An artificial neural network to safely reduce the number of ambulance ECGs transmitted for physician assessment in a system with prehospital detection of ST elevation myocardial infarction.

BACKGROUND: Pre-hospital electrocardiogram (ECG) transmission to an expert for interpretation and triage reduces time to acute percutaneous coronary intervention (PCI) in patients with ST elevation Myocardial Infarction (STEMI). In order to detect all STEMI patients, the ECG should be transmitted in all cases of suspected acute cardiac ischemia. The aim of this study was to examine the ability of an artificial neural network (ANN) to safely reduce the number of ECGs transmitted by identifying patients without STEMI and patients not needing acute PCI. METHODS: Five hundred and sixty ambulance ECGs transmitted to the coronary care unit (CCU) in routine care were prospectively collected. The ECG interpretation by the ANN was compared with the diagnosis (STEMI or not) and the need for an acute PCI (or not) as determined from the Swedish coronary angiography and angioplasty register. The CCU physician's real time ECG interpretation (STEMI or not) and triage decision (acute PCI or not) were registered for comparison. RESULTS: The ANN sensitivity, specificity, positive and negative predictive values for STEMI was 95%, 68%, 18% and 99%, respectively, and for a need of acute PCI it was 97%, 68%, 17% and 100%. The area under the ANN's receiver operating characteristics curve for STEMI detection was 0.93 (95% CI 0.89-0.96) and for predicting the need of acute PCI 0.94 (95% CI 0.90-0.97). If ECGs where the ANN did not identify a STEMI or a need of acute PCI were theoretically to be withheld from transmission, the number of ECGs sent to the CCU could have been reduced by 64% without missing any case with STEMI or a need of immediate PCI. CONCLUSIONS: Our ANN had an excellent ability to predict STEMI and the need of acute PCI in ambulance ECGs, and has a potential to safely reduce the number of ECG transmitted to the CCU by almost two thirds.

Negative predictive value and potential cost savings of acute nuclear myocardial perfusion imaging in low risk patients with suspected acute coronary syndrome: a prospective single blinded study.

BACKGROUND: Previous studies from the USA have shown that acute nuclear myocardial perfusion imaging (MPI) in low risk emergency department (ED) patients with suspected acute coronary syndrome (ACS) can be of clinical value. The aim of this study was to evaluate the utility and hospital economics of acute MPI in Swedish ED patients with suspected ACS. METHODS: We included 40 patients (mean age 55 +/- 2 years, 50% women) who were admitted from the ED at Lund University Hospital for chest pain suspicious of ACS, and who had a normal or non-ischemic ECG and no previous myocardial infarction. All patients underwent MPI from the ED, and the results were analyzed only after patient discharge. The current diagnostic practice of admitting the included patients for observation and further evaluation was compared to a theoretical "MPI strategy", where patients with a normal MPI test would have been discharged home from the ED. RESULTS: Twenty-seven patients had normal MPI results, and none of them had ACS. MPI thus had a negative predictive value for ACS of 100%. With the MPI strategy, 2/3 of the patients would thus have been discharged from the ED, resulting in a reduction of total hospital cost by some 270 EUR and of bed occupancy by 0.8 days per investigated patient. CONCLUSION: Our findings in a Swedish ED support the results of larger American trials that acute MPI has the potential to safely reduce the number of admissions and decrease overall costs for low-risk ED patients with suspected ACS.

In search of the best method to predict acute coronary syndrome using only the electrocardiogram from the emergency department.

INTRODUCTION: The aim of this study was to compare different methods to predict acute coronary syndrome (ACS) using only data from a single electrocardiogram (ECG) in the emergency department (ED). METHOD: We compared the ACS prediction abilities of classical ECG criteria, human expert ECG interpretation, a logistic regression model and an artificial neural network ensemble (ANN). The ED ECG and discharge diagnoses were retrieved for 861 patient visits to the ED for chest pain. Cross-validation was used to estimate the generalization performance of the logistic regression and the ANN model. RESULTS: The logistic regression model had the overall best performance in predicting ACS with an area under the receiver operating characteristic curve of 0.88. The sensitivities of logistic regression, ANN, expert physicians, and classical ECG criteria were 95%, 95%, 82%, and 75%, respectively, and the specificities were 54%, 44%, 63%, and 69%. CONCLUSION: Our logistic regression model was the best overall method to predict ACS, followed by our ANN. Decision support models have the potential to improve even experienced ECG readers' ability to predict ACS in the ED.

A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department.

BACKGROUND: Several models for prediction of acute coronary syndrome (ACS) among chest pain patients in the emergency department (ED) have been presented, but many models predict only the likelihood of acute myocardial infarction, or include a large number of variables, which make them less than optimal for implementation at a busy ED. We report here a simple statistical model for ACS prediction that could be used in routine care at a busy ED. METHODS: Multivariable analysis and logistic regression were used on data from 634 ED visits for chest pain. Only data immediately available at patient presentation were used. To make ACS prediction stable and the model useful for personnel inexperienced in electrocardiogram (ECG) reading, simple ECG data suitable for computerized reading were included. RESULTS: Besides ECG, eight variables were found to be important for ACS prediction, and included in the model: age, chest discomfort at presentation, symptom duration and previous hypertension, angina pectoris, AMI, congestive heart failure or PCI/CABG. At an ACS prevalence of 21% and a set sensitivity of 95%, the negative predictive value of the model was 96%. CONCLUSION: The present prediction model, combined with the clinical judgment of ED personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.

Direct hospital costs of chest pain patients attending the emergency department: a retrospective study.

BACKGROUND: Chest pain is one of the most common complaints in the Emergency Department (ED), but the cost of ED chest pain patients is unclear. The aim of this study was to describe the direct hospital costs for unselected chest pain patients attending the emergency department (ED). METHODS: 1,000 consecutive ED visits of patients with chest pain were retrospectively included. Costs directly following the ED visit were retrieved from the hospital economy system. RESULTS: The mean cost per patient visit was 26.8 thousand Swedish kronar (kSEK) (median 7.2 kSEK), with admission time accounting for 73% of all costs. Mean cost for patients discharged from the ED was 1.4 kSEK (median 1.3 kSEK), and for patients without ACS admitted 1 day or less 7.6 kSEK (median 6.9 kSEK). The practice in the present study to admit 67% of the patients, of whom only 31% proved to have ACS, was estimated to give a cost per additional life-year saved by hospital admission, compared to theoretical strategy of discharging all patients home, of about 350 kSEK (39 kEUR or 42 kUSD). CONCLUSION: Costs for chest pain patients are large and primarily due to admission time. The present admission practice seems to be cost-effective, but the substantial overadmission indicates that better ED diagnostics and triage could decrease costs considerably.