Prognosis Impact and Prediction of Trans-Radial Access Failure in Patients With STEMI, A Nationwide Observational Study

Trans-radial access (TRA) is the primary arterial approach for percutaneous coronary intervention (PCI) in ST-elevation myocardial infarction (STEMI). However, occasionally, a crossover to trans-femoral access is necessary because of unsuccessful TRA. The impact of failed TRA on the prognosis in STE...

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Published in:The American journal of cardiology 2024-06, Vol.220, p.23-32
Main Authors: Tornyos, Dániel, Lukács, Réka, Jánosi, András, Komócsi, András
Format: Article
Language:English
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access site
Algorithms
Balloon treatment
Cardiovascular diseases
Clinical outcomes
Comparative analysis
Confidence intervals
coronary intervention
Decision making
Deep learning
Discriminant analysis
failed trans-radial
Failure
Generalized linear models
Heart attacks
Machine learning
Matching
Medical prognosis
Mortality
Mortality risk
Myocardial infarction
Observational studies
Patients
Prediction models
Prognosis
Regression analysis
Risk analysis
Sensitivity
Statistical analysis
STEMI
trans-radial
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Lukács, Réka
Jánosi, András
Komócsi, András
description Trans-radial access (TRA) is the primary arterial approach for percutaneous coronary intervention (PCI) in ST-elevation myocardial infarction (STEMI). However, occasionally, a crossover to trans-femoral access is necessary because of unsuccessful TRA. The impact of failed TRA on the prognosis in STEMI patients and the utility of predictive models for TRA failure remains uncertain. Data from the Hungarian Myocardial Infarction Registry (January 2014 to December 2020) were analyzed. Primary endpoints were 1-year mortality and major adverse cardiovascular events. Propensity score matching was employed to create a balanced cohort for comparing successful and failed TRA. The impact of unsuccessful TRA on prognosis was evaluated using Cox regression analysis. Machine learning techniques were applied to predict TRA failure. The performance and the clinical applicability of the novel and previous prediction models were comprehensively evaluated. Of 76,625 registered patients, 34,293 (69.8 ± 13.4 years, male/female: 21,893/12,400) underwent TRA (33,573) or failed TRA (720) PCI for STEMI. After propensity score matching, in the unsuccessful TRA group, the risk of mortality (34.3% vs 22.5%, hazard ratio 1.6, 95% confidence interval 1.3 to 2.0, p
doi_str_mv 10.1016/j.amjcard.2024.03.016
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However, occasionally, a crossover to trans-femoral access is necessary because of unsuccessful TRA. The impact of failed TRA on the prognosis in STEMI patients and the utility of predictive models for TRA failure remains uncertain. Data from the Hungarian Myocardial Infarction Registry (January 2014 to December 2020) were analyzed. Primary endpoints were 1-year mortality and major adverse cardiovascular events. Propensity score matching was employed to create a balanced cohort for comparing successful and failed TRA. The impact of unsuccessful TRA on prognosis was evaluated using Cox regression analysis. Machine learning techniques were applied to predict TRA failure. The performance and the clinical applicability of the novel and previous prediction models were comprehensively evaluated. Of 76,625 registered patients, 34,293 (69.8 ± 13.4 years, male/female: 21,893/12,400) underwent TRA (33,573) or failed TRA (720) PCI for STEMI. After propensity score matching, in the unsuccessful TRA group, the risk of mortality (34.3% vs 22.5%, hazard ratio 1.6, 95% confidence interval 1.3 to 2.0, p &lt;0.001) and major adverse cardiovascular events (37.4% vs 26.8%, hazard ratio 1.5, 95% confidence interval 1.3 to 1.8, p &lt;0.001) were significantly higher. Door-to-balloon time did not differ significantly (p = 0.835). In predictive analysis, Regularized Discriminant Analysis emerged as the most promising model, surpassing previous prediction models (area under the curve: 0.66, sensitivity: 0.32, specificity: 0.86). Nevertheless, Global Registry of Acute Coronary Events (GRACE) 2.0 score demonstrated a remarkable performance (area under the curve: 0.65, sensitivity: 0.51, specificity: 0.73). This study underscores the pivotal role of successful TRA in enhancing outcomes in STEMI cases, advocating for its prioritization. The inability to conclude interventions through this approach is linked to a poorer prognosis, even in risk-adjusted analyses. Our findings indicate that prediction models utilizing clinical parameters do not outperform the established GRACE 2.0 algorithm, questioning their utility. In conclusion, the results emphasize the significance of TRA success and the continued relevance of the GRACE score in clinical decision-making to optimize patient outcomes. 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After propensity score matching, in the unsuccessful TRA group, the risk of mortality (34.3% vs 22.5%, hazard ratio 1.6, 95% confidence interval 1.3 to 2.0, p &lt;0.001) and major adverse cardiovascular events (37.4% vs 26.8%, hazard ratio 1.5, 95% confidence interval 1.3 to 1.8, p &lt;0.001) were significantly higher. Door-to-balloon time did not differ significantly (p = 0.835). In predictive analysis, Regularized Discriminant Analysis emerged as the most promising model, surpassing previous prediction models (area under the curve: 0.66, sensitivity: 0.32, specificity: 0.86). Nevertheless, Global Registry of Acute Coronary Events (GRACE) 2.0 score demonstrated a remarkable performance (area under the curve: 0.65, sensitivity: 0.51, specificity: 0.73). This study underscores the pivotal role of successful TRA in enhancing outcomes in STEMI cases, advocating for its prioritization. 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After propensity score matching, in the unsuccessful TRA group, the risk of mortality (34.3% vs 22.5%, hazard ratio 1.6, 95% confidence interval 1.3 to 2.0, p &lt;0.001) and major adverse cardiovascular events (37.4% vs 26.8%, hazard ratio 1.5, 95% confidence interval 1.3 to 1.8, p &lt;0.001) were significantly higher. Door-to-balloon time did not differ significantly (p = 0.835). In predictive analysis, Regularized Discriminant Analysis emerged as the most promising model, surpassing previous prediction models (area under the curve: 0.66, sensitivity: 0.32, specificity: 0.86). Nevertheless, Global Registry of Acute Coronary Events (GRACE) 2.0 score demonstrated a remarkable performance (area under the curve: 0.65, sensitivity: 0.51, specificity: 0.73). This study underscores the pivotal role of successful TRA in enhancing outcomes in STEMI cases, advocating for its prioritization. The inability to conclude interventions through this approach is linked to a poorer prognosis, even in risk-adjusted analyses. Our findings indicate that prediction models utilizing clinical parameters do not outperform the established GRACE 2.0 algorithm, questioning their utility. In conclusion, the results emphasize the significance of TRA success and the continued relevance of the GRACE score in clinical decision-making to optimize patient outcomes. [Display omitted]</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>38521231</pmid><doi>10.1016/j.amjcard.2024.03.016</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-1406-4016</orcidid><oa>free_for_read</oa></addata></record>
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subjects access site
Algorithms
Balloon treatment
Cardiovascular diseases
Clinical outcomes
Comparative analysis
Confidence intervals
coronary intervention
Decision making
Deep learning
Discriminant analysis
failed trans-radial
Failure
Generalized linear models
Heart attacks
Machine learning
Matching
Medical prognosis
Mortality
Mortality risk
Myocardial infarction
Observational studies
Patients
Prediction models
Prognosis
Regression analysis
Risk analysis
Sensitivity
Statistical analysis
STEMI
trans-radial
title Prognosis Impact and Prediction of Trans-Radial Access Failure in Patients With STEMI, A Nationwide Observational Study
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