The effect of high-risk ST elevation myocardial infarction transfer patients on risk-adjusted in-hospital mortality: A report from the American Heart Association Mission: Lifeline program

Regional Differences in the Ratio of Observed and Expected In-hospital Mortality for Acute Coronary Syndrome Patients in China: The Improving Care for Cardiovascular Disease in China-Acute Coronary Syndrome Project Analysis

Previous studies reported regional variations in in-hospital acute coronary syndrome (ACS) mortality, but the reasons for that were not clearly defined. We explored whether differences in patient characteristics could explain regional variation. The Improving Care for Cardiovascular Disease in China (CCC)-ACS project is an ongoing national registry and quality improvement project, involving 150 tertiary hospitals from 30 provinces across China. We applied a prediction model that included patient-specific variables to calculate the expected in-hospital mortality. For each province, we reported the observed, expected in-hospital mortality and the risk-adjusted ratio which is based on the observed divided by the expected mortality. From 2014 to 2018, 79 585 ACS patients were enrolled. The average in-hospital mortality was 1.8%. There was a wide variation in the in-hospital mortality among different provinces (0.2-3.9%). Patient characteristics explained part of this variation because of differences in the expected in-hospital mortality (0.7-2.8%). There was a substantial variation in the risk-adjusted ratio among provinces (0.2-3.5), which suggests that the variations in the mortality cannot be completely explained by the differences in patient characteristics. In conclusion, we observed a wide regional variation in mortality for ACS, part of which could be explained by the difference in patient characteristics.

Patient Phenotypes, Cardiovascular Risk, and Ezetimibe Treatment in Patients After Acute Coronary Syndromes (from IMPROVE-IT)

Risk prediction following acute coronary syndrome (ACS) remains challenging. Data-driven machine-learning algorithms can potentially identify patients at high risk of clinical events. The Improved Reduction of Outcomes: Vytorin Efficacy International Trial randomized 18,144 post-ACS patients to ezetimibe + simvastatin or placebo + simvastatin. We performed hierarchical cluster analysis to identify patients at high risk of adverse events. Associations between clusters and outcomes were assessed using Cox proportional hazards models. The primary outcome was cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, unstable angina hospitalization, or coronary revascularization ≥30 days after randomization. We evaluated ezetimibe's impact on outcomes across clusters and the ability of the cluster analysis to discriminate for outcomes compared with the Global Registry of Acute Coronary Events (GRACE) score. Five clusters were identified. In cluster 1 (n = 13,252), most patients experienced a non-STEMI (54.8%). Cluster 2 patients (n = 2,719) had the highest incidence of unstable angina (n = 83.3%). Cluster 3 patients (n = 782) all identified as Spanish descent, whereas cluster 4 patients (n = 803) were primarily from South America (56.2%). In cluster 5 (n = 587), all patients had ST elevation. Cluster analysis identified patients at high risk of adverse outcomes (log-rank p <0.0001); Cluster 2 (vs 1) patients had the highest risk of outcomes (hazards ratio 1.33, 95% confidence interval 1.24 to 1.43). Compared with GRACE risk, cluster analysis did not provide superior outcome discrimination. A consistent ezetimibe treatment effect was identified across clusters (interaction p = 0.882). In conclusion, cluster analysis identified significant difference in risk of outcomes across cluster groups. Data-driven strategies to identify patients who may differentially benefit from therapies and for risk stratification require further evaluation.

A Long-Forgotten Tale: The Management of Cardiogenic Shock in Acute Myocardial Infarction

Patients with acute myocardial infarction (AMI) complicated with cardiogenic shock (CS) present one of the highest mortality rates recorded in critical care. Mortality rate in this setting is reported around 45-50% even in the most experienced and well-equipped medical centers. The continuous development of ST-segment elevation acute myocardial infarction (STEMI) networks has led not only to a dramatic decrease in STEMI-related mortality, but also to an increase in the frequency of severely complicated cases who survive to be transferred to tertiary centers for life-saving treatments. The reduced effectiveness of vasoactive drugs on a severely altered hemodynamic status led to the development of new devices dedicated to advanced cardiac support. What’s more, efforts are being made to reduce time from first medical contact to initiation of mechanical support in this particular clinical context. This review aims to summarize the most recent advances in mechanical support devices, in the setting of CS-complicated AMI. At the same time, the review presents several modern concepts in the organization of complex CS centers. These specialized hubs could improve survival in this critical condition.

Review. Regional Networks in Acute Cardiac Care

In acute cardiac care, the timely initiation of life-saving measures proved to be life-saving and requires many organizational and logistic measures. One of such measures is represented by the development and implementation of a regional network dedicated for the treatment of major cardiovascular emergencies, a strategy that proved to significantly reduce mortality rates on short and long term. This review aims to describe the current status in the development of regional networks in three of the main cardiovascular emergencies: acute myocardial infarction, out-of-hospital cardiac arrest, and acute stroke. The concepts demonstrating the utility of such networks, together with their results in reducing cardiac events, are presented in this paper.

Calculating the 30-day Survival Rate in Acute Myocardial Infarction: Should we Use the Treatment Chain or the Hospital Catchment Model?

Introduction

Acute myocardial infarction (AMI) is a potentially deadly disease and significant efforts have been concentrated on improving hospital performance. A 30-day survival rate has become a key quality of care indicator. In Northern Norway, some patients undergoing AMI are directly transferred to the Regional Cardiac Intervention Center at the University Hospital of North Norway in Tromsø. Here, coronary angiography and percutaneous coronary intervention is performed. Consequently, local hospitals may be bypassed in the treatment chain, generating differences in case mix, and making the treatment chain model difficult to interpret. We aimed to compare the treatment chain model with an alternative based on patients’ place of living.

Methods

Between 2013 and 2015, a total of 3,155 patients were registered in the Norwegian Patient Registry database. All patients were categorized according to their local hospital's catchment area. The method of Guo-Romano, with an indifference interval of 0.02, was used to test whether a hospital was an outlier or not. We adjusted for age, sex, comorbidity, and number of prior hospitalizations.

Conclusions

We revealed the 30-day AMI survival figure ranging between 88.0% and 93.5% (absolute difference 5.5%) using the hospital catchment method. The treatment chain rate ranged between 86.0% and 94.0% (absolute difference 8.0%). The latter figure is the one published as the National Quality of Care Measure in Norway. Local hospitals may get negative attention even though their catchment area is well served. We recommend the hospital catchment method as the first choice when measuring equality of care.