Jakob Lundager Forberg

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

BackgroundChest 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).Methods1,000 consecutive ED visits of patients with chest pain were retrospectively included. Costs directly following the ED visit were retrieved from the hospital economy system.ResultsThe 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).ConclusionCosts 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.

Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room

OBJECTIVE Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. METHODS AND MATERIALS Artificial neural network (ANN) ensembles and logistic regression models were trained on data from 634 patients presenting an emergency department with chest pain. Only data immediately available at patient presentation were used, including electrocardiogram (ECG) data. The models were analyzed using receiver operating characteristics (ROC) curve analysis, calibration assessments, inter- and intra-method variations. Effective odds ratios for the ANN ensembles were compared with the odds ratios obtained from the logistic model. RESULTS The ANN ensemble approach together with ECG data preprocessed using principal component analysis resulted in an area under the ROC curve of 80%. At the sensitivity of 95% the specificity was 41%, corresponding to a negative predictive value of 97%, given the ACS prevalence of 21%. Adding clinical data available at presentation did not improve the ANN ensemble performance. Using the area under the ROC curve and model calibration as measures of performance we found an advantage using the ANN ensemble models compared to the logistic regression models. CONCLUSION Clinically, a prediction model of the present type, combined with the judgment of trained emergency department personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.

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

BackgroundSeveral 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.MethodsMultivariable 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.ResultsBesides 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%.ConclusionThe 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.

Evaluation of emergency department performance: A systematic review on recommended performance and quality-in-care measures

BackgroundEvaluation of emergency department (ED) performance remains a difficult task due to the lack of consensus on performance measures that reflects high quality, efficiency, and sustainability.AimTo describe, map, and critically evaluate which performance measures that the published literature regard as being most relevant in assessing overall ED performance.MethodsFollowing the PRISMA guidelines, a systematic literature review of review articles reporting accentuated ED performance measures was conducted in the databases of PubMed, Cochrane Library, and Web of Science. Study eligibility criteria includes: 1) the main purpose was to discuss, analyse, or promote performance measures best reflecting ED performance, 2) the article was a review article, and 3) the article reported macro-level performance measures, thus reflecting an overall departmental performance level.ResultsA number of articles addresses this study’s objective (n = 14 of 46 unique hits). Time intervals and patient-related measures were dominant in the identified performance measures in review articles from US, UK, Sweden and Canada. Length of stay (LOS), time between patient arrival to initial clinical assessment, and time between patient arrivals to admission were highlighted by the majority of articles. Concurrently, “patients left without being seen” (LWBS), unplanned re-attendance within a maximum of 72 hours, mortality/morbidity, and number of unintended incidents were the most highlighted performance measures that related directly to the patient. Performance measures related to employees were only stated in two of the 14 included articles.ConclusionsA total of 55 ED performance measures were identified. ED time intervals were the most recommended performance measures followed by patient centeredness and safety performance measures. ED employee related performance measures were rarely mentioned in the investigated literature. The study’s results allow for advancement towards improved performance measurement and standardised assessment across EDs.

New methods for improved evaluation of patients with suspected acute coronary syndrome in the emergency department

This paper aims to identify and review new and unproven emergency department (ED) methods for improved evaluation in cases of suspected acute coronary syndrome (ACS). Systematic news coverage through PubMed from 2000 to 2006 identified papers on new methods for ED assessment of patients with suspected ACS. Articles found described decision support models, new ECG methods, new biomarkers and point-of-care testing, cardiac imaging, immediate exercise tests and the chest pain unit concept. None of these new methods is likely to be the perfect solution, and the best strategy today is therefore a combination of modern methods, where the optimal protocol depends on local resources and expertise. With a suitable combination of new methods, it is likely that more patients can be managed as outpatients, that length of stay can be shortened for those admitted, and that some patients with ACS can get earlier treatment.

Exploring new possibilities for case-based explanation of artificial neural network ensembles

Artificial neural network (ANN) ensembles have long suffered from a lack of interpretability. This has severely limited the practical usability of ANNs in settings where an erroneous decision can be disastrous. Several attempts have been made to alleviate this problem. Many of them are based on decomposing the decision boundary of the ANN into a set of rules. We explore and compare a set of new methods for this explanation process on two artificial data sets (Monks 1 and 3), and one acute coronary syndrome data set consisting of 861 electrocardiograms (ECG) collected retrospectively at the emergency department at Lund University Hospital. The algorithms managed to extract good explanations in more than 84% of the cases. More to the point, the best method provided 99% and 91% good explanations in Monks data 1 and 3 respectively. Also there was a significant overlap between the algorithms. Furthermore, when explaining a given ECG, the overlap between this method and one of the physicians was the same as the one between the two physicians in this study. Still the physicians were significantly, p-value<0.001, more similar to each other than to any of the methods. The algorithms have the potential to be used as an explanatory aid when using ANN ensembles in clinical decision support systems.

Peripheral venous lactate at admission is associated with in-hospital mortality, a prospective cohort study

The prognostic value of blood lactate as a predictor of adverse outcome in the acutely ill patient is unclear. The aim of this study was to investigate if a peripheral venous lactate measurement, taken at admission, is associated with in‐hospital mortality in acutely ill patients with all diagnosis. Furthermore, we wanted to investigate if the test improves a triage model in terms of predicting in‐hospital mortality.

Readmissions in the Emergency Department

Methods All patients admitted to the ED at Hilleroed Hospital were registered during a 6-months period from September 2009 to March 2010. The ED has approximately 50,000 patient contacts annually, a level 2 Trauma centre and the following specialties: Internal Medicine, Neurology, Surgery and Orthopaedics. Paediatric patients (medicine) and patients to Obstetrics/Gynaecology are admitted directly to the general ward and not through the ED. All contacts resulting in admission were retrieved.

SuPAR - A future prognostic biomarker in emergency medicine

Background Efficient triage in the Emergency Departments (ED) is important to identify patients in need of urgent care. Biomarker measurements may aid these clinical decisions. suPAR, soluble urokinase-type plasminogen activator receptor, is a non-specific biomarker reflecting inflammation and is a strong prognostic marker for several diseases. This study investigated suPAR’s predictive capacity to identify highand low-risk patients in the Emergency Department.

Prediction of in-hospital mortality and admission to ICU using vital signs - a study of Early Warning Score as an alternative to traditional triage

Background Triage of patients in the Emergency Department includes scoring of vital parameters. The objective of this study was to compare two such triage systems for assessing vital parameters - a single-parameter system, T-vital, as used in Danish Emergency Process Triage, and a multiple-parameter system, T-EWS, which we based on Early Warning Score (EWS) - and correlate the triage scores to in-hospital mortality and admission to ICU. Studies examining EWS in triage are currently limited in number. Methods Using data from the Acute Admission Database of Nordsjaellands Hospital (n = 6164 admissions), we calculated and stratified EWS into four T-EWS colour codes (red, orange, yellow, and green), testing different stratifications’ correlation to in-hospital mortality and admission to ICU. Afterwards, we compared the ability of the chosen T-EWS and T-vital to predict patients at risk (red and orange category) of in-hospital mortality or admission to ICU. The data were analysed using area under the receiver operating curve (AUROC), sensitivity, specificity, overtriage, undertriage, and diagnostic rates. Results