ArticleMedical science monitor : international medical journal of experimental and clinical research2019
Development and Validation of Risk Nomogram Model Predicting Coronary Microvascular Obstruction in Patients with ST-Segment Elevation Myocardial Infarction (STEMI) Undergoing Primary Percutaneous Catheterization.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Predicting the no-reflow phenomenon in ST-elevation myocardial infarction patients undergoing primary percutaneous coronary intervention: a systematic review of clinical prediction models.Therapeutic advances in cardiovascular diseasePooled it
- A nomogram model for predicting intramyocardial hemorrhage post-PCI based on SYNTAX score and clinical features.BMC cardiovascular disorders · 2024Trial
- Development of a novel tool: a nomogram for predicting in-hospital mortality of patients in intensive care unit after percutaneous coronary intervention.BMC anesthesiology · 2023Trial
- Genetic Scale for Predicting the No-Reflow Phenomenon in Myocardial Infarction.Sovremennye tekhnologii v meditsine · 2025Article
- Coronary microcirculation dysfunction evaluated by myocardial contrast echocardiography predicts poor prognosis in patients with ST-segment elevation myocardial infarction after percutaneous coronary intervention.BMC cardiovascular disorders · 2022Article
- Development and Validation of a Clinical and Laboratory-Based Nomogram for Predicting Coronary Microvascular Obstruction in NSTEMI Patients After Primary PCI.Therapeutics and clinical risk management · 2022Article
- The Comparison between Two Risk Scores as for the Prediction of Coronary Microvascular Obstruction during Primary Percutaneous Intervention.Arquivos brasileiros de cardiologia · 2021Article
- A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients.BMC infectious diseases · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND Coronary microvascular functional and structural obstruction (CMVO) remains a major complication in patients with ST-segment elevation myocardial infarction (STEMI). This study was designed to develop and validate a nomogram model to predict CMVO risk during primary percutaneous catheterization procedure. MATERIAL AND METHODS Starting January 2014 to December 2016, a cohort of eligible candidates were enrolled and divided into a training or a validation database. Each database was divided into MO or NMO subgroups based on TIMI myocardial perfusion grade results after recanalization. Independent factors were identified by multivariate logistic regression, from which the nomogram was plotted. The echocardiography measurement of the left ventricular ejection fraction (LVEF) was arranged within 7 days after the procedure. RESULTS A nomogram was built for CMVO risk prediction for the first time. There were 446 participants in the training database with 319 cases in the NMO subgroup and 127 participants in the MO subgroup. The validation database included 99 participants with 25 cases in the NMO subgroup and 74 in the MO subgroup. The risk model was developed by 6 independently significant factors: age, symptom onset to balloon time, Killip classification, admission activated clotting time, neutrophil/lymphocyte ratio, and glucose value. Internal receiver operating characteristic displayed favorable performance with concordance index of 0.925, while external validation area under curve was 0.939. There were significant differences in LVEF values during hospitalization between the subgroups of each database (both P<0.001). CONCLUSIONS The nomogram model consisting of 6 factors could predict CMVO risk accurately for STEMI patients undergoing primary percutaneous catheterization.
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