of Bias Assessment Tool (PROBAST) and Transparent Reporting of a multivariate Individual Prognosis Or Diagnosis (TRIPOD) statement. The key findings were narratively summarized and presented in tables. Results A total of 5,234 articles were retrieved, and after thorough screening, 23 studies that met the predefined inclusion criteria were ultimately included. The models were mainly constructed using data from individuals diagnosed with ST-segment elevation myocardial infarction (STEMI). The discrimination of the models, as measured by the area under the curve (AUC) or C-index, varied between 0.638 and 0.96. The commonly used predictor variables include LVEF, age, Killip classification, diabetes, and various others. All models were determined to have a high risk of bias, and their adherence to the TRIPOD items was reported to be over 60%. Conclusion The existing models show some predictive ability, but all have a high risk of bias due to methodological shortcomings. This suggests that investigators should follow guidelines to develop high-quality models for better clinical service and dissemination. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=400835, Identifier CRD42023400835.
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Affiliation(s)
- Wenqi Deng
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Dayang Wang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute of Cardiovascular Diseases, Beijing University of Chinese Medicine, Beijing, China
| | - Yandi Wan
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Sijia Lai
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yukun Ding
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xian Wang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute of Cardiovascular Diseases, Beijing University of Chinese Medicine, Beijing, China
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Lu Y, Wang Y, Zhou B. Predicting long-term prognosis after percutaneous coronary intervention in patients with acute coronary syndromes: a prospective nested case-control analysis for county-level health services. Front Cardiovasc Med 2023; 10:1297527. [PMID: 38111892 PMCID: PMC10725923 DOI: 10.3389/fcvm.2023.1297527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/21/2023] [Indexed: 12/20/2023] Open
Abstract
Purpose We aimed to establish and authenticate a clinical prognostic nomogram for predicting long-term Major Adverse Cardiovascular Events (MACEs) among high-risk patients who have undergone Percutaneous Coronary Intervention (PCI) in county-level health service. Patients and methods This prospective study included Acute Coronary Syndrome (ACS) patients treated with PCI at six county-level hospitals between September 2018 and August 2019, selected from both the original training set and external validation set. Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques and logistic regression were used to assess potential risk factors and construct a risk predictive nomogram. Additionally, the potential non-linear relationships between continuous variables were tested using Restricted Cubic Splines (RCS). The performance of the nomogram was evaluated based on the Receiver Operating Characteristic (ROC) curve analysis, Calibration Curve, Decision Curve Analysis (DCA), and Clinical Impact Curve (CIC). Results The original training set and external validation set comprised 520 and 1,061 patients, respectively. The final nomogram was developed using nine clinical variables: Age, Killip functional classification III-IV, Hypertension, Hyperhomocysteinemia, Heart failure, Number of stents, Multivessel disease, Low-density Lipoprotein Cholesterol, and Left Ventricular Ejection Fraction. The AUC of the nomogram was 0.79 and 0.75 in the training set and external validation set, respectively. The DCA and CIC validated the clinical value of the constructed prognostic nomogram. Conclusion We developed and validated a prognostic nomogram for predicting the probability of 3-year MACEs in ACS patients who underwent PCI at county-level hospitals. The nomogram could provide a precise risk assessment for secondary prevention in ACS patients receiving PCI.
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Affiliation(s)
| | | | - Bo Zhou
- Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, Shenyang, China
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Hu Q, Li PX, Li YS, Ren Q, Zhang J, Liang YC, Zhang QY, Han YL. Daily exercise improves the long-term prognosis of patients with acute coronary syndrome. Front Public Health 2023; 11:1126413. [PMID: 37006550 PMCID: PMC10050345 DOI: 10.3389/fpubh.2023.1126413] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 02/22/2023] [Indexed: 03/17/2023] Open
Abstract
ObjectiveTo demonstrate the effect of daily exercise on the incidence of major adverse cardiovascular events (MACE) for patients with acute coronary syndrome (ACS).MethodsA cohort of 9,636 patients with ACS were consecutively enrolled in our retrospective study between November 2015 and September 2017, which were used for model development. 6,745 patients were assigned as the derivation cohort and 2,891 patients were assigned as the validation cohort. The least absolute shrinkage and selection operator (LASSO) regression and COX regression were used to screen out significant variables for the construction of the nomogram. Multivariable COX regression analysis was employed for the development of a model represented by a nomogram. The nomogram was then evaluated for performance traits such as discrimination, calibration, and clinical efficacy.ResultsAmong 9,636 patients with ACS (mean [SD] age, 60.3 [10.4] years; 7,235 men [75.1%]), the 5-year incidence for MACE was 0.19 at a median follow-up of 1,747 (1,160–1,825) days. Derived from the LASSO regression and COX regression, the nomogram has included 15 factors in total including age, previous myocardial infarction (MI), previous percutaneous coronary intervention (PCI), systolic pressure, N-terminal Pro-B-type natriuretic peptide (NT-proBNP), high-density lipoprotein cholesterol (HDL), serum creatinine, left ventricular end-diastolic diameter (LVEDD), Killip class, the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score, left anterior descending (LAD) stenosis (≥50%), circumflex (LCX) stenosis (≥50%), right coronary artery (RCA) stenosis (≥50%), exercise intensity, cumulative time. The 5-year area under the ROC curve (AUC) of derivation and validation cohorts were 0.659 (0.643–0.676) and 0.653 (0.629–0.677), respectively. The calibration plots showed the strong concordance performance of the nomogram model in both two cohorts. Moreover, decision curve analysis (DCA) also showed the usefulness of nomogram in clinical practice.ConclusionThe present work provided a prediction nomogram predicting MACE for patients with ACS after incorporating the already known factors and the daily exercise, which demonstrated the effectiveness of daily exercise on the improvement of prognosis for patients with ACS.
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Affiliation(s)
- Qiang Hu
- Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China
- Department of Cardiology, Air Force Hospital of Western Theater Command, Chengdu, China
| | - Peng-Xiao Li
- Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China
- Department of Cardiology, Xijing Hospital, Air Force Medical University, Xi'an, Shaanxi, China
| | - Yu-Shan Li
- Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Qiang Ren
- Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Jian Zhang
- Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Yan-Chun Liang
- Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Quan-Yu Zhang
- Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China
- *Correspondence: Quan-Yu Zhang
| | - Ya-Ling Han
- Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, China
- Ya-Ling Han
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Fang C, Chen Z, Zhang J, Jin X, Yang M. Construction and evaluation of nomogram model for individualized prediction of risk of major adverse cardiovascular events during hospitalization after percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction. Front Cardiovasc Med 2022; 9:1050785. [PMID: 36620648 PMCID: PMC9810984 DOI: 10.3389/fcvm.2022.1050785] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Background Emergency percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI) helps to reduce the occurrence of major adverse cardiovascular events (MACEs) such as death, cardiogenic shock, and malignant arrhythmia, but in-hospital MACEs may still occur after emergency PCI, and their mortality is significantly increased once they occur. The aim of this study was to investigate the risk factors associated with MACE during hospitalization after PCI in STEMI patients, construct a nomogram prediction model and evaluate its effectiveness. Methods A retrospective analysis of 466 STEMI patients admitted to our hospital from January 2018 to June 2022. According to the occurrence of MACE during hospitalization, they were divided into MACE group (n = 127) and non-MACE group (n = 339), and the clinical data of the two groups were compared; least absolute shrinkage and selection operator (LASSO) regression was used to screen out the predictors with non-zero coefficients, and multivariate Logistic regression was used to analyze STEMI Independent risk factors for in-hospital MACE in patients after emergency PCI; a nomogram model for predicting the risk of in-hospital MACE in STEMI patients after PCI was constructed based on predictive factors, and the C-index was used to evaluate the predictive performance of the prediction model; the Bootstrap method was used to repeat sampling 1,000 Internal validation was carried out for the second time, the Hosmer-Lemeshow test was used to evaluate the model fit, and the calibration curve was drawn to evaluate the calibration degree of the model. Receiver operating characteristic (ROC) curves were drawn to evaluate the efficacy of the nomogram model and thrombolysis in myocardial infarction (TIMI) score in predicting in-hospital MACE in STEMI patients after acute PCI. Results The results of LASSO regression showed that systolic blood pressure, diastolic blood pressure, Killip grade II-IV, urea nitrogen and left ventricular ejection fraction (LVEF), IABP, NT-ProBNP were important predictors with non-zero coefficients, and multivariate logistic regression analysis was performed to analyze that Killip grade II-IV, urea nitrogen, LVEF, and NT-ProBNP were independent factors for in-hospital MACE after PCI in STEMI patients; a nomogram model for predicting the risk of in-hospital MACE after PCI in STEMI patients was constructed with the above independent predictors, with a C-index of 0.826 (95% CI: 0.785-0.868) having a good predictive power; the results of H-L goodness of fit test showed χ2 = 1.3328, P = 0.25, the model calibration curve was close to the ideal model, and the internal validation C-index was 0.818; clinical decision analysis also showed that the nomogram model had a good clinical efficacy, especially when the threshold probability was 0.1-0.99, the nomogram model could bring clinical net benefits to patients. The nomogram model predicted a greater AUC (0.826) than the TIMI score (0.696) for in-hospital MACE after PCI in STEMI patients. Conclusion Urea nitrogen, Killip class II-IV, LVEF, and NT-ProBNP are independent factors for in-hospital MACE after PCI in STEMI patients, and nomogram models constructed based on the above factors have high predictive efficacy and feasibility.
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Affiliation(s)
- Caoyang Fang
- Department of Cardiology, The Second People’s Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, China,Department of Cardiology, Hefei Second People’s Hospital Affiliated to Bengbu Medical College, Hefei, Anhui, China
| | - Zhenfei Chen
- Department of Cardiology, The Second People’s Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, China,*Correspondence: Zhenfei Chen,
| | - Jinig Zhang
- Department of Cardiology, The Second People’s Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, China
| | - Xiaoqin Jin
- Department of Cardiology, The Second People’s Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, China,Department of Cardiology, Hefei Second People’s Hospital Affiliated to Bengbu Medical College, Hefei, Anhui, China
| | - Mengsi Yang
- Department of Cardiology, The Second People’s Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, China
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