Predicting the no-reflow phenomenon in ST-elevation myocardial infarction patients undergoing primary percutaneous coronary intervention: a systematic review of clinical prediction models

• Published in: Therapeutic advances in cardiovascular disease Vol. 18; p. 17539447241290438
• Main Authors: Ebrahimi, Reza, Rahmani, Mahdi, Fallahtafti, Parisa, Ghaseminejad-Raeini, Amirhossein, Azarboo, Alireza, Jalali, Arash, Mehrani, Mehdi
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2024; SAGE PUBLICATIONS, INC; SAGE Publishing
• Subjects: Age Factors; Angioplasty; Cardiovascular disease; Clinical Decision Rules; Clinical Decision-Making - methods; Content analysis; Coronary Circulation; Decision Support Techniques; Heart attacks; Humans; Ischemia; No-Reflow Phenomenon - diagnosis; No-Reflow Phenomenon - etiology; No-Reflow Phenomenon - physiopathology; Percutaneous Coronary Intervention - adverse effects; Predictive Value of Tests; Reproducibility of Results; Risk Assessment; Risk Factors; ST Elevation Myocardial Infarction - diagnosis; ST Elevation Myocardial Infarction - physiopathology; ST Elevation Myocardial Infarction - therapy; Systematic Review; Thrombolytic drugs; Time Factors; Treatment Outcome; STEMI; percutaneous coronary intervention; prediction model; systematic review; no-reflow phenomenon
• ISSN: 1753-9447; 1753-9455; 1753-9455
• DOI: 10.1177/17539447241290438

Background: The no-reflow (NRF) phenomenon is the “Achilles heel” of interventionists after performing percutaneous coronary intervention (PCI) in patients with ST-segment elevation myocardial infarction (STEMI). No definitive treatment has been proposed for NRF, and preventive strategies are central to improving care for patients who develop NRF. Objectives: In this study, we aim to investigate the clinical prediction models developed to predict NRF in STEMI patients undergoing primary PCI. Design: Systematic review. Data sources and methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were observed. Studies that developed clinical prediction modeling for NRF after primary PCI in STEMI patients were included. Data extraction was performed using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist. The Prediction Model Risk of Bias Assessment Tool (PROBAST) tool was used for critical appraisal of the included studies. Results: The three most common predictors were age, total ischemic time, and preoperative thrombolysis in myocardial infarction flow grade. Most of the included studies internally validated their developed model via various methods: random split, bootstrapping, and cross-validation. Only three studies (18%) externally validated their model. Six studies (37%) reported a calibration plot with or without the Hosmer–Lemeshow test. The reported area under the curve ranged from 0.648 to 0.925. The most common biases were in the statistical domain. Conclusion: Clinical prediction models aid in individualizing care for STEMI patients with NRF after primary PCI. Of the 16 included studies, we report four to have a low risk of bias and low concern with regard to our research question, which should undergo external validation with or without updating in future studies. Jargon summary Introduction: Heart attacks, or acute myocardial infarctions (MI), are severe consequences of coronary artery disease. One type, ST-segment elevation myocardial infarction (STEMI), requires prompt treatment to restore blood flow and prevent severe complications. The preferred treatment is primary percutaneous coronary intervention (PCI). However, sometimes blood flow remains poor despite successful PCI, a condition known as the no-reflow (NRF) phenomenon, which can lead to worse outcomes. Study Goal: This study reviews and evaluates existing models that predict NRF in STEMI patients undergoing PCI to help doctors identify and prevent this complication. Methods: We systematically searched databases for studies on NRF prediction models in STEMI patients. We included studies that developed these models and evaluated their risk of bias and applicability. Key Findings: - Search Results: Out of 7,095 citations, 16 studies were reviewed. - Study Locations: Studies were mainly conducted in China, Turkey, and a few other countries. - Predictors: Common predictors of NRF included age, total ischemic time, and preoperative blood flow. The models used various data like patient demographics, lab results, and clinical observations. - Model Performance: Models showed varying levels of accuracy in predicting NRF. Most models used logistic regression for development. Internal validation was done in several studies, but external validation was limited. Implications: - Clinical Use: Predictive models can help in timely decision-making during PCI. However, current models need more validation and improvement in their design to be widely accepted in clinical practice. - Future Research: More robust, prospective studies are needed, especially in diverse populations, to develop and validate better prediction models. Conclusion: This review highlights the need for better predictive models for NRF in STEMI patients. Existing models show promise but require further refinement and validation to improve their clinical utility.