A nomogram for predicting the risk of no-reflow after primary percutaneous coronary intervention in elderly patients with ST-segment elevation myocardial infarction

No-Reflow Prediction in Acute Coronary Syndrome During Percutaneous Coronary Intervention: The NORPACS Risk Score

BACKGROUND

Suboptimal coronary reperfusion (no reflow) is common in acute coronary syndrome percutaneous coronary intervention (PCI) and is associated with poor outcomes. We aimed to develop and externally validate a clinical risk score for angiographic no reflow for use following angiography and before PCI.

METHODS

We developed and externally validated a logistic regression model for prediction of no reflow among adult patients undergoing PCI for acute coronary syndrome using data from the Melbourne Interventional Group PCI registry (2005-2020; development cohort) and the British Cardiovascular Interventional Society PCI registry (2006-2020; external validation cohort).

RESULTS

A total of 30 561 patients (mean age, 64.1 years; 24% women) were included in the Melbourne Interventional Group development cohort and 440 256 patients (mean age, 64.9 years; 27% women) in the British Cardiovascular Interventional Society external validation cohort. The primary outcome (no reflow) occurred in 4.1% (1249 patients) and 9.4% (41 222 patients) of the development and validation cohorts, respectively. From 33 candidate predictor variables, 6 final variables were selected by an adaptive least absolute shrinkage and selection operator regression model for inclusion (cardiogenic shock, ST-segment-elevation myocardial infarction with symptom onset >195 minutes pre-PCI, estimated stent length ≥20 mm, vessel diameter <2.5 mm, pre-PCI Thrombolysis in Myocardial Infarction flow <3, and lesion location). Model discrimination was very good (development C statistic, 0.808; validation C statistic, 0.741) with excellent calibration. Patients with a score of ≥8 points had a 22% and 27% risk of no reflow in the development and validation cohorts, respectively.

CONCLUSIONS

The no-reflow prediction in acute coronary syndrome risk score is a simple count-based scoring system based on 6 parameters available before PCI to predict the risk of no reflow. This score could be useful in guiding preventative treatment and future trials.

Nomograms referenced by cardiac magnetic resonance in the prediction of cardiac injuries in patients with ST-elevation myocardial infarction

BACKGROUND

Evaluation of cardiac injuries is essential in patients with ST-elevation myocardial infarction (STEMI). Cardiac magnetic resonance (CMR) has become the gold standard for quantifying cardiac injuries; however, its routine application is limited. A nomogram is a useful tool for prognostic prediction based on the comprehensive utilization of clinical data. We presumed that the nomogram models established using CMR as a reference could precisely predict cardiac injuries.

METHODS

This analysis included 584 patients with acute STEMI from a CMR registry study for STEMI (NCT03768453). The patients were divided into training (n = 408) and testing (n = 176) datasets. The least absolute shrinkage and selection operator method and multivariate logistic regression were used to construct nomograms for predicting left ventricular ejection fraction (LVEF) ≤40%, infarction size (IS) ≥ 20% on the LV mass, and microvascular dysfunction.

RESULTS

The nomogram for predicting LVEF≤40%, IS≥20%, and microvascular dysfunction comprised 14, 10, and 15 predictors, respectively. With the nomograms, the individual risk probability of developing specific outcomes could be calculated, and the weight of each risk factor was demonstrated. The C-index of the nomograms in the training dataset were 0.901, 0.831, and 0.814, respectively, and were comparable in the testing set, showing good nomogram discrimination and calibration. The decision curve analysis demonstrated good clinical effectiveness. Online calculators were also constructed.

CONCLUSIONS

With the CMR results as the reference standard, the established nomograms demonstrated good effectiveness in predicting cardiac injuries after STEMI and could provide physicians with a new option for individual risk stratification.

A nomogram risk prediction model for no-reflow after primary percutaneous coronary intervention based on rapidly accessible patient data among patients with ST-segment elevation myocardial infarction and its relationship with prognosis

Background

No-reflow occurring after primary percutaneous coronary intervention (PCI) in patients with ST-segment elevation myocardial infarction (STEMI) can increase the incidence of major adverse cardiovascular events (MACE). The present study aimed to construct a nomogram prediction model that can be quickly referred to before surgery to predict the risk for no-reflow after PCI in STEMI patients, and to further explore its prognostic utility in this patient population.

Methods

Research subjects included 443 STEMI patients who underwent primary PCI between February 2018 and February 2021. Rapidly available clinical data obtained from emergency admissions were collected. Independent risk factors for no-reflow were analyzed using a multivariate logistic regression model. Subsequently, a nomogram for no-reflow was constructed and verified using bootstrap resampling. A receiver operating characteristic (ROC) curve was plotted to evaluate the discrimination ability of the nomogram model and a calibration curve was used to assess the concentricity between the model probability curve and ideal curve. Finally, the clinical utility of the model was evaluated using decision curve analysis.

Results

The incidence of no-reflow was 18% among patients with STEMI. Killip class ≥2 on admission, pre-operative D-dimer and fibrinogen levels, and systemic immune–inflammation index (SII) were independent risk factors for no-reflow. A simple and quickly accessible prediction nomogram for no-reflow after PCI was developed. This nomogram demonstrated good discrimination, with an area under the ROC curve of 0.716. This nomogram was further validated using bootstrapping with 1,000 repetitions; the C-index of the bootstrap model was 0.706. Decision curve analysis revealed that this model demonstrated good fit and calibration and positive net benefits. Kaplan–Meier survival curve analysis revealed that patients with higher model scores were at a higher risk of MACE. Multivariate Cox regression analysis revealed that higher model score(s) was an independent predictor of MACE (hazard ratio 2.062; P = 0.004).

Conclusions

A nomogram prediction model that can be quickly referred to before surgery to predict the risk for no-reflow after PCI in STEMI patients was constructed. This novel nomogram may be useful in identifying STEMI patients at higher risk for no-reflow and may predict prognosis in this patient population.

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

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.

Effects of nicorandil on systemic inflammation and oxidative stress induced by percutaneous coronary intervention in patients with coronary heart disease

Objective

The present study investigated the effects of a bolus intracoronary injection of nicorandil on systemic inflammation and oxidative stress induced by percutaneous coronary intervention (PCI) in patients with coronary heart disease (CHD).

Methods

Patients undergoing coronary angiography (CAG) were enrolled into the CAG group (n = 30). Patients undergoing PCI were randomly divided into the PCI (n = 30) and PCI + nicorandil groups (n = 30).

Results

Blood taken from patients in the placebo group 24 hours after PCI exhibited significant increases in the expression of inflammatory indicators and mild increases in the expression of anti-inflammatory indicators. The intracoronary injection of nicorandil reversed the elevation of inflammatory indicators and significantly increased the levels of anti-inflammatory indicators in the blood of patients with PCI. Blood taken from patients in the placebo group 24 hours after PCI also displayed significant decreased superoxide dismutase levels and increased malondialdehyde levels. Nicorandil treatment reversed these changes of oxidative stress marker levels.

Conclusions

These results indicated the possible medical application of intracoronary injections of nicorandil for reducing systemic inflammation and oxidative stress in the peripheral blood of patients undergoing PCI.