Angiographic characteristics of patients with STEMI and COVID-19: Insights from NACMI registry.

BACKGROUND: To date, there has been no independent core lab angiographic analysis of patients with COVID-19 and STEMI. The study characterized the angiographic parameters of patients with COVID-19 and STEMI. METHODS: Angiograms of patients with COVID-19 and STEMI from the North American COVID-19 Myocardial Infarction (NACMI) Registry were sent to a Core Laboratory in Vancouver, Canada. Culprit lesion(s), Thrombolysis In Myocardial Infarction (TIMI) flow, Thrombus Grade Burden (TGB), and percutaneous coronary intervention (PCI) outcome were assessed. RESULTS: From 234 patients, 74% had one culprit lesion, 14% had multiple culprits and 12% had no culprit identified. Multivessel thrombotic disease and multivessel CAD were found in 27% and 53% of patients, respectively. Stent thrombosis accounted for 12% of the presentations and occurred in 55% of patients with previous coronary stents. Of the 182 who underwent PCI, 60 (33%) had unsuccessful PCI due to post-PCI TIMI flow <3 (43/60), residual high thrombus burden (41/60) and/or thrombus related complications (27/60). In-hospital mortality for successful, partially successful, and unsuccessful PCI was 14%, 13%, and 27%, respectively. Unsuccessful PCI was associated with increased risk of in-hospital mortality (risk ratio [RR] 1.96; 95% CI: 1.05-3.66, P = .03); in the adjusted model this estimate was attenuated (RR: 1.24; 95% CI: 0.65-2.34, P = .51). CONCLUSION: In patients with COVID-19 and STEMI, thrombus burden was pervasive with notable rates of multivessel thrombotic disease and stent thrombosis. Post-PCI, persistent thrombus and sub-optimal TIMI 3 flow rates led to one-third of the PCI's being unsuccessful, which decreased over time but remained an important predictor of in-hospital mortality.

Increased disparities in waitlist and post-heart transplantation outcomes according to socioeconomic status with the new heart transplant allocation system.

BACKGROUND: The study objective was to assess disparities in outcomes in the waitlist and post-heart transplantation (HT) according to socioeconomic status (SES) in the old and new U.S. HT allocation systems. METHODS: Adult HT candidates in the United Network for Organ Sharing database from 2014 through 2021 were included. Old or new system classification was according to listing before or after October 18, 2018. SES was stratified by patient ZIP code and median household income via U.S. Census Bureau and classified into terciles. Competing waitlist outcomes and post-transplantation survival were compared between systems. RESULTS: In total, 26,450 patients were included. Waitlisted candidates with low SES were more frequently younger, female, African American, and with higher body mass index. Reduced cumulative incidence (CI) of HT in the old system occurred in low SES (53.5%) compared to middle (55.7%, p = 0.046), and high (57.9%, p < 0.001). In the new system, the CI of HT was 65.3% in the low SES vs middle (67.6%, p = 0.002) and high (70.2%, p < 0.001), and SES remained significant in the adjusted analysis. In the old system, CI of death/delisting was similar across SES. In the new system, low SES had increased CI of death/delisting (7.4%) vs middle (6%, p = 0.012) and high (5.4%, p = 0.002). The old system showed similar 1-year survival across SES. In the new system, recipients with low SES had decreased 1-year survival (p = 0.041). CONCLUSIONS: SES affects waitlist and post-transplant outcomes. In the new system, all SES had increased access to HT; however, low SES had increased death/delisting due to worsening clinical status and decreased post-transplant survival.

Predicting Survival of End-Stage Heart Failure Patients Receiving HeartMate-3: Comparing Machine Learning Methods.

HeartMate 3 is the only durable left ventricular assist devices (LVAD) currently implanted in the United States. The purpose of this study was to develop a predictive model for 1 year mortality of HeartMate 3 implanted patients, comparing standard statistical techniques and machine learning algorithms. Adult patients registered in the Society of Thoracic Surgeons, Interagency Registry for Mechanically Assisted Circulatory Support (STS-INTERMACS) database, who received primary implant with a HeartMate 3 between January 1, 2017, and December 31, 2019, were included. Epidemiological, clinical, hemodynamic, and echocardiographic characteristics were analyzed. Standard logistic regression and machine learning (elastic net and neural network) were used to predict 1 year survival. A total of 3,853 patients were included. Of these, 493 (12.8%) died within 1 year after implantation. Standard logistic regression identified age, Model End Stage Liver Disease (MELD)-XI score, right arterial (RA) pressure, INTERMACS profile, heart rate, and etiology of heart failure (HF), as important predictor factors for 1 year mortality with an area under the curve (AUC): 0.72 (0.66-0.77). This predictive model was noninferior to the ones developed using the elastic net or neural network. Standard statistical techniques were noninferior to neural networks and elastic net in predicting 1 year survival after HeartMate 3 implantation. The benefit of using machine-learning algorithms in the prediction of outcomes may depend on the type of dataset used for analysis.

Aortic valve disorders and left ventricular assist devices.

Aortic valve disorders are important considerations in advanced heart failure patients being evaluated for left ventricular assist devices (LVAD) and those on LVAD support. Aortic insufficiency (AI) can be present prior to LVAD implantation or develop de novo during LVAD support. It is usually a progressive disorder and can lead to impaired LVAD effectiveness and heart failure symptoms. Severe AI is associated with worsening hemodynamics, increased hospitalizations, and decreased survival in LVAD patients. Diagnosis is made with echocardiographic, device assessment, and/or catheterization studies. Standard echocardiographic criteria for AI are insufficient for accurate diagnosis of AI severity. Management of pre-existing AI includes aortic repair or replacement at the time of LVAD implant. Management of de novo AI on LVAD support is challenging with increased risks of repeat surgical intervention, and percutaneous techniques including transcatheter aortic valve replacement are assuming greater importance. In this manuscript, we provide a comprehensive approach to contemporary diagnosis and management of aortic valve disorders in the setting of LVAD therapy.

Extracorporeal membrane oxygenation as a bridge to advanced heart failure therapies.

BACKGROUND: Venoarterial extracorporeal membrane oxygenation (VA-ECMO) is a key support modality for cardiogenic shock. The 2018 United Network for Organ Sharing (UNOS) heart transplant allocation algorithm prioritizes VA-ECMO patients. OBJECTIVE: To evaluate the role of VA-ECMO in bridging to advanced heart failure therapies. METHODS: We analyzed adult patients from the multicenter Extracorporeal Life Support Organization registry receiving VA-ECMO for cardiac support or resuscitation between 2016 and 2021 in the United States, comparing bridge-to-transplant (BTT) and non-BTT intent patients, as well as pre- vs post-2018 patients, on a wide range of demographic and clinical outcome predictors. RESULTS: Of 17,087 patients, 797 received left ventricular assist device (LVAD)/heart transplant, 7,931 died or had poor prognosis, and 8,359 had expected recovery at ECMO discontinuation. Patients supported with BTT intent had lower clinical acuity than non-BTT candidates and were more likely to receive LVAD/transplant. The proportion of patients who received VA-ECMO as BTT and received LVAD/transplant increased after 2018. Post-2018 BTT patients had significantly lower clinical acuity and higher likelihood of transplant than both post-2018 non-BTT patients and pre-2018 BTT patients. ECMO complications were associated with lower likelihood of transplant but were significantly less common post-2018 than pre-2018. CONCLUSIONS: After implementation of the 2018 UNOS allocation system, ECMO utilization as BTT or LVAD has increased, and the acuity of BTT intent patients cannulated for ECMO has decreased. There has not yet been an increase in more acute ECMO patients getting transplanted. This may partially explain the post-transplant outcomes of ECMO patients in the current era reported in UNOS.

Mechanical Circulatory Support in Patients With COVID-19 Presenting With Myocardial Infarction.

ST-segment elevation myocardial infarction (STEMI) complicating COVID-19 is associated with an increased risk of cardiogenic shock and mortality. However, little is known about the frequency of use and clinical impact of mechanical circulatory support (MCS) in these patients. We sought to define patterns of MCS utilization, patient characteristics, and outcomes in patients with COVID-19 with STEMI. The NACMI (North American COVID-19 Myocardial Infarction) is an ongoing prospective, observational registry of patients with COVID-19 positive (COVID-19+) with STEMI with a contemporary control group of persons under investigation who subsequently tested negative for COVID-19 (COVID-19-). We compared the baseline characteristics and in-hospital outcomes of COVID-19+ and patients with COVID-19- according to the use of MCS. The primary outcome was a composite of in-hospital mortality, stroke, recurrent MI, and repeat unplanned revascularization. A total of 1,379 patients (586 COVID-19+ and 793 COVID-19-) enrolled in the NACMI registry between January 2020 and November 2021 were included in this analysis; overall, MCS use was 12.3% (12.1% [n = 71] COVID-19+/MCS positive [MCS+] vs 12.4% [n = 98] COVID-19-/MCS+). Baseline characteristics were similar between the 2 groups. The use of percutaneous coronary intervention was similar between the groups (84% vs 78%; p = 0.404). Intra-aortic balloon pump was the most frequently used MCS device in both groups (53% in COVID-19+/MCS+ and 75% in COVID-19-/MCS+). The primary outcome was significantly higher in COVID-19+/MCS+ patients (60% vs 30%; p = 0.001) because of very high in-hospital mortality (59% vs 28%; p = 0.001). In conclusion, patients with COVID-19+ with STEMI requiring MCS have very high in-hospital mortality, likely related to the significantly higher pulmonary involvement compared with patients with COVID-19- with STEMI requiring MCS.

Persistent or Recurrent Angina Following Percutaneous Coronary Revascularization.

PURPOSE OF REVIEW: Persistent or recurrent angina after percutaneous coronary intervention (PCI) has substantial patient morbidity and economic impact. As knowledge of the pathophysiology of this condition has evolved, new tools for accurate diagnosis and treatment have become available. We provide a current, comprehensive review of mechanisms of post-PCI angina, diagnostic strategies, and therapeutic options. RECENT FINDINGS: The routine use of functional testing during PCI may enable more accurate revascularization. Coronary vasomotor disorders commonly cause angina after PCI in the absence of obstructive epicardial CAD. Invasive coronary vasoreactivity testing can enable phenotype-guided therapy of coronary vasomotor disorders with improvement in angina. Multiple nonpharmacologic modalities to treat refractory angina are under development. A comprehensive approach to the diagnosis of persistent or recurrent angina after PCI with noninvasive and invasive techniques is required. An individualized, phenotype-guided management using lifestyle, pharmacologic, and nonpharmacologic modalities is necessary to optimize outcomes.

Early Prediction of Cardiogenic Shock Using Machine Learning.

Cardiogenic shock (CS) is a severe condition with in-hospital mortality of up to 50%. Patients who develop CS may have previous cardiac history, but that may not always be the case, adding to the challenges in optimally identifying and managing these patients. Patients may present to a medical facility with CS or develop CS while in the emergency department (ED), in a general inpatient ward (WARD) or in the critical care unit (CC). While different clinical pathways for management exist once CS is recognized, there are challenges in identifying the patients in a timely manner, in all settings, in a timeframe that will allow proper management. We therefore developed and evaluated retrospectively a machine learning model based on the XGBoost (XGB) algorithm which runs automatically on patient data from the electronic health record (EHR). The algorithm was trained on 8 years of de-identified data (from 2010 to 2017) collected from a large regional healthcare system. The input variables include demographics, vital signs, laboratory values, some orders, and specific pre-existing diagnoses. The model was designed to make predictions 2 h prior to the need of first CS intervention (inotrope, vasopressor, or mechanical circulatory support). The algorithm achieves an overall area under curve (AUC) of 0.87 (0.81 in CC, 0.84 in ED, 0.97 in WARD), which is considered useful for clinical use. The algorithm can be refined based on specific elements defining patient subpopulations, for example presence of acute myocardial infarction (AMI) or congestive heart failure (CHF), further increasing its precision when a patient has these conditions. The top-contributing risk factors learned by the model are consistent with existing clinical findings. Our conclusion is that a useful machine learning model can be used to predict the development of CS. This manuscript describes the main steps of the development process and our results.

A randomized controlled trial of simulation training in teaching coronary angiographic views.

INTRODUCTION: Simulation technology has an established role in teaching technical skills to cardiology fellows, but its impact on teaching trainees to interpret coronary angiographic (CA) images has not been systematically studied. The aim of this randomized controlled study was to test whether structured simulation training, in addition to traditional methods would improve CA image interpretation skills in a heterogeneous group of medical trainees. METHODS: We prospectively randomized a convenience sample of 105 subjects comprising of medical students (N = 20), residents (N = 68) and fellows (N = 17) from the University of Arizona. Subjects were randomized in a stratified fashion into a simulation training group which received simulation training in addition to didactic teaching (n = 53) and a control training group which received didactic teaching alone (n = 52). The change in pre and post-test score (delta score) was analyzed by a two-way ANOVA for education status and training arm. RESULTS: Subjects improved in their post-test scores with a mean change of 4.6 ± 4.0 points. Subjects in the simulation training arm had a higher delta score compared to control (5.4 ± 4.2 versus 3.8 ± 3.7, p = 0.04), with greatest impact for residents (6.6 ± 4.0 versus 3.5 ± 3.4) with a p = 0.02 for interaction of training arm and education status. CONCLUSIONS: Simulation training complements traditional methods to improve CA interpretation skill, with greatest impact on residents. This highlights the importance of incorporating high-fidelity simulation training early in cardiovascular fellowship curricula.

North American COVID-19 Myocardial Infarction (NACMI) Risk Score for Prediction of In-Hospital Mortality.

Background: In-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI) is higher in those with COVID-19 than in those without COVID-19. The factors that predispose to this mortality rate and their relative contribution are poorly understood. This study developed a risk score inclusive of clinical variables to predict in-hospital mortality in patients with COVID-19 and STEMI. Methods: Baseline demographic, clinical, and procedural data from patients in the North American COVID-19 Myocardial Infarction registry were extracted. Univariable logistic regression was performed using candidate predictor variables, and multivariable logistic regression was performed using backward stepwise selection to identify independent predictors of in-hospital mortality. Independent predictors were assigned a weighted integer, with the sum of the integers yielding the total risk score for each patient. Results: In-hospital mortality occurred in 118 of 425 (28%) patients. Eight variables present at the time of STEMI diagnosis (respiratory rate of >35 breaths/min, cardiogenic shock, oxygen saturation of <93%, age of >55 â€‹years, infiltrates on chest x-ray, kidney disease, diabetes, and dyspnea) were assigned a weighted integer. In-hospital mortality increased exponentially with increasing integer risk score (Cochran-Armitage χ2, P â€‹< â€‹.001), and the model demonstrated good discriminative power (c-statistic â€‹= â€‹0.81) and calibration (Hosmer-Lemeshow, P â€‹= â€‹.40). The increasing risk score was strongly associated with in-hospital mortality (3.6%-60% mortality for low-risk and very high-risk score categories, respectively). Conclusions: The risk of in-hospital mortality in patients with COVID-19 and STEMI can be accurately predicted and discriminated using readily available clinical information.

Unprovoked Isolated Pulmonary Embolism and Graves' Disease in a Patient With Dyspnea: A Case Report.

Graves' disease is a commonly diagnosed disease with a plethora of manifestations that can lead to its diagnosis. One of the rarer presentations of Graves' disease is hypercoagulability with the development of spontaneous venous thrombosis. In patients presenting with unprovoked pulmonary embolism, we suggest evaluating the patient's thyroid function tests as a potential underlying cause. To bring this issue to attention, we are presenting a rare case of isolated spontaneous pulmonary embolism development secondarily to underlying Graves' disease.

Transcatheter Closure of Complex Left Circumflex to Coronary Sinus Fistula.

Coronary artery fistulae connecting the left circumflex to the coronary sinus are rare. Surgical closure of coronary sinus connections is technically challenging because of the location, especially in high-risk surgical patients. We used multimodality imaging to delineate the drainage site and successfully closed a left circumflex to coronary sinus fistula using a transcatheter closure technique. (Level of Difficulty: Advanced.).

Trends in Clinical Presentation, Management, and Outcomes of STEMI in Patients With COVID-19.

BACKGROUND: We previously reported high in-hospital mortality for ST-segment elevation myocardial infarction (STEMI) patients with COVID-19 treated in the early phase of the pandemic. OBJECTIVES: The purpose of this study was to describe trends of COVID-19 patients with STEMI during the course of the pandemic. METHODS: The NACMI (North American COVID-19 STEMI) registry is a prospective, investigator-initiated, multicenter, observational registry of hospitalized STEMI patients with confirmed or suspected COVID-19 infection in North America. We compared trends in clinical characteristics, management, and outcomes of patients treated in the first year of the pandemic (January 2020 to December 2020) vs those treated in the second year (January 2021 to December 2021). RESULTS: A total of 586 COVID-19-positive patients with STEMI were included in the present analysis; 227 treated in Y2020 and 359 treated in Y2021. Patients' characteristics changed over time. Relative to Y2020, the proportion of Caucasian patients was higher (58% vs 39%; P < 0.001), patients presented more frequently with typical ischemic symptoms (59% vs 51%; P = 0.04), and patients were less likely to have shock pre-PCI (13% vs 18%; P = 0.07) or pulmonary manifestations (33% vs. 47%; P = 0.001) in Y2021. In-hospital mortality decreased from 33% (Y2020) to 23% (Y2021) (P = 0.008). In Y2021, none of the 22 vaccinated patients expired in hospital, whereas in-hospital death was recorded in 37 (22%) unvaccinated patients (P = 0.009). CONCLUSIONS: Significant changes have occurred in the clinical characteristics and outcomes of STEMI patients with COVID-19 infection during the course of the pandemic.

Five-meter walk test before transcatheter aortic valve replacement and 1-year noncardiac mortality.

Objective: The purpose of this study is to assess whether the 5-m walk test is associated with 1-year mortality after transcatheter aortic valve replacement. Methods: Included in the analysis were 304 patients who received the 5-m walk test and underwent transcatheter aortic valve replacement from September 2012 to March 2019. They were classified into 3 groups based on their test score: ≤7, >7, and unable to walk. Preprocedure characteristics, postprocedure outcomes, and follow-up outcomes were compared between the groups. Results: For the 5-m walk test, 145 had a score ≤7 (Group N), 111 had a score >7 (Group S), and 48 were unable to walk (Group I). Average age in years was 80.2 ± 8.7 years in Group N, 81.2 ± 9.4 years in Group S, and 79.4 ± 9.2 in Group I (P = .23). The aortic valve mean gradient at discharge was 9.5 ± 4.1 mm Hg in Group N, 10.4 ± 5.5 mm Hg in Group S, and 8.2 ± 4.2 mm Hg in Group I (P = .05). The discharge survival was 97.2% in Group N, 96.4% in Group S, and 95.8% in Group I (P = .76). One-year survival was 92.8% in Group N, 84.1% in Group S, and 75% in Group I (P < .01) after adjusting for preprocedure characteristics. Noncardiac death was 5.1% in Group N, 13.1% in Group S, and 22.7% in Group I (P = .03). This indicates that the 5-m walk test was a risk factor for 1-year mortality. More specifically, a poor 5-m walk test score was associated with 1-year noncardiac mortality. Conclusions: The 5-m walk test score before transcatheter aortic valve replacement was associated with 1-year mortality, especially noncardiac mortality. It may help identify patients at high risk for 1-year mortality.

Decreased frequency of transplantation and lower post-transplant survival free of re-transplantation in LVAD patients with the new heart transplant allocation system.

PURPOSE: To evaluate the effect of the new heart transplant (HT) allocation system in left ventricular assist device (LVAD) supported patients listed as bridge to transplantation (BTT). METHODS: Adult patients who were listed for HT between October 18, 2016 and October 17, 2019, and were supported with an LVAD, enrolled in the UNOS database were included in this study. Patients were classified in the old or new system if they were listed or transplanted before or after October 18, 2018, respectively. RESULTS: A total of 3261 LVAD patients were listed for transplant. Of these, 2257 were classified in the old and 1004 in the new system. The cumulative incidence of death or removal from the transplant list due to worsening clinical status at 360-days after listing was lower in the new system (4% vs. 7%, P = .011). LVAD Patients listed in the new system had a lower frequency of transplantation within 360-days of listing (52% vs. 61%, P < .001). A total of 1843 LVAD patients were transplanted, 1004 patients in the old system and 839 patients in the new system. The post-transplant survival at 360 days was similar between old and new systems (92.3% vs. 90%, P = .08). However, LVAD patients transplanted in the new system had lower frequency of the combined endpoint, freedom of death or re-transplantation at 360 days (92.2% vs. 89.6%, P = .046). CONCLUSION: The new HT allocation system has affected the LVAD-BTT population significantly. On the waitlist, LVAD patients have a decreased cumulative frequency of transplantation and a concomitant decrease in death or delisting due to worsening status. In the new system, LVAD patients have a decreased survival free of re-transplantation at 360 days post-transplant.