Citations

AbstractObjectivesTo investigate the perceptions of interventional cardiologists (IC) regarding the frequency, impact, and management strategies of percutaneous coronary intervention (PCI) complications.BackgroundThe perceptions and management strategies of ICs of PCI complications have received limited study.MethodsOnline survey on PCI complications: 46 questions were distributed via email lists and Twitter to ICs.ResultsOf 11,663 contacts, 821 responded (7% response rate): 60% were from the United States and the median age was 46–50 years. Annual PCI case numbers were <100 (26%), 100–199 (37%), 200–299 (21%), and ≥300 (16%); 42% do not perform structural interventions, others reported performing <40 (30%), or >100 (11%) structural cases annually. On a scale of 0–10, participating ICs were highly concerned about potential complications with a median score of 7.2 (interquartile range: 5.0–8.7). The most feared complication was death (39%), followed by coronary perforation (26%) and stroke (9%). Covered stents were never deployed by 21%, and 32% deployed at least one during the past year; 79% have never used fat to seal perforations; 64% have never used coils for perforations. Complications were attributed to higher patient/angiographic complexity by 68% and seen as opportunities for improvement by 70%; 97% of participants were interested in learning more about the management of PCI complications. The most useful learning methods were meetings (66%), webinars (48%), YouTube (32%), and Twitter (29%).ConclusionICs who participated in the survey are highly concerned about complications. Following complication management algorithms and having access to more experienced operators might alleviate stress and optimize patient outcomes.

AbstractCardiovascular disease (CVD) remains the leading cause of death in the United States. However, percutaneous interventional cardiovascular therapies are often underutilized in Blacks, Hispanics, and women and may contribute to excess morbidity and mortality in these vulnerable populations. The Society for Cardiovascular Angiography and Interventions (SCAI) is committed to reducing racial, ethnic, and sex‐based treatment disparities in interventional cardiology patients. Accordingly, each of the SCAI Clinical Interest Councils (coronary, peripheral, structural, and congenital heart disease [CHD]) participated in the development of this whitepaper addressing disparities in diagnosis, treatment, and outcomes in underserved populations. The councils were charged with summarizing the available data on prevalence, treatment, and outcomes and elucidating potential reasons for any disparities. Given the huge changes in racial and ethnic composition by age in the United States (Figure 1), it was difficult to determine disparities in rates of diagnosis and we expected to find some racial differences in prevalence of disease. For example, since the average age of patients undergoing transcatheter aortic valve replacement (TAVR) is 80 years, one may expect 80% of TAVR patients to be non‐Hispanic White. Conversely, only 50% of congenital heart interventions would be expected to be performed in non‐Hispanic Whites. Finally, we identified opportunities for SCAI to advance clinical care and equity for our patients, regardless of sex, ethnicity, or race.

Abstract Background Limited data exist on high-sensitivity cardiac troponin (hs-cTn) for risk-stratification in COVID-19. Methods We conducted a multicenter, retrospective, observational, US-based study of COVID-19 patients undergoing hs-cTnT. Outcomes included short-term mortality (in-hospital and 30-days post-discharge) and a composite of major adverse events, including respiratory failure requiring mechanical ventilation, cardiac arrest, and shock within the index presentation and/or mortality during the index hospitalization or within 30-days post-discharge. Results Among 367 COVID-19 patients undergoing hs-cTnT, myocardial injury was identified in 46%. They had a higher risk for mortality (20% vs 12%, P &lt; 0.0001; unadjusted HR 4.44, 95% CI 2.13–9.25, P &lt; 0.001) and major adverse events (35% vs. 11%, P &lt; 0.0001; unadjusted OR 4.29, 95% CI 2.50–7.40, P &lt; 0.0001). Myocardial injury was associated with major adverse events (adjusted OR 3.84, 95% CI 2.00–7.36, P &lt; 0.0001) but not mortality. Baseline (adjusted OR 1.003, 95% CI 1.00–1.007, P = 0.047) and maximum (adjusted OR 1.005, 95% CI 1.001–1.009, P = 0.0012) hs-cTnT were independent predictors of major adverse events. Most (95%) increases were due to myocardial injury, with 5% (n = 8) classified as type 1 or 2 myocardial infarction. A single hs-cTnT &lt;6 ng/L identified 26% of patients without mortality, with a 94.9% (95% CI 87.5–98.6) negative predictive value and 93.1% sensitivity (95% CI 83.3–98.1) for major adverse events in those presenting to the ED. Conclusions Myocardial injury is frequent and prognostic in COVID-19. While most hs-cTnT increases are modest and due to myocardial injury, they have important prognostic implications. A single hs-cTnT &lt;6 ng/L at presentation may facilitate the identification of patients with a favorable prognosis.

AbstractDespite the widespread adoption of primary percutaneous intervention and modern antithrombotic therapy, ST-segment elevation myocardial infarction (STEMI) remains the leading cause of death in the United States and remains one of the most important causes of morbidity and mortality worldwide. Certain high-risk patients present a challenge for diagnosis and treatment. The widespread adoption of primary percutaneous intervention in addition to modern antithrombotic therapy has resulted in substantial improvement in the short- and long-term prognosis following STEMI. In this review, we aim to provide a brief analysis of the state-of-the-art treatment for patients presenting with STEMI, focusing on cardiogenic shock, current treatment and controversies, cardiac arrest, and diagnosis and treatment of mechanical complications, as well as multivessel and left main-related STEMI.

Background: Myocardial injury results in release of cardiac troponin (cTn) into the bloodstream, readily detected by high-sensitivity cTn (hs-cTn) assays. A noninvasive, rapid, home-based test to exclude acute myocardial injury at the time of the test and for the next 7 hours would help manage patients with suspected myocardial infarction. Since myocyte injury is associated with ECG changes, we hypothesized an artificial intelligence ECG (AI-ECG) could identify absence of injury. Objective: To train and test an AI-ECG convolutional neural network (CNN) to identify patients using a single ECG who are suspected of myocardial infarction who are low risk, with hs-cTn levels below the 99 th percentile at test time and for the subsequent 7 hours. Methods: A CNN tuned to identify the absence of a hs-cTnT (5 th Gen cTnT Roche Diagnostics) >15ng/L for men and >10ng/L for women was developed. All ECGs were recorded within one hour of the hs-cTnT assay. We used 73,012 ECGs and hs-cTnT pairs from 47,542 unique patients to train the network, 9031 ECGs from 5,811 patients for internal validation to optimize hyperparameters, and 11,904 ECGs with 21,191 hs-cTnT measurements up to 7 hours after the ECG, from 11,904 different patients as a holdout test set. Results: The mean age was 63.9±17.5 years, and 30,348 of the 59,446 patients (51%) were male. 5,852 patients (49.1%) had no elevation of hs-cTnT and 6,052 (50.9%) had an hs-cTnT above the 99 th percentile at baseline or within 7 hours of the test. Of the 11,904 patients in the test set, using a sensitive threshold, the 12 lead AI ECG identified 1037 patients (8.7%) likely to have a low risk for subsequent hs-cTnT increases > 99 th % (AUC 0.86), and the single lead ECG identified 685 patients. Of the 1037 low risk pts, 59 had an hs-cTnT > 99 th percentile within 7 hours. The mean maximum hs-cTnT among the 59 low-risk patients was 53 ng/L ± 92 vs 184 ng/L ± 1474 in the others. None of these low risk patients died within 14 days of the test. Conclusion: Use of the AI ECG to identify patients without cardiac injury at the time of the test and for 7 subsequent hours is feasible. This may permit rapid, point of care, home-based, bloodless, painless testing to exclude cardiac injury, facilitating care and management of patients with suspected ischemia.

Background: High-sensitivity cardiac troponin (hs-cTn) assays quantify cTn in patients at very low concentrations. Myocyte injury due to ischemia or other pathologies cause blood levels to increase, which is prognostic. A noninvasive, rapid, broadly available, home-based test to detect hs-cTn increases would facilitate risk-stratification. Since myocyte injury is associated with ECG changes, we hypothesized an artificial intelligence ECG (AI-ECG) could non-invasively predict current or impending hs-cTnT elevations. Objective: To develop an AI-ECG convolutional neural network (CNN) to detect an abnormal hs-cTnT (5 th Gen cTnT, Roche Diagnostics) concentration using a 12-lead ECG, and a single lead ECG (lead I), which would enable smartphone, home-based detection. Methods: We developed single lead and 12-lead ECG CNNs to detect a) hs-cTnT concentrations that were at or above the 6ng/L limit that can be reported b) above the 99 th percentile upper limits of >15ng/L for men and >10ng/L for women. All ECGs were recorded within one hour of the hs-cTnT measurements. We used 73,012 ECG and hs-cTnT pairs from 47,542 unique patients to train the network, 9031 ECGs from 5,811 patients for internal validation to optimize hyperparameters, and 18,276 ECG and hs-cTnT pairs from 11,904 different patients as a holdout test set to determine the area under the receiver-operator curve (AUC). Results: The mean age was 63.9±17.5 years, and 30,348 of the 59,446 patients (51%) were male. Of the 91,288 hs-cTnT pairs 73,271 (80.2%) were above 6ng/L and 50,799 (55.6%) are above the 99 th percentile. In the test set, the AUC for the detection of a hs-cTnT level higher than 6ng/L was 0.88 using the 12 lead ECG and 0.834 with the single lead. For the detection of hs-cTnT level above of 99 th percentile, the 12 lead ECG AUC was 0.853 and the single lead was 0.806. Conclusion: The AI-ECG permits detection of levels of hs-cTnT consistent with myocardial injury. This may allow a home-based, non-invasive test that would be massively scalable and could further enhance rapid-risk stratification and patient triage with potentially significant cost reductions and enable novel triage strategies at sites without hs-cTn assays.