Establishment and Evaluation of Nomogram Model for Predicting the Risk of No Reflow after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction

Zhang, Z.; Ji, Zhen Guo; Du, Rong; Wang, X. M.; Yao, Lianke

doi:10.36468/pharmaceutical-sciences.spl.353

Cited by 4 publications

(4 citation statements)

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“…1,2 The culprit-only arm of the study of Di Mario et al 3 included less than 50 patients and no clear information about cardiovascular death was reported. Lastly, the study of Zhang et al 8 has been published only in Chinese. For these reasons, the previous four full texts have been excluded.…”

Section: Search Results and Study Selectionmentioning

confidence: 99%

“…These trials were mainly focused on small populations and with sample size not powered enough to obtain conclusive evidence. [1][2][3][4][5][6][7][8][9][10][11] To overcome these limitations several meta-analyses have been carried out. 12,13 However, they displayed several limitations and did not achieve an adequate sample size for reliable estimation in cardiovascular death.…”

Section: Introductionmentioning

confidence: 99%

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Complete revascularization reduces cardiovascular death in patients with ST-segment elevation myocardial infarction and multivessel disease: systematic review and meta-analysis of randomized clinical trials

Pavasini

Biscaglia

Barbato

et al. 2019

European Heart Journal

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Aims The aim of this work was to investigate the prognostic impact of revascularization of non-culprit lesions in patients with ST-segment elevation myocardial infarction (STEMI) and multivessel disease by performing a meta-analysis of available randomized clinical trials (RCTs). Methods and results Data from six RCTs comparing complete vs. culprit-only revascularization in STEMI patients with multivessel disease were analysed with random effect generic inverse variance method meta-analysis. The endpoints were expressed as hazard ratio (HR) with 95% confidence interval (CI). The primary outcome was cardiovascular death. Main secondary outcomes of interest were all-cause death, myocardial infarction (MI), and repeated coronary revascularization. Overall, 6528 patients were included (3139 complete group, 3389 culprit-only group). After a follow-up ranging between 1 and 3 years (median 2 years), cardiovascular death was significantly reduced in the group receiving complete revascularization (HR 0.62, 95% CI 0.39–0.97, I2 = 29%). The number needed to treat to prevent one cardiovascular death was 70 (95% CI 36–150). The secondary endpoints MI and revascularization were also significantly reduced (HR 0.68, 95% CI 0.55–0.84, I2 = 0% and HR 0.29, 95% CI 0.22–0.38, I2 = 36%, respectively). Needed to treats were 45 (95% CI 37–55) for MI and 8 (95% CI 5–13) for revascularization. All-cause death (HR 0.81, 95% CI 0.56–1.16, I2 = 27%) was not affected by the revascularization strategy. Conclusion In a selected study population of STEMI patients with multivessel disease, a complete revascularization strategy is associated with a reduction in cardiovascular death. This reduction is concomitant with that of MI and the need of repeated revascularization.

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Section: Search Results and Study Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Complete revascularization reduces cardiovascular death in patients with ST-segment elevation myocardial infarction and multivessel disease: systematic review and meta-analysis of randomized clinical trials

Pavasini

Biscaglia

Barbato

et al. 2019

European Heart Journal

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“…A nomogram is a graph with high and low line segments of scores based on multiple clinical indicators and multi-factor regression analysis, which is used to predict specific clinical outcomes or incidences of adverse events. It is more concise and accurate in quantifying risks [28]. Domestic studies have confirmed that nomograms have a good application effect in predicting the risk of CHD [29,30] and the risk of ventricular arrhythmia after PCI [31].…”

Section: Discussionmentioning

confidence: 99%

A risk predictive model for determining the severity of coronary artery lesions in older postmenopausal women with coronary heart disease

Wen

2024

Am J Cardiovasc Dis

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Objective: To determine the risk factors affecting the severity of coronary artery disease (CAD) in older postmenopausal women with coronary heart disease (CHD) and to construct a personalized risk predictive model. Methods: In this cohort study, clinical records of 527 female patients aged ≥60 with CHD who were hospitalized in the First Affiliated Hospital of the University of Science and Technology of China from March 2018 to February 2019 were analyzed retrospectively. The severity of CAD was determined using the Gensini scores that are based on coronary angiography findings. Patients with Gensini scores ≥40 and <40 were divided into high-risk (n=277) and non-high-risk groups (n=250), respectively. Logistic regression analysis was used to assess independent predictors of CAD severity. The nomogram prediction model of CAD severity was plotted by the R software. The area under the receiver operating characteristic (ROC) and calibration curves were used to evaluate the predictive efficiency of the nomogram model, and the decision curve analysis (DCA) was used to assess the clinical applicability of the nomogram model. Results: Multivariate analysis showed that high-sensitivity C-reactive protein, RBC count, WBC count, BMI, and diabetes mellitus were independent risk factors associated with CAD severity in older menopausal women (P<0.05); the area under the ROC curve of the nomogram constructed based on the independent risk factors was 0.846 (95% CI: 0.756-0.937). The area under the ROC curve after internal validation of the nomogram by the Bootstrap method after resampling 1000 times was 0.840 (95% CI: 0.741-0.923). The calibration curve suggested that the nomogram had an excellent predictive agreement, and the DCA curve indicated that the net benefit of applying the nomogram was significantly higher than that of the "no intervention" and "all intervention" methods when the risk probability of patients with high-risk CAD severity was 0.30-0.81. Conclusion: A personalized risk assessment model was constructed based on the risk factors of severe CAD in older menopausal women with CHD, which had good prediction efficiency based on discrimination, calibration, and clinical applicability evaluation indicators. This model could assist cardiology medical staff in screening older menopausal women with CHD who are at a high risk of severe CAD to implement targeted interventions.

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“…Based on the affecting risk factors, a prediction model; Nomogram is a prediction model selected by multifactor regression analysis could visualize the contribution of each affecting factor to the outcome events with more accuracy and reliability (15). In this study, a nomogram prediction model was established based on the risk factors affecting no-reflow after PCI in patients.…”

Section: Introductionmentioning

confidence: 99%

A Nomogram model for predicting the occurrence of no-reflow phenomenon after percutaneous coronary intervention using the lncRNA TUG1/miR-30e/NPPB biomarkers

Hu¹,

Cai²,

He³

et al. 2022

J Thorac Dis

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Background: Studies have shown that percutaneous coronary intervention (PCI) is considered as the essential therapeutic strategy for the patients with ST-segment elevation myocardial infarction (STEMI).However; no-reflow could still occur in a few patients after PCI. Studies have reported that biomarkers related to no-reflow pathogenetic components could play a prognostic role in the prediction phenomenon. Hence, this study explored the establishment of nomogram model for predicting the occurrence of no-reflow phenomenon after PCI using the lncRNA TUG1/miR-30e/NPPB biomarkers in patients with STEMI after PCI.Methods: In this observational study, a total of 76 STEMI patients who underwent emergency PCI between January 2018 and December 2021were included. The patients after PCI, were divided into reflow (n=44) and no-reflow groups (n=32). The demographic, environmental and clinical risk factors were assessed and analysed between the groups. Quantitative RT-PCR was used to detect TUG1, miR-30e, and NPPB messenger RNA (mRNA) expression levels in the plasma of patients after PCI. Bioinformatic methods were used to predict the interaction of the plasma TUG1/miR-30e/NPPB axis. The risk factors in the no-reflow group were screened using a logistic-regression analysis, and a nomogram prediction model was constructed and validated. Subsequently, a gene set enrichment analysis revealed the function of lncRNA TUG1.Results: Plasma lncRNA TUG1 and NPPB were more highly expressed and miR-30e was more lowly expressed in the no-reflow group than the normal-reflow group (P<0.001). A negative correlation was observed between lncRNA TUG1 and miR-30e, and between miR-30e and NPPB. However, a positive correlation was observed between lncRNA TUG1 and NPPB mRNA. The bioinformatics analysis predicted multiple binding sites on the lncRNA TUG1 and miR-30e. LncRNA TUG1 [odds ratio (OR): 0.163, 95% confidence interval (CI): 0.021-0.944] and hs-CRP (OR: 2.151, 95% CI: 1.536-3.974) found to be as independent predictors. The C-index of this prediction model was 0.982 (95% CI: 0.956-1.000).Conclusions: TUG1 could function as an effective biomarker for no-reflow among patients with STEMI after PCT and the proposed nomogram may provide information for individualized treatment in patients with STEMI.

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Establishment and Evaluation of Nomogram Model for Predicting the Risk of No Reflow after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction

Cited by 4 publications

References 0 publications

Complete revascularization reduces cardiovascular death in patients with ST-segment elevation myocardial infarction and multivessel disease: systematic review and meta-analysis of randomized clinical trials

Complete revascularization reduces cardiovascular death in patients with ST-segment elevation myocardial infarction and multivessel disease: systematic review and meta-analysis of randomized clinical trials

A risk predictive model for determining the severity of coronary artery lesions in older postmenopausal women with coronary heart disease

A Nomogram model for predicting the occurrence of no-reflow phenomenon after percutaneous coronary intervention using the lncRNA TUG1/miR-30e/NPPB biomarkers

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