Comparative effectiveness of population interventions to improve access to reperfusion for ST-segment–elevation myocardial infarction in Australia

Background Improving timely access to reperfusion is a major goal of ST-segment–elevation myocardial infarction care. We sought to compare the population impact of interventions proposed to improve timely access to reperfusion therapy in Australia. Methods and Results Australian hospitals, population, and road network data were integrated using Geographical Information Systems. Hospitals were classified into those that provided primary percutaneous coronary intervention (PPCI) or fibrinolysis. Population impact of interventions proposed to improve timely access to reperfusion (PPCI, fibrinolysis, or both) were modeled and compared. Timely access to reperfusion was defined as the proportion of the population capable of reaching a fibrinolysis facility ≤60 minutes or a PPCI facility ≤120 minutes from emergency medical services activation. The majority (93.2%) of the Australian population has timely access to reperfusion, mainly (53%) through fibrinolysis. Only 40.2% of the population had timely access to PPCI, and access to PPCI services is particularly limited in regional and nonexistent in remote areas. Optimizing the emergency medical services’ response or increasing PPCI services resulted in marginal improvement in timely access (1.8% and 3.7%, respectively). Direct transport to PPCI facilities and interhospital transfer for PPCI improves timely access to PPCI for 19.4% and 23.5% of the population, respectively. Prehospital fibrinolysis markedly improved access to timely reperfusion in regional and remote Australia. Conclusions Significant gaps in timely provision of reperfusion remain in Australia. Systematic implementation of changes in service delivery has potential to improve timely access to PPCI for a majority of the population and improve access to fibrinolysis to those living in regional and remote areas.

Ticagrelor, a platelet aggregation inhibitor for the potential prevention and treatment of arterial thrombosis and acute coronary syndromes

Ticagrelor is an orally active ADP P2Y12 receptor antagonist in development by AstraZeneca plc for the reduction of recurrent ischemic events in patients with acute coronary syndromes (ACS). Prior to the development of ticagrelor, thienopyridine compounds, such as clopidogrel, were the focus of research into therapies for ACS. Although the thienopyridines are effective platelet aggregation inhibitors, they are prodrugs and, consequently, exert a slow onset of action. In addition, the variability in inter-individual metabolism of thienopyridine prodrugs has been associated with reduced efficacy in some patients. Ticagrelor is not a prodrug and exhibits a more rapid onset of action than the thienopyridine prodrugs. In clinical trials conducted to date, ticagrelor was a potent inhibitor of ADP-induced platelet aggregation and demonstrated effects that were comparable to clopidogrel. In a phase II, short-term trial, the bleeding profile of participants treated with ticagrelor was similar to that obtained with clopidogrel; however, an increased incidence of dyspnea was observed - an effect that has not been reported with the thienopyridines. Considering the occurrence of dyspnea, and the apparent non-superiority of ticagrelor to clopidogrel, it is difficult to justify a clear benefit to the continued development of ticagrelor. Outcomes from an ongoing phase III trial comparing ticagrelor with clopidogrel in 18,000 patients with ACS are likely to impact on the future development of ticagrelor.

Comprehensive standardized data definitions for acute coronary syndrome research in emergency departments in Australasia

Patients with chest discomfort or other symptoms suggestive of acute coronary syndrome (ACS) are one of the most common categories seen in many Emergency Departments (EDs). While the recognition of patients at high-risk of ACS has improved steadily, identifying the majority of chest pain presentations who fall into the low-risk group remains a challenge. Research in this area needs to be transparent, robust, applicable to all hospitals from large tertiary centres to rural and remote sites, and to allow direct comparison between different studies with minimum patient spectrum bias. A standardised approach to the research framework using a common language for data definitions must be adopted to achieve this. The aim was to create a common framework for a standardised data definitions set that would allow maximum value when extrapolating research findings both within Australasian ED practice, and across similar populations worldwide. Therefore a comprehensive data definitions set for the investigation of non-traumatic chest pain patients with possible ACS was developed, specifically for use in the ED setting. This standardised data definitions set will facilitate ‘knowledge translation’ by allowing extrapolation of useful findings into the real-life practice of emergency medicine.

Psychosocial outcomes of Hong Kong Chinese diagnosed with acute coronary syndromes: a prospective repeated measures study.

BACKGROUND: Western studies have suggested that emotional stress and distress impacted on the morbidity and mortality in people following acute coronary events. Symptoms of anxiety and depression have been associated with re-infarction and death, prolonged recovery and disability and depression may precipitate the client's low self-esteem. This study examined perceived anxiety, depression and self-esteem of Hong Kong Chinese clients diagnosed with acute coronary syndrome (ACS) over a 6-month period following hospital admission. OBJECTIVES: To examine: DESIGN: A prospective, repeated measures design with measures taken on two occasions over a 6-month period; (1) within the 1st week of hospital admission following the onset of ACS and (2) at 6 months follow up. SETTING AND PARTICIPANTS: Convenient sample of 182 voluntary consented clients admitted with ACS to a major public hospital in Hong Kong who could communicate in Chinese, complete questionnaires, cognitive intact, and were haemodynamically stable and free from acute chest pain at the time of interview. METHODS: Baseline data were obtained within 1 week after hospital admission. The follow-up data was collected 6 months after hospital discharge. The Chinese version of the Hospital Anxiety and Depression Scale (HADS), State Self-esteem Scale (SSES), and Rosenberg's Self-Esteem Scale (RSES) were used to assess anxiety and depression, state self-esteem, and trait self-esteem, respectively. RESULTS: Findings suggested gender differences in clients' perception in anxiety, depression and self-esteem. Improvements in clients' perception of these variables were evident over the 6-month period following their acute coronary events. CONCLUSION: The study confirmed the western notion that psychosocial problems are common among coronary clients and this also applies to Hong Kong Chinese diagnosed with ACS. Further studies to explore effective interventions to address these psychosocial issues are recommended.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

New drugs for the treatment of coronary artery syndromes

Background: Acute coronary syndromes are a major cause of mortality and morbidity. Objectives/Methods: The objective of this evaluation is to review the clinical trials of two new drugs being developed for the treatment of acute coronary syndromes. The first drug is the anti-coagulant otamixaban, and the trial compared otamixaban with unfractionated heparin and eptifibatide in acute coronary syndromes. The second drug is the anti-platelet ticagrelor, and the trial compared ticagrelor with clopidogrel in acute coronary syndromes. Results: In the SEPIA-ACS1 TIMI 42 trial, the primary efficacy endpoint occurred in 6.2% of subjects treated with unfractionated heparin and eptifibatide, and to a significantly lesser extent with otamixaban. In the PLATO trial, the primary efficacy endpoint had occurred less in the ticagrelor group (9.8%) than in the clopidogrel group (11.7%) at 12 months. Conclusions: Two new drugs for acute coronary syndromes, otamixaban and ticagrelor, have recently been shown to have benefits in subjects undergoing percutaneous interventions compared to the present standard regimens for this condition.