PATIENTS AND METHODS: Materials and methods: The study involved 134 ST-segment elevation myocardial infarction patients. Occurrence of post-percutaneous coronary (PCI) intervention epicardial blood flow of TIMI <3 or myocardial blush grade 0-1 along with ST resolution <70% within 2 hours after PCI was qualified as the no-reflow condition. Left ventricle remodeling was defined after 6-months as an increase in left ventricle end-diastolic volume and/or end-systolic volume by more than 10%.
RESULTS: Results: A logistic regression formula was evaluated. Included biomarkers were macrophage migration inhibitory factor and sST2, left ventricle ejection fraction: Y=exp(-39.06+0.82EF+0.096ST2+0.0028MIF) / (1+exp(-39.06+0.82EF+0.096ST2+0.0028MIF)). The estimated range is from 0 to 1 point. Less than 0.5 determines an adverse outcome, and more than 0.5 is a good prognosis. This equation, with sensitivity of 77 % and specificity of 85%, could predict the development of adverse left ventricle remodeling six months after a coronary event (AUC=0.864, CI 0.673 to 0.966, p<0.05).
CONCLUSION: Conclusions: A combination of biomarkers gives a significant predicting result in the formation of adverse left ventricular remodeling after ST-segment elevation myocardial infarction.
OBJECTIVE: The objective of this study is to collate the prognosis, symptomatology and clinical findings of COVID-19 adverse events causing STEMI.
METHODS: Databases were queried with various keyword combinations to find applicable articles. Cardiovascular risk factors, symptomatology, mortality and rates of PCI were analyzed using random-effect model.
RESULTS: 15 studies with a total of 379 patients were included in the final analysis. Mean age of patients was 62.82 ± 36.01, with a male predominance (72%, n = 274). Hypertension, dyslipidemia and diabetes mellitus were the most common cardiovascular risk factors among these patients, with a pooled proportion of 72%, 59% and 40% respectively. Dyspnea (61%, n = 131) was the most frequent presenting symptom, followed by chest pain (60%, n = 101) and fever (56%, n = 104). 62% of the patients had obstructive CAD during coronary angiography. The primary reperfusion method used in the majority of cases was percutaneous coronary intervention (64%, n = 124). Mortality, which is the primary outcome in our study, was relatively high, with a rate of 34% across studies.
CONCLUSION: Our findings show that most cases have been found in males, while the most common risk factors were Hypertension and Diabetes Mellitus. In most COVID-19 cases with ST-segment myocardial infarction, most hospitalized patients underwent primary percutaneous coronary intervention instead of fibrinolysis. The in-hospital mortality was significantly higher, making this report significant. As the sample size and reported study are considerably less, it warrants a further large-scale investigation to generalize it.
AIMS: This study aimed to examine the influence of vessel volume on bolus thermodilution measurements.
METHODS: We prospectively included patients with angina with non-obstructive coronary arteries (ANOCA) undergoing bolus and continuous thermodilution assessments. All patients underwent coronary CT angiography to extract vessel volume. Coronary microvascular dysfunction was defined as coronary flow reserve (CFR)
OBJECTIVES: To validate the accuracy of virtual FFR pullback curves compared to wire-based FFR pullbacks and to assess their clinical utility using patient-reported outcomes.
METHODS: Pooled analysis of two prospective studies, including patients with hemodynamically significant (FFR ≤ 0.80) coronary artery disease (CAD). Virtual and wire-based FFR pullbacks were compared to assess the accuracy of virtual pullbacks to characterize CAD as focal or diffuse. Pullbacks were analyzed visually and quantitatively using the pullback pressure gradient (PPG). Patients underwent PCI, and the Seattle Angina Questionnaire (SAQ) was administered at 3-month follow-up.
RESULTS: A total of 298 patients (300 vessels) with both virtual and wire-based pullbacks who underwent PCI were included in the analysis. The mean age was 61.8 ± 8.8, and 15% were female. The agreement on the visual adjudication of the CAD pattern was fair (Cohen's Kappa: 0.31, 95% confidence interval: 0.18-0.45). The mean PPG were 0.65 ± 0.18 from virtual pullbacks and 0.65 ± 0.13 from wire-based pullbacks (r = 0.68, mean difference 0, limits of agreement -0.27 to 0.28). At follow-up, patients with high virtual PPG (>0.67) had higher SAQ angina frequency scores (i.e., less angina) than those with low virtual PPG (SAQ scores 92.0 ± 14.3 vs. 85.5 ± 23.1, p = 0.022).
CONCLUSION: Virtual FFR pullback curves showed moderate agreement with wire-based FFR pullbacks. Nonetheless, patients with focal disease based on virtual PPG reported greater improvement in angina after PCI.
AIM: The primary objective is to determine the predictive capacity of the PPG for post-PCI FFR.
METHODS: This prospective, large-scale, controlled, investigator-initiated, multicenter study is enrolling patients with at least 1 lesion in a major epicardial vessel with a distal FFR ≤ 0.80 intended to be treated by PCI. The study will include 982 subjects. A standardized physiological assessment will be performed pre-PCI, including the online calculation of PPG from FFR pullbacks performed manually. PPG quantifies the CAD pattern by combining several parameters from the FFR pullback curve. Post-PCI physiology will be recorded using a standardized protocol with FFR pullbacks. We hypothesize that PPG will predict optimal PCI results (post-PCI FFR ≥ 0.88) with an area under the ROC curve (AUC) ≥ 0.80. Secondary objectives include patient-reported and clinical outcomes in patients with focal vs. diffuse CAD defined by the PPG. Clinical follow-up will be collected for up to 36 months, and an independent clinical event committee will adjudicate events.
RESULTS: Recruitment is ongoing and is expected to be completed in the second half of 2023.
CONCLUSION: This international, large-scale, prospective study with pre-specified powered hypotheses will determine the ability of the preprocedural PPG index to predict optimal revascularization assessed by post-PCI FFR. In addition, it will evaluate the impact of PPG on treatment decisions and the predictive performance of PPG for angina relief and clinical outcomes.
METHODS: This prospective, investigator-initiated, single-arm, multicenter study enrolled patients with at least one epicardial lesion with an FFR ≤0.80 scheduled for PCI. Manual FFR pullbacks were used to calculate PPG. The primary outcome of optimal revascularization was defined as an FFR ≥0.88 after PCI.
RESULTS: A total of 993 patients with 1044 vessels were included. The mean FFR was 0.68±0.12, PPG 0.62±0.17, and the post-PCI FFR was 0.87±0.07. PPG was significantly correlated with the change in FFR after PCI (r=0.65 [95% CI, 0.61-0.69]; P<0.001) and demonstrated excellent predictive capacity for optimal revascularization (area under the receiver operating characteristic curve, 0.82 [95% CI, 0.79-0.84]; P<0.001). FFR alone did not predict revascularization outcomes (area under the receiver operating characteristic curve, 0.54 [95% CI, 0.50-0.57]). PPG influenced treatment decisions in 14% of patients, redirecting them from PCI to alternative treatment modalities. Periprocedural myocardial infarction occurred more frequently in patients with low PPG (<0.62) compared with those with focal disease (odds ratio, 1.71 [95% CI, 1.00-2.97]).
CONCLUSIONS: Pathophysiologic coronary artery disease patterns distinctly affect the safety and effectiveness of PCI. PPG showed an excellent predictive capacity for optimal revascularization and demonstrated added value compared with an FFR measurement.
REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04789317.