A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain

Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review

Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.

International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction

Aims

A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria.

Methods and results

An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924-0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7-92.0), sensitivity 80.6% (95% CI: 76.8-84.0), and specificity 93.7 (95% CI: 92.6-94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6)].

Conclusion

The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects

Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease. Within heart failure, AI algorithms can augment diagnostic capabilities and clinical decision-making through automated cardiac measurements. Occult cardiac disease is increasingly being identified using ML from diagnostic data. Improved diagnostic and prognostic capabilities using ML algorithms are enhancing clinical care of patients with valvular heart disease and coronary artery disease. The growth of AI techniques is not without inherent challenges, most important of which is the need for greater external validation through multicenter, prospective clinical trials.

[Managing STEMI before the hospital : let's identify our enemies!]

Managing a patient with chest pain suspected to be a ST segment elevation myocardial infarction is a race against time. This management is based on a chain, like what is presented for cardiac arrest. Three phases follow one another, with potential loss of time successively attributable to the patient, the emergency physician and then the cardiologist. It would be tempting to consider that the main culprit in the event of delayed treatment is the patient. This review is the opportunity to show that it is not the case. The emergency physician, the cardiologist and their interconnection are the main providers of delay and, as such, the main enemies of myocardial reperfusion.

Application of Artificial Intelligence in Acute Coronary Syndrome: A Brief Literature Review

Artificial intelligence (AI) is defined as a set of algorithms and intelligence to try to imitate human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques. The application of AI in healthcare systems including hospitals and clinics has many possible advantages and future prospects. Applications of AI in cardiovascular medicine are machine learning techniques for diagnostic procedures including imaging modalities and biomarkers and predictive analytics for personalized therapies and improved outcomes. In cardiovascular medicine, AI-based systems have found new applications in risk prediction for cardiovascular diseases, in cardiovascular imaging, in predicting outcomes after revascularization procedures, and in newer drug targets. AI such as machine learning has partially resolved and provided possible solutions to unmet requirements in interventional cardiology. Predicting economically vital endpoints, predictive models with a wide range of health factors including comorbidities, socioeconomic factors, and angiographic factors comprising of the size of stents, the volume of contrast agent which was infused during angiography, stent malposition, and so on have been possible owing to machine learning and AI. Nowadays, machine learning techniques might possibly help in the identification of patients at risk, with higher morbidity and mortality following acute coronary syndrome (ACS). AI through machine learning has shown several potential benefits in patients with ACS. From diagnosis to treatment effects to predicting adverse events and mortality in patients with ACS, machine learning should find an essential place in clinical medicine and in interventional cardiology for the treatment and management of patients with ACS. This paper is a review of the literature which will focus on the application of AI in ACS.

Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review

BACKGROUND

Chest pain is amongst the most common reason for presentation to the emergency department (ED). There are many causes of chest pain, and it is important for the emergency physician to quickly and accurately diagnose life threatening causes such as acute myocardial infarction (AMI). Multiple clinical decision tools have been developed to assist clinicians in risk stratifying patients with chest. There is growing recognition that machine learning (ML) will have a significant impact on the practice of medicine in the near future and may assist with diagnosis and risk stratification. This systematic review aims to evaluate how ML has been applied to adults presenting to the ED with undifferentiated chest pain and assess if ML models show improved performance when compared to physicians or current risk stratification techniques.

METHODS AND FINDINGS

We conducted a systematic review of journal articles that applied a ML technique to an adult patient presenting to an emergency department with undifferentiated chest pain. Multiple databases were searched from inception through to November 2020. In total, 3361 articles were screened, and 23 articles were included. We did not conduct a metanalysis due to a high level of heterogeneity between studies in both their methods, and reporting. The most common primary outcomes assessed were diagnosis of acute myocardial infarction (AMI) (12 studies), and prognosis of major adverse cardiovascular event (MACE) (6 studies). There were 14 retrospective studies and 5 prospective studies. Four studies reported the development of a machine learning model retrospectively then tested it prospectively. The most common machine learning methods used were artificial neural networks (14 studies), random forest (6 studies), support vector machine (5 studies), and gradient boosting (2 studies). Multiple studies achieved high accuracy in both the diagnosis of AMI in the ED setting, and in predicting mortality and composite outcomes over various timeframes. ML outperformed existing risk stratification scores in all cases, and physicians in three out of four cases. The majority of studies were single centre, retrospective, and without prospective or external validation. There were only 3 studies that were considered low risk of bias and had low applicability concerns. Two studies reported integrating the ML model into clinical practice.

CONCLUSIONS

Research on applications of ML for undifferentiated chest pain in the ED has been ongoing for decades. ML has been reported to outperform emergency physicians and current risk stratification tools to diagnose AMI and predict MACE but has rarely been integrated into practice. Many studies assessing the use of ML in undifferentiated chest pain in the ED have a high risk of bias. It is important that future studies make use of recently developed standardised ML reporting guidelines, register their protocols, and share their datasets and code. Future work is required to assess the impact of ML model implementation on clinical decision making, patient orientated outcomes, and patient and physician acceptability.

TRIAL REGISTRATION

International Prospective Register of Systematic Reviews registration number: CRD42020184977.

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.

Machine Learning Versus Usual Care for Diagnostic and Prognostic Prediction in the Emergency Department: A Systematic Review

OBJECTIVE

Having shown promise in other medical fields, we sought to determine whether machine learning (ML) models perform better than usual care in diagnostic and prognostic prediction for emergency department (ED) patients.

METHODS

In this systematic review, we searched MEDLINE, Embase, Central, and CINAHL from inception to October 17, 2019. We included studies comparing diagnostic and prognostic prediction of ED patients by ML models to usual care methods (triage-based scores, clinical prediction tools, clinician judgment) using predictor variables readily available to ED clinicians. We extracted commonly reported performance metrics of model discrimination and classification. We used the PROBAST tool for risk of bias assessment (PROSPERO registration: CRD42020158129).

RESULTS

The search yielded 1,656 unique records, of which 23 studies involving 16,274,647 patients were included. In all seven diagnostic studies, ML models outperformed usual care in all performance metrics. In six studies assessing in-hospital mortality, the best-performing ML models had better discrimination (area under the receiver operating characteristic curve [AUROC] =0.74-0.94) than any clinical decision tool (AUROC =0.68-0.81). In four studies assessing hospitalization, ML models had better discrimination (AUROC =0.80-0.83) than triage-based scores (AUROC =0.68-0.82). Clinical heterogeneity precluded meta-analysis. Most studies had high risk of bias due to lack of external validation, low event rates, and insufficient reporting of calibration.

CONCLUSIONS

Our review suggests that ML may have better prediction performance than usual care for ED patients with a variety of clinical presentations and outcomes. However, prediction model reporting guidelines should be followed to provide clinically applicable data. Interventional trials are needed to assess the impact of ML models on patient-centered outcomes.

In-Hospital Prognostic Value of Electrocardiographic Parameters Other Than ST-Segment Changes in Acute Myocardial Infarction: Literature Review and Future Perspectives

Electrocardiography (ECG) remains an irreplaceable tool in the management of the patients with myocardial infarction, with evaluation of the QRS and ST segment being the present major focus. Several ECG parameters have already been proposed to have prognostic value with regard to both in-hospital and long-term follow-up of patients. In this review, we discuss various ECG parameters other than ST segment changes, particularly with regard to their in-hospital prognostic importance. Our review not only evaluates the prognostic segments and parts of ECG, but also highlights the need for an integrative approach in big data to re-assess the parameters reported to predict in-hospital prognosis. The evolving importance of artificial intelligence in evaluation of ECG, particularly with regard to predicting prognosis, and the potential integration with other patient characteristics to predict prognosis, are discussed.

Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach

Emergency departments (EDs) play a vital role in the whole healthcare system as they are the first point of care in hospitals for urgent and critically ill patients. Therefore, effective management of hospital's ED is crucial in improving the quality of the healthcare service. The effectiveness depends on how efficiently the hospital resources are used, particularly under budget constraints. Simulation modeling is one of the best methods to optimize resources and needs inputs such as patients' arrival time, patient's length of stay (LOS), and the route of patients in the ED. This study develops a simulation model to determine the optimum number of beds in an ED by minimizing the patients' LOS. The hospital data are analyzed, and patients' LOS and the route of patients in the ED are determined. To determine patients' arrival times, the features associated with patients' arrivals at ED are identified. Mean arrival rate is used as a feature in addition to climatic and temporal variables. The exhaustive feature-selection method has been used to determine the best subset of the features, and the mean arrival rate is determined as one of the most significant features. This study is executed using the one-year ED arrival data together with five-year (43.824 study hours) ED arrival data to improve the accuracy of predictions. Furthermore, ten different machine learning (ML) algorithms are used utilizing the same best subset of these features. After a tenfold cross-validation experiment, based on mean absolute percentage error (MAPE), the stateful long short-term memory (LSTM) model performed better than other models with an accuracy of 47%, followed by the decision tree and random forest methods. Using the simulation method, the LOS has been minimized by 7% and the number of beds at the ED has been optimized.

Artificial Intelligence for Diagnosis of Acute Coronary Syndromes: A Meta-analysis of Machine Learning Approaches

BACKGROUND

Machine learning (ML) encompasses a wide variety of methods by which artificial intelligence learns to perform tasks when exposed to data. Although detection of myocardial infarction has been facilitated with introduction of troponins, the diagnosis of acute coronary syndromes (ACS) without myocardial damage (without elevation of serum troponin) remains subjective, and its accuracy remains highly dependent on clinical skills of the health care professionals. Application of a ML algorithm may expedite management of ACS for either early discharge or early initiation of ACS management. We aim to summarize the published studies of ML for diagnosis of ACS.

METHODS

We searched electronic databases, including PubMed, Embase, and Web of Science from inception up to January 13, 2019, for studies that evaluated ML algorithms for the diagnosis of ACS in patients presenting with chest pain. We then used random-effects bivariate meta-analysis models to summarize the studies.

RESULTS

We retained 9 studies that evaluated ML in a total of 6292 patients. The prevalence of ACS in the evaluated cohorts ranged from relatively rare (7%) to common (57%). The pooled sensitivity and specificity were 0.95 and 0.90, respectively. The positive predictive values ranged from 0.64 to 1.0, and the negative predictive values ranged from 0.91 to 1.0. The positive and negative likelihood ratios ranged from 1.6 to 33.0 and 0.01 to 0.13, respectively.

CONCLUSIONS

The excellent sensitivity, negative likelihood ratio, and negative predictive values suggest that ML may be useful as an initial triage tool for ruling out ACS.

Artificial neural networks and risk stratification in emergency departments

Emergency departments are characterized by the need for quick diagnosis under pressure. To select the most appropriate treatment, a series of rules to support decision-making has been offered by scientific societies. The effectiveness of these rules affects the appropriateness of treatment and the hospitalization of patients. Analyzing a sample of 1844 patients and focusing on the decision to hospitalize a patient after a syncope event to prevent severe short-term outcomes, this work proposes a new algorithm based on neural networks. Artificial neural networks are a non-parametric technique with the well-known ability to generalize behaviors, and they can thus predict severe short-term outcomes with pre-selected levels of sensitivity and specificity. This innovative technique can outperform the traditional models, since it does not require a specific functional form, i.e., the data are not supposed to be distributed following a specific design. Based on our results, the innovative model can predict hospitalization with a sensitivity of 100% and a specificity of 79%, significantly increasing the appropriateness of medical treatment and, as a result, hospital efficiency. According to Garson's Indexes, the most significant variables are exertion, the absence of symptoms, and the patient's gender. On the contrary, cardio-vascular history, hypertension, and age have the lowest impact on the determination of the subject's health status. The main application of this new technology is the adoption of smart solutions (e.g., a mobile app) to customize the stratification of patients admitted to emergency departments (ED)s after a syncope event. Indeed, the adoption of these smart solutions gives the opportunity to customize risk stratification according to the specific clinical case (i.e., the patient's health status) and the physician's decision-making process (i.e., the desired levels of sensitivity and specificity). Moreover, a decision-making process based on these smart solutions might ensure a more effective use of available resources, improving the management of syncope patients and reducing the cost of inappropriate treatment and hospitalization.

Outcomes of non-invasive diagnostic modalities for the detection of coronary artery disease: network meta-analysis of diagnostic randomised controlled trials

OBJECTIVE

To evaluate differences in downstream testing, coronary revascularisation, and clinical outcomes following non-invasive diagnostic modalities used to detect coronary artery disease.

DESIGN

Systematic review and network meta-analysis.

DATA SOURCES

Medline, Medline in process, Embase, Cochrane Library for clinical trials, PubMed, Web of Science, SCOPUS, WHO International Clinical Trials Registry Platform, and Clinicaltrials.gov.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Diagnostic randomised controlled trials comparing non-invasive diagnostic modalities in patients presenting with symptoms suggestive of low risk acute coronary syndrome or stable coronary artery disease.

DATA SYNTHESIS

A random effects network meta-analysis synthesised available evidence from trials evaluating the effect of non-invasive diagnostic modalities on downstream testing and patient oriented outcomes in patients with suspected coronary artery disease. Modalities included exercise electrocardiograms, stress echocardiography, single photon emission computed tomography-myocardial perfusion imaging, real time myocardial contrast echocardiography, coronary computed tomographic angiography, and cardiovascular magnetic resonance. Unpublished outcome data were obtained from 11 trials.

RESULTS

18 trials of patients with low risk acute coronary syndrome (n=11 329) and 12 trials of those with suspected stable coronary artery disease (n=22 062) were included. Among patients with low risk acute coronary syndrome, stress echocardiography, cardiovascular magnetic resonance, and exercise electrocardiograms resulted in fewer invasive referrals for coronary angiography than coronary computed tomographic angiography (odds ratio 0.28 (95% confidence interval 0.14 to 0.57), 0.32 (0.15 to 0.71), and 0.53 (0.28 to 1.00), respectively). There was no effect on the subsequent risk of myocardial infarction, but estimates were imprecise. Heterogeneity and inconsistency were low. In patients with suspected stable coronary artery disease, an initial diagnostic strategy of stress echocardiography or single photon emission computed tomography-myocardial perfusion imaging resulted in fewer downstream tests than coronary computed tomographic angiography (0.24 (0.08 to 0.74) and 0.57 (0.37 to 0.87), respectively). However, exercise electrocardiograms yielded the highest downstream testing rate. Estimates for death and myocardial infarction were imprecise without clear discrimination between strategies.

CONCLUSIONS

For patients with low risk acute coronary syndrome, an initial diagnostic strategy of stress echocardiography or cardiovascular magnetic resonance is associated with fewer referrals for invasive coronary angiography and revascularisation procedures than non-invasive anatomical testing, without apparent impact on the future risk of myocardial infarction. For suspected stable coronary artery disease, there was no clear discrimination between diagnostic strategies regarding the subsequent need for invasive coronary angiography, and differences in the risk of myocardial infarction cannot be ruled out.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registry no CRD42016049442.

Patient's Experience in Pediatric Primary Immunodeficiency Disorders: Computerized Classification of Questionnaires

Introduction

Primary immunodeficiency disorders (PIDs) are a heterogeneous group of more than 200 rare diseases. Timely diagnosis is of uttermost importance. Therefore, we aimed to develop a diagnostic questionnaire with computerized pattern-recognition in order to support physicians to identify suspicious patient histories.

Materials and methods

Standardized interviews were conducted with guardians of children with PID. The questionnaire based on parental observations was developed using Colaizzis’ framework for content analysis. Answers from 64 PID patients and 62 controls were analyzed by data mining methods in order to make a diagnostic prediction. Performance was evaluated by k-fold stratified cross-validation.

Results

The diagnostic support tool achieved a diagnostic sensitivity of up to 98%. The analysis of 12 interviews revealed 26 main phenomena observed by parents in the pre-diagnostic period. The questions were systematically phrased and selected resulting in a 36-item questionnaire. This was answered by 126 patients with or without PID to evaluate prediction. Item analysis revealed significant questions.

Discussion

Our approach proved suitable for recognizing patterns and thus differentiates between observations of PID patients and control groups. These findings provide the basis for developing a tool supporting physicians to consider a PID with a questionnaire. These data support the notion that patient’s experience is a cornerstone in the diagnostic process.

[Intelligent systems tools in the diagnosis of acute coronary syndromes: A systemic review]

BACKGROUND

Acute myocardial infarction is the leading cause of non-communicable deaths worldwide. Its diagnosis is a highly complex task, for which modelling through automated methods has been attempted. A systematic review of the literature was performed on diagnostic tests that applied intelligent systems tools in the diagnosis of acute coronary syndromes.

METHODS

A systematic review of the literature is presented using Medline, Embase, Scopus, IEEE/IET Electronic Library, ISI Web of Science, Latindex and LILACS databases for articles that include the diagnostic evaluation of acute coronary syndromes using intelligent systems. The review process was conducted independently by 2 reviewers, and discrepancies were resolved through the participation of a third person. The operational characteristics of the studied tools were extracted.

RESULTS

A total of 35 references met the inclusion criteria. In 22 (62.8%) cases, neural networks were used. In five studies, the performances of several intelligent systems tools were compared. Thirteen studies sought to perform diagnoses of all acute coronary syndromes, and in 22, only infarctions were studied. In 21 cases, clinical and electrocardiographic aspects were used as input data, and in 10, only electrocardiographic data were used. Most intelligent systems use the clinical context as a reference standard. High rates of diagnostic accuracy were found with better performance using neural networks and support vector machines, compared with statistical tools of pattern recognition and decision trees.

CONCLUSIONS

Extensive evidence was found that shows that using intelligent systems tools achieves a greater degree of accuracy than some clinical algorithms or scales and, thus, should be considered appropriate tools for supporting diagnostic decisions of acute coronary syndromes.

Using neural networks and just nine patient-reportable factors of screen for AMI

The study investigated the effect of different input selections on the performance of artificial neural networks in screening for acute myocardial infarction (AMI) in Malaysian patients complaining of chest pain. We used hospital data to create neural networks with four input selections and used these to diagnose AMI. A 10-fold cross-validation and committee approach was used. All the neural networks using various input selections outperformed a multiple logistic regression model, although the difference was not statistically significant. The neural networks achieved an area under the ROC curve of 0.792 using nine inputs, whereas multiple logistic regression achieved 0.739 using 64 inputs. Sensitivity levels of over 90 per cent were achieved using low output threshold levels. Specificity levels of over 90 per cent were achieved using threshold levels of 0.4-0.5. Thus neural networks can perform as well as multiple logistic regression models even when using far fewer inputs.

Artificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective

The primary goal of Emergency Department (ED) physicians is to discriminate between individuals at low risk, who can be safely discharged, and patients at high risk, who require prompt hospitalization. The problem of correctly classifying patients is an issue involving not only clinical but also managerial aspects, since reducing the rate of admission of patients to EDs could dramatically cut costs. Nevertheless, a trade-off might arise due to the need to find a balance between economic interests and the health conditions of patients. This work considers patients in EDs after a syncope event and presents a comparative analysis between two models: a multivariate logistic regression model, as proposed by the scientific community to stratify the expected risk of severe outcomes in the short and long run, and Artificial Neural Networks (ANNs), an innovative model. The analysis highlights differences in correct classification of severe outcomes at 10 days (98.30% vs. 94.07%) and 1 year (97.67% vs. 96.40%), pointing to the superiority of Neural Networks. According to the results, there is also a significant superiority of ANNs in terms of false negatives both at 10 days (3.70% vs. 5.93%) and at 1 year (2.33% vs. 10.07%). However, considering the false positives, the adoption of ANNs would cause an increase in hospital costs, highlighting the potential trade-off which policy makers might face.

Proteomics in atherothrombosis: a future perspective

Atherothrombosis is the primary cause of death in Western countries. The cellular and molecular mechanisms underlying atherosclerosis remain widely unknown. The complex nature of atherosclerotic cardiovascular diseases demands the development of novel technologies that enable discovery of new biomarkers for early disease detection and risk stratification, which may predict clinical outcome. In this review, we outline potential sources and recent proteomic approaches that could be applied in the search of novel biomarkers of cardiovascular risk. In addition, we describe some issues raised in relation to the application of proteomics to blood samples, as well as two novel emerging concepts, such as peptidomics and population proteomics. In the future, the use of high-throughput techniques (proteomic, genomics and metabolomics) will potentially identify novel patterns of biomarkers, which, along with traditional risk factors and imaging techniques, could help to target vulnerable patients and monitor the beneficial effects of pharmacological agents.

Diagnostic accuracy of prehospital clinical prediction models to identify short-term outcomes in patients with acute coronary syndromes: a systematic review

BACKGROUND

Although cardiac risk prediction is widely used in various clinical settings, its potential role in enhancing prehospital triage is yet to be understood.

OBJECTIVE

To systematically review the diagnostic accuracy of short-term clinical prediction models for potential use in a prehospital population with suspected acute coronary syndrome.

METHODS

Eleven electronic medical databases were searched from 1990 to the end of August 2010 for all English-language observational and interventional studies. An online search strategy tool was used to identify grey-literature studies. Eligibility criteria were: 1) an unselected population of adult acute coronary syndrome patients; 2) recruited within the Emergency Department or Emergency Medical Services; 3) reported multivariate analysis encompassing patient history or physical examination; 4) reported short-term outcome measures; 5) were not solely computer protocols; and 6) were not reliant on tests unavailable out of the hospital. Data extraction was conducted by a single reviewer and verified by a second reviewer. Study quality was assessed independently by two reviewers using a validated quality assessment tool.

RESULTS

A total of seven clinical prediction models were identified. Only two models reported were derived from a prehospital study population. Six clinical prediction models described good discriminate abilities (c-statistic) of 0.72 to 0.87. Among the range of independent predictors identified, electrocardiogram abnormalities, age, heart rate, and systolic blood pressure provided the strongest prognostic information.

CONCLUSION

The models identified provided reasonable diagnostic accuracy for determining short-term outcomes. Methodological weaknesses and variability in the populations investigated limit their use in clinical practice.

Emergency physician estimates of the probability of acute coronary syndrome in a cohort of patients enrolled in a study of coronary computed tomographic angiography

INTRODUCTION

Little information exists regarding how accurately emergency physicians (EPs) predict the probability of acute coronary syndrome (ACS). Our objective was to determine if EPs can accurately predict ACS in a prospectively identified cohort of emergency department (ED) patients who met enrolment criteria for a study of coronary computed tomographic angiography (CCTA) and were admitted for a "rule out ACS" protocol.

METHODS

A prospective observational pilot study in an academic medical centre was carried out. EPs caring for patients with chest pain provided whole-number estimates of the probability of ACS after clinical review. This substudy was part of the now published Rule Out Myocardial Infarction/Ischemia Using Computer Assisted Tomography (ROMICAT) study, a study of CCTA and admission of patients for a rule out ACS protocol after a nondiagnostic evaluation. Predictions were grouped into probability groups based on the validated Goldman criteria. ACS was determined by an adjudication committee using American Heart Association/American College of Cardiology/European Society of Cardiology guidelines.

RESULTS

A total of 334 predictions were obtained for a study population with a mean age of 54 (SD 12) years, 63% of whom were male. There were 35 ACS events. EPs predicted ACS better than by chance, and increasingly higher estimates were associated with a higher incidence of ACS (p = 0.0004). The percentage of patients with ACS was 0%, 6%, 7%, and 17%, respectively, for very low, low, intermediate, and high probability groups. EPs' estimates had a sensitivity of 63% using a > 20% probability of ACS to define a positive test. Lowering this threshold to > 7% to define a test as positive increased the sensitivity of physician estimates to 89% but lowered specificity from 65% to 24%.

CONCLUSION

Our data suggest that for a selected ED cohort meeting eligibility criteria for a study of CCTA, EPs predict ACS better than by chance, with an increasing proportion of patients proving to have ACS with increasing probability estimates. Lowering the estimate threshold does not result in an overall sensitivity level that is sufficient to send patients home from the ED and is associated with a poor specificity.

Comparison of traditional cardiovascular risk models and coronary atherosclerotic plaque as detected by computed tomography for prediction of acute coronary syndrome in patients with acute chest pain

OBJECTIVES

The objective was to determine the association of four clinical risk scores and coronary plaque burden as detected by computed tomography (CT) with the outcome of acute coronary syndrome (ACS) in patients with acute chest pain. The hypothesis was that the combination of risk scores and plaque burden improved the discriminatory capacity for the diagnosis of ACS.

METHODS

The study was a subanalysis of the Rule Out Myocardial Infarction Using Computer-Assisted Tomography (ROMICAT) trial-a prospective observational cohort study. The authors enrolled patients presenting to the emergency department (ED) with a chief complaint of acute chest pain, inconclusive initial evaluation (negative biomarkers, nondiagnostic electrocardiogram [ECG]), and no history of coronary artery disease (CAD). Patients underwent contrast-enhanced 64-multidetector-row cardiac CT and received standard clinical care (serial ECG, cardiac biomarkers, and subsequent diagnostic testing, such as exercise treadmill testing, nuclear stress perfusion imaging, and/or invasive coronary angiography), as deemed clinically appropriate. The clinical providers were blinded to CT results. The chest pain score was calculated and the results were dichotomized to ≥10 (high-risk) and <10 (low-risk). Three risk scores were calculated, Goldman, Sanchis, and Thrombolysis in Myocardial Infarction (TIMI), and each patient was assigned to a low-, intermediate-, or high-risk category. Because of the low number of subjects in the high-risk group, the intermediate- and high-risk groups were combined into one. CT images were evaluated for the presence of plaque in 17 coronary segments. Plaque burden was stratified into none, intermediate, and high (zero, one to four, and more than four segments with plaque). An outcome panel of two physicians (blinded to CT findings) established the primary outcome of ACS (defined as either an acute myocardial infarction or unstable angina) during the index hospitalization (from the presentation to the ED to the discharge from the hospital). Logistic regression modeling was performed to examine the association of risk scores and coronary plaque burden to the outcome of ACS. Unadjusted models were individually fitted for the coronary plaque burden and for Goldman, Sanchis, TIMI, and chest pain scores. In adjusted analyses, the authors tested whether the association between risk scores and ACS persisted after controlling for the coronary plaque burden. The prognostic discriminatory capacity of the risk scores and plaque burden for ACS was assessed using c-statistics. The differences in area under the receiver-operating characteristic curve (AUC) and c-statistics were tested by performing the -2 log likelihood ratio test of nested models. A p value <0.05 was considered statistically significant.

RESULTS

Among 368 subjects, 31 (8%) subjects were diagnosed with ACS. Goldman (AUC = 0.61), Sanchis (AUC = 0.71), and TIMI (AUC = 0.63) had modest discriminatory capacity for the diagnosis of ACS. Plaque burden was the strongest predictor of ACS (AUC = 0.86; p < 0.05 for all comparisons with individual risk scores). The combination of plaque burden and risk scores improved prediction of ACS (plaque + Goldman AUC = 0.88, plaque + Sanchis AUC = 0.90, plaque + TIMI AUC = 0.88; p < 0.01 for all comparisons with coronary plaque burden alone).

CONCLUSIONS

Risk scores (Goldman, Sanchis, TIMI) have modest discriminatory capacity and coronary plaque burden has good discriminatory capacity for the diagnosis of ACS in patients with acute chest pain. The combined information of risk scores and plaque burden significantly improves the discriminatory capacity for the diagnosis of ACS.

One-year outcomes following coronary computerized tomographic angiography for evaluation of emergency department patients with potential acute coronary syndrome

OBJECTIVES

Coronary computerized tomographic angiography (CTA) has high correlation with cardiac catheterization and has been shown to be safe and cost-effective when used for rapid evaluation of low-risk chest pain patients from the emergency department (ED). The long-term outcome of patients discharged from the ED with negative coronary CTA has not been well studied.

METHODS

The authors prospectively evaluated consecutive low- to intermediate-risk patients who received coronary CTA in the ED for evaluation of a potential acute coronary syndrome (ACS). Patients with cocaine use, known cancer, and significant comorbidity reducing life expectancy and those found to have significant disease (stenosis > or = 50% or ejection fraction < 30%) were excluded. Demographics, medical and cardiac history, labs, and electrocardiogram (ECG) results were collected. Patients were followed by telephone contact and record review for 1 year. The main outcome was 1-year cardiovascular death or nonfatal acute myocardial infarction (AMI).

RESULTS

Of 588 patients who received coronary CTA in the ED, 481 met study criteria. They had a mean (+/-SD) age of 46.1 (+/-8.8) years, 63% were black or African American, and 60% were female. There were 53 patients (11%) rehospitalized and 51 patients (11%) who received further diagnostic testing (stress or catheterization) over the subsequent year. There was one death (0.2%; 95% confidence interval [CI] = 0.01% to 1.15%) with unclear etiology, no AMI (0%; 95% CI = 0 to 0.76%), and no revascularization procedures (0%; 95% CI = 0 to 0.76%) during this time period.

CONCLUSIONS

Low- to intermediate-risk patients with a Thrombosis In Myocardial Infarction (TIMI) score of 0 to 2 who present to the ED with potential ACS and have a negative coronary CTA have a very low likelihood of cardiovascular events over the ensuing year.

Emergency physician high pretest probability for acute coronary syndrome correlates with adverse cardiovascular outcomes

OBJECTIVES

The value of unstructured physician estimate of risk for disease processes, other than acute coronary syndrome (ACS), has been demonstrated. The authors sought to evaluate the predictive value of unstructured physician estimate of risk for ACS in emergency department (ED) patients without obvious initial evidence of a cardiac event.

METHODS

This was a post hoc secondary analysis of the Internet Tracking Registry for Acute Coronary Syndromes (i*trACS), a prospectively collected multicenter data registry of patients over the age of 18 years presenting to the ED with symptoms of ACS between 1999 and 2001. In this registry, following patient history, physical exam, and electrocardiogram (ECG), the unstructured treating physician estimate of risk was recorded. A 30-day follow-up and a medical record review were used to determine rates of adverse cardiac events, death, myocardial infarction (MI), or revascularization procedure. The analysis included all patients with nondiagnostic ECG changes, normal initial biomarkers, and a non-MI initial impression from the registry and excluded those without complete data or who were lost to follow-up. Data were stratified by unstructured physician risk estimate: noncardiac, low risk, high risk, or unstable angina.

RESULTS

Of 15,608 unique patients in the registry, 10,145 met inclusion/exclusion criteria. Patients were defined as having unstable angina in 6.0% of cases; high risk, 23.5% of cases; low risk, 44.2%; and noncardiac, 26.3% of cases. Adverse cardiac event rates had an inverse relationship, decreasing from 22.0% (95% confidence interval [CI] = 18.8% to 25.6%) for unstable angina, 10.2% (95% CI = 9.0% to 11.5%) for those stratified as high risk, 2.2% (95% CI = 1.8% to 2.6%) for low risk, and to 1.8% (95% CI = 1.4% to 2.4%) for noncardiac. The relative risk (RR) of an adverse cardiac event for those with an initial label of unstable angina compared to those with a low-risk designation was 10.2 (95% CI = 8.0 to 13.0). The RR of an event for those with a high-risk initial impression compared to those with a low-risk initial impression was 4.7 (95% CI = 3.8 to 5.9). The risk of an event among those with a low-risk initial impression was the same as for those with a noncardiac initial impression (RR = 0.83, 95% CI = 0.6 to 1.2).

CONCLUSIONS

In ED patients without obvious initial evidence of a cardiac event, unstructured emergency physician (EP) estimate of risk correlates with adverse cardiac outcomes.

The association between emergency department crowding and adverse cardiovascular outcomes in patients with chest pain

OBJECTIVES

While emergency department (ED) crowding is a worldwide problem, few studies have demonstrated associations between crowding and outcomes. The authors examined whether ED crowding was associated with adverse cardiovascular outcomes in patients with chest pain syndromes (chest pain or related complaints of possible cardiac origin).

METHODS

A retrospective analysis was performed for patients >or=30 years of age with chest pain syndrome admitted to a tertiary care academic hospital from 1999 through 2006. The authors compared rates of inpatient adverse outcomes from ED triage to hospital discharge, defined as delayed acute myocardial infarction (AMI), heart failure, hypotension, dysrhythmias, and cardiac arrest, which occurred after ED arrival using five separate crowding measures.

RESULTS

Among 4,574 patients, 251 (4%) patients developed adverse outcomes after ED arrival; 803 (18%) had documented acute coronary syndrome (ACS), and of those, 273 (34%) had AMI. Compared to less crowded times, ACS patients experienced more adverse outcomes at the highest waiting room census (odds ratio [OR] = 3.7, 95% confidence interval [CI] = 1.3 to 11.0) and patient-hours (OR = 5.2, 95% CI = 2.0 to 13.6) and trended toward more adverse outcomes during time of high ED occupancy (OR = 3.1, 95% CI = 1.0 to 9.3). Adverse outcomes were not significantly more frequent during times with the highest number of admitted patients (OR = 1.6, 95% CI = 0.6 to 4.1) or the highest trailing mean length of stay (LOS) for admitted patients transferred to inpatient beds within 6 hours (OR = 1.5, 95% CI = 0.5 to 4.0). Patients with non-ACS chest pain experienced more adverse outcomes during the highest waiting room census (OR = 3.5, 95% CI = 1.4 to 8.4) and patient-hours (OR = 4.3, 95% CI = 2.6 to 7.3), but not occupancy (OR = 1.8, 95% CI = 0.9 to 3.3), number of admitted patients (OR = 0.6, 95% CI 0.4 to 1.1), or trailing LOS for admitted patients (OR = 1.2, 95% CI = 0.6 to 2.0).

CONCLUSIONS

There was an association between some measures of ED crowding and a higher risk of adverse cardiovascular outcomes in patients with both ACS-related and non-ACS-related chest pain syndrome.

Prediction of asymptomatic cirrhosis in chronic hepatitis C patients: accuracy of artificial neural networks compared with logistic regression models

OBJECTIVE

Models based on logistic regression analysis are proposed as noninvasive tools to predict cirrhosis in chronic hepatitis C (CHC) patients. However, none showed to be sufficiently accurate to replace liver biopsy. Artificial neural networks (ANNs), providing a prediction based on nonlinear algorithms, can improve the diagnosis of cirrhosis, a syndrome characterized by complex, nonlinear biological alterations. We compared ANNs with two logistic regression analysis-based models in predicting CHC histologically proven cirrhosis.

METHODS

Liver biopsy was obtained in CHC patients of two different cohorts (an internal cohort including 244 patients and an external cohort including 220 patients). One hundred and forty-four patients from the internal cohort served as a training set to construct ANNs and a logistic regression model (LOGIT). These two models and the aspartate aminotransferase-to-platelet ratio index (APRI) were tested in the remaining 100 patients (internal validation set) and in the external cohort (external validation set). Diagnostic performances were evaluated by standard indices of accuracy.

RESULTS

In the internal validation set, ANNs, LOGIT, and APRI showed similar discrimination powers (0.88, 0.87, and 0.87 respectively). However, ANNs showed the best positive predictive value (0.86 vs. 0.67 and 0.56) and positive likelihood ratio (40.2 vs. 13.4 and 8.4). In the external validation set, the discrimination power of ANNs (0.76) was significantly higher than those of LOGIT (0.67) and APRI (0.67).

CONCLUSION

Compared to conventional models, ANNs performance in predicting CHC cirrhosis is slightly better and more reproducible.

Diagnostic accuracy of clinical prediction rules to exclude acute coronary syndrome in the emergency department setting: a systematic review

OBJECTIVE

We sought to determine the diagnostic accuracy of clinical prediction rules to exclude acute coronary syndrome (ACS) in the emergency department (ED) setting.

METHODS

We searched MEDLINE, EMBASE, Web of Science and the Cochrane Database of Systematic Reviews. We contacted content experts to identify additional articles for review. Reference lists of included studies were hand searched. We selected articles for review based on the following criteria: 1) enrolled consecutive ED patients; 2) incorporated variables from the history or physical examination, electrocardiogram and cardiac biomarkers; 3) did not incorporate cardiac stress testing or coronary angiography into prediction rule; 4) based on original research; 5) prospectively derived or validated; 6) did not require use of a computer; and 7) reported sufficient data to construct a 2 x 2 contingency table. We assessed study quality and extracted data independently and in duplicate using a standardized data extraction form.

RESULTS

Eight studies met inclusion criteria, encompassing 7937 patients. None of the studies verified the prediction rule with a reference standard on all or a random sample of patients. Six studies did not report blinding prediction rule assessors to reference standard results, and vice versa. Three prediction rules were prospectively validated. Sensitivities and specificities ranged from 94% to 100% and 13% to 57%, and positive and negative likelihood ratios from 1.1 to 2.2 and 0.01 to 0.17, respectively.

CONCLUSION

Current prediction rules for ACS have substantial methodological limitations and have not been successfully implemented in the clinical setting. Future methodologically sound studies are needed to guide clinical practice.

Coronary computed tomographic angiography for rapid discharge of low-risk patients with potential acute coronary syndromes

STUDY OBJECTIVE

Coronary computed tomographic (CT) angiography has excellent performance characteristics relative to coronary angiography and exercise or pharmacologic stress testing. We hypothesize that coronary CT angiography can identify a cohort of emergency department (ED) patients with a potential acute coronary syndrome who can be safely discharged with a less than 1% risk of 30-day cardiovascular death or nonfatal myocardial infarction.

METHODS

We conducted a prospective cohort study at an urban university hospital ED that enrolled consecutive patients with potential acute coronary syndromes and a low TIMI risk score who presented to the ED with symptoms suggestive of a potential acute coronary syndrome and received a coronary CT angiography. Our intervention was either immediate coronary CT angiography in the ED or after a 9- to 12-hour observation period that included cardiac marker determinations, depending on time of day. The main clinical outcome was 30-day cardiovascular death or nonfatal myocardial infarction.

RESULTS

Five hundred sixty-eight patients with potential acute coronary syndrome were evaluated: 285 of these received coronary CT angiography immediately in the ED and 283 received coronary CT angiography after a brief observation period. Four hundred seventy-six (84%) were discharged home after coronary CT angiography. During the 30-day follow-up period, no patients died of a cardiovascular event (0%; 95% confidence interval [CI] 0% to 0.8%) or sustained a nonfatal myocardial infarction (0%; 95% CI 0 to 0.8%).

CONCLUSION

ED patients with symptoms concerning for a potential acute coronary syndrome with a low TIMI risk score and a nonischemic initial ECG result can be safely discharged home after a negative coronary CT angiography test result.

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.

Automatic covariate selection in logistic models for chest pain diagnosis: a new approach

A newly established method for optimizing logistic models via a minorization-majorization procedure is applied to the problem of diagnosing acute coronary syndromes (ACS). The method provides a principled approach to the selection of covariates which would otherwise require the use of a suboptimal method owing to the size of the covariate set. A strategy for building models is proposed and two models optimized for performance and for simplicity are derived via 10-fold cross-validation. These models confirm that a relatively small set of covariates including clinical and electrocardiographic features can be used successfully in this task. The performance of the models is comparable with previously published models using less principled selection methods. The models prove to be portable when tested on data gathered from three other sites. Whilst diagnostic accuracy and calibration diminishes slightly for these new settings, it remains satisfactory overall. The prospect of building predictive models that are as simple as possible for a required level of performance is valuable if data-driven decision aids are to gain wide acceptance in the clinical situation owing to the need to minimize the time taken to gather and enter data at the bedside.

A study to derive a clinical decision rule for triage of emergency department patients with chest pain: design and methodology

BACKGROUND

Chest pain is the second most common chief complaint in North American emergency departments. Data from the U.S. suggest that 2.1% of patients with acute myocardial infarction and 2.3% of patients with unstable angina are misdiagnosed, with slightly higher rates reported in a recent Canadian study (4.6% and 6.4%, respectively). Information obtained from the history, 12-lead ECG, and a single set of cardiac enzymes is unable to identify patients who are safe for early discharge with sufficient sensitivity. The 2007 ACC/AHA guidelines for UA/NSTEMI do not identify patients at low risk for adverse cardiac events who can be safely discharged without provocative testing. As a result large numbers of low risk patients are triaged to chest pain observation units and undergo provocative testing, at significant cost to the healthcare system. Clinical decision rules use clinical findings (history, physical exam, test results) to suggest a diagnostic or therapeutic course of action. Currently no methodologically robust clinical decision rule identifies patients safe for early discharge.

METHODS/DESIGN

The goal of this study is to derive a clinical decision rule which will allow emergency physicians to accurately identify patients with chest pain who are safe for early discharge. The study will utilize a prospective cohort design. Standardized clinical variables will be collected on all patients at least 25 years of age complaining of chest pain prior to provocative testing. Variables strongly associated with the composite outcome acute myocardial infarction, revascularization, or death will be further analyzed with multivariable analysis to derive the clinical rule. Specific aims are to: i) apply standardized clinical assessments to patients with chest pain, incorporating results of early cardiac testing; ii) determine the inter-observer reliability of the clinical information; iii) determine the statistical association between the clinical findings and the composite outcome; and iv) use multivariable analysis to derive a highly sensitive clinical decision rule to guide triage decisions.

DISCUSSION

The study will derive a highly sensitive clinical decision rule to identify low risk patients safe for early discharge. This will improve patient care, lower healthcare costs, and enhance flow in our busy and overcrowded emergency departments.

The elder patient with suspected acute coronary syndromes in the emergency department

OBJECTIVES

To describe the evaluation and outcomes of elder patients with suspected acute coronary syndromes (ACS) presenting to the emergency department (ED).

METHODS

This was a post hoc analysis of the Internet Tracking Registry for Acute Coronary Syndromes (i*trACS) registry, which had 17,713 ED visits for suspected ACS. First visits from the United States with nonmissing patient demographics, 12-lead electrocardiogram results, and clinical history were included in the analysis. Those who used cocaine or amphetamines or left the ED against medical advice were excluded. Elder was defined as age 75 years or older. ACS was defined by 30-day revascularization, Diagnosis-related Group codes, or death within 30 days with positive cardiac biomarkers at index hospitalization. Multivariable logistic regression analyses were performed to determine the association between being elder and 1) 30-day all-cause mortality, 2) ACS, 3) diagnostic tests ordered, and 4) disposition. Multivariable logistic regression was also performed to determine which clinical variables were associated with ACS in elder and nonelder patients.

RESULTS

A total of 10,126 patients with suspected ACS presenting to the ED were analyzed. For patients presenting to the ED, being elder was independently associated with ACS and all-cause 30-day mortality, with adjusted odds ratios of 1.8 (95% confidence interval [CI] = 1.5 to 2.2) and 2.6 (95% CI = 1.6 to 4.3), respectively. Elder patients were more likely to be admitted to the hospital (adjusted odds ratio, 2.2; 95% CI = 1.8 to 2.6), but there were no differences in the rates of cardiac catheterization and noninvasive stress cardiac imaging. Different clinical variables were associated with ACS in elder and nonelder patients. Chest pain as chief complaint, typical chest pain, and previous history of coronary artery disease were significantly associated with ACS in nonelder patients but were not associated with ACS in elder patients. Male gender and left arm pain were associated with ACS in both elder and nonelder patients.

CONCLUSIONS

Elder patients who present to the ED with suspected ACS represent a population at high risk for ACS and 30-day mortality. Elders are more likely to be admitted to the hospital, but despite an increased risk for adverse events, they have similar odds of receiving a diagnostic test, such as stress cardiac imaging or cardiac catheterization, compared with nonelder patients. Different clinical variables are associated with ACS, and clinical prediction rules utilizing presenting symptoms should consider the effect modification of age.

TIMI risk score: does it work equally well in both males and females?

OBJECTIVE

The TIMI (Thrombolysis In Myocardial Infarction) risk score is a seven item risk stratification tool derived from trials of patients with non-ST segment elevation acute coronary syndromes (ACS) that has been validated in emergency department (ED) patients with potential ACS. We hypothesised that it might have different prognostic abilities in male and female patients.

METHODS

This was a prospective cohort study of ED patients with potential ACS. Data included demographics, medical and cardiac history, and components of the TIMI risk score. Investigators followed the hospital course daily. The main outcome was death, acute myocardial infarction (AMI), or revascularisation within 30 days as stratified by TIMI risk score and compared between genders using chi2 tests.

RESULTS

There were 2022 patients enrolled: 1204 (60%) females and 818 (40%) males. The incidence of 30 day death, AMI, revascularisation (n = 168) according to TIMI score is as follows (female vs male): TIMI 0 (n = 670), 1.6% vs 2.0%, p = 0.2; TIMI 1 (n = 525), 4.6% vs 8.5%, p = 0.02; TIMI 2 (n = 378), 6.3% vs 10.4%, p = 0.05; TIMI 3 (n = 234), 6.5% vs 24.6%, p<0.001; TIMI 4 (n = 157), 22.7% vs 24.4%, p = 0.15; TIMI 5 (n = 52), 35.5% vs 39.1%, p = 0. 2; TIMI 6 or 7 (n = 6), 33.3% vs 66.7%, p = 1.0. The relationship between TIMI score and outcome was highly significant (p<0.001) for each gender; however, males tended to have worse outcomes at lower TIMI risk scores.

CONCLUSIONS

The TIMI risk score successfully risk stratifies both males and females with potential ACS at the time of ED presentation; however, males have worse outcomes at lower TIMI scores than females.

Best leads in the standard electrocardiogram for the emergency detection of acute coronary syndrome

BACKGROUND AND PURPOSE

The purpose of this study was to determine which leads in the standard 12-lead electrocardiogram (ECG) are the best for detecting acute coronary syndrome (ACS) among chest pain patients in the emergency department.

METHODS

Neural network classifiers were used to determine the predictive capability of individual leads and combinations of leads from 862 ECGs from chest pain patients in the emergency department at Lund University Hospital.

RESULTS

The best individual lead was aVL, with an area under the receiver operating characteristic curve of 75.5%. The best 3-lead combination was III, aVL, and V2, with a receiver operating characteristic area of 82.0%, compared with the 12-lead ECG performance of 80.5%.

CONCLUSIONS

Our results indicate that leads III, aVL, and V2 are sufficient for computerized prediction of ACS. The present results are likely important in situations where the 12-lead ECG is impractical and for the creation of clinical decision support systems for ECG prediction of ACS.

Relationship between a clear-cut alternative noncardiac diagnosis and 30-day outcome in emergency department patients with chest pain

BACKGROUND

Accurate identification of patients with acute coronary syndromes (ACSs) in the emergency department (ED) remains problematic. Studies have not been able to identify a cohort of patients that are safe for immediate ED discharge; however, prior studies have not examined the utility of a clear-cut alternative noncardiac diagnosis.

OBJECTIVES

To compare the 30-day event rate in ED chest pain patients who were diagnosed with a clear-cut alternative noncardiac diagnosis with the 30-day event rate in the cohort of patients in whom a definitive diagnosis could not be made in the ED.

METHODS

This was a prospective cohort study of consecutive ED patients with potential ACS. Data included demographics, medical and cardiac history, laboratory and electrocardiogram results, and whether or not the treating physician ascribed the condition to a clear-cut alternative noncardiac diagnosis. The main outcome was death, acute myocardial infarction (AMI), or revascularization within 30 days, as determined by phone follow-up or medical record review.

RESULTS

The investigators enrolled 1,995 patients in the ED who had potential ACSs. Overall, 77 had a final hospital diagnosis of AMI (4%). Within 30 days, 73 patients received revascularization (4%), and 26 died (1%). There were 599 (30%) patients given a clear-cut alternative noncardiac diagnosis. Comparing the patients with a clear-cut alternative noncardiac diagnosis with those without an obvious noncardiac diagnosis, the presence of a clear-cut alternative noncardiac diagnosis was associated with a reduced risk of an in-hospital triple-composite endpoint (death, MI, and revascularization), with a risk ratio of 0.32 (95% confidence interval [CI] = 0.19 to 0.55) and 30-day triple-composite endpoint with a risk ratio of 0.45 (95% CI = 0.29 to 0.69); however, patients with a clear-cut alternative noncardiac diagnosis still had a 4% event rate at 30 days (95% CI = 2.4% to 5.6%).

CONCLUSIONS

In the ED chest pain patient, the presence of a clear-cut alternative noncardiac diagnosis reduces the likelihood of a composite outcome of death and cardiovascular events within 30 days. However, it does not reduce the event rate to an acceptable level to allow ED discharge of these patients.

The role of cardiac risk factor burden in diagnosing acute coronary syndromes in the emergency department setting

STUDY OBJECTIVE

We seek to determine whether cardiac risk factor burden (defined as the number of conventional cardiac risk factors present) is useful for the diagnosis of acute coronary syndromes in the emergency department (ED) setting.

METHODS

This was a post hoc analysis of the Internet Tracking Registry of Acute Coronary Syndromes (i*trACS) registry, which had 17,713 ED visits for suspected acute coronary syndromes. First visit for US patients who were not cocaine or amphetamine users, who did not leave against medical advice, and for whom ECG and demographic data were complete were included. Acute coronary syndrome was defined by 30-day revascularization, diagnostic-related group codes, or death within 30 days, with positive cardiac biomarkers at index hospitalization. Cardiac risk factors were diabetes, hypertension, smoking, hypercholesterolemia, and family history of coronary artery disease. Cardiac risk factor burden was defined as the number of risk factors present. Because multiple logistic regression analysis revealed that age modified the relationship between cardiac risk factor burden and acute coronary syndromes, a stratified analysis was performed for 3 age categories: younger than 40, 40 to 65, and older than 65 years. Positive likelihood ratios and negative likelihood ratios with their 95% confidence intervals (CIs) were calculated for each total risk factor cutoff.

RESULTS

Of 10,806 eligible patients, 871 (8.1%) had acute coronary syndromes. In patients younger than 40 years, having no risk factors had a negative likelihood ratio of 0.17 (95% CI 0.04 to 0.66), and having 4 or more risk factors had a positive likelihood ratio of 7.39 (95% CI 3.09 to 17.67). In patients between 40 and 65 years of age, having no risk factors had a negative likelihood ratio of 0.53 (95% CI 0.40 to 0.71), and having 4 or more risk factors had a positive likelihood ratio of 2.13 (95% CI 1.66 to 2.73). In patients older than 65 years, having no risk factors had a negative likelihood ratio of 0.96 (95% CI 0.74 to 1.23), and having 4 or more risk factors had a positive likelihood ratio of 1.09 (95% CI 0.64 to 1.62).

CONCLUSION

Cardiac risk factor burden has limited clinical value in diagnosing acute coronary syndromes in the ED setting, especially in patients older than 40 years.

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.

Analysis of factors affecting emergency physicians?? decisions in the management of chest pain patients

OBJECTIVE

The aim of this study was to determine the factors most affecting emergency physicians' decisions in the management of chest pain patients.

METHODS

This prospective randomized cross-sectional study was carried out between March 2004 and September 2004 in an urban university hospital emergency department. Residents collected data on patients' demographic features, chest pain characteristics, electrocardiography, cardiac enzymes and outcome of patients.

RESULTS

Five hundred and sixty-two patients were enrolled in the study; 389 (69.2%) patients were classified as having cardiac chest pain. Of the 389 patients suggested to have cardiac chest pain, 369 (94.4%) were classified as probable acute coronary syndrome; 286 (50.9%) patients were seen by cardiologists and 187 (33.3%) were admitted to the cardiology ward. The logistic regression analysis revealed that angina equivalents (P<0.001), age (P=0.002), history of coronary artery disease (P=0.003), electrocardiography (P=0.001), substernal chest pain (P=0.001), typical chest pain (P=0.000) and radiation of chest pain (P=0.039) were independent factors affecting emergency physicians' decisions.

CONCLUSION

The factors affecting emergency physicians' decisions are correlated with guidelines.

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.

Evaluation of artificial neural networks in the classification of primary oesophageal dysmotility

OBJECTIVE

Artificial neural networks (ANNs) can rapidly analyse large data sets and exploit complex mathematical relationships between variables. We investigated the feasibility of utilizing ANNs in the recognition and objective classification of primary oesophageal motor disorders, based on stationary oesophageal manometry recordings.

MATERIAL AND METHODS

One hundred swallow sequences, including 80 that were representative of various oesophageal motor disorders and 20 of normal motility, were identified from 54 patients (34 F; median age 59 years). Two different ANN techniques were trained to recognize normal and abnormal swallow sequences using mathematical features of pressure wave patterns both with (ANN(+)) and without (ANN(-)) the inclusion of standard manometric criteria. The ANNs were cross-validated and their performances were compared to the diagnoses obtained by standard visual evaluation of the manometric data.

RESULTS

Interestingly, ANN(-), rather than ANN(+), programs gave the best overall performance, correctly classifying >80% of swallow sequences (achalasia 100%, nutcracker oesophagus 100%, ineffective oesophageal motility 80%, diffuse oesophageal spasm 60%, normal motility 80%). The standard deviation of the distal oesophageal pressure and propagated pressure wave activity were the most influential variables in the ANN(-) and ANN(+) programs, respectively.

CONCLUSIONS

ANNs represent a potentially important tool that can be used to improve the classification and diagnosis of primary oesophageal motility disorders.

Prospective multicenter study of quantitative pretest probability assessment to exclude acute coronary syndrome for patients evaluated in emergency department chest pain units

STUDY OBJECTIVE

We compare the diagnostic accuracy of 3 methods--attribute matching, physician's written unstructured estimate, and a logistic regression formula (Acute Coronary Insufficiency-Time Insensitive Predictive Instrument, ACI-TIPI)--of estimating a very low pretest probability (< or = 2%) for acute coronary syndromes in emergency department (ED) patients evaluated in chest pain units.

METHODS

We prospectively studied 1,114 consecutive patients from 3 academic EDs, evaluated for acute coronary syndrome. Physicians collected data required for pretest probability assessment before protocol-driven chest pain unit testing. A pretest probability greater than 2% was considered "test positive." The criterion standard was the outcome of acute coronary syndrome (death, myocardial infarction, revascularization, or > 60% stenosis prompting new treatment) within 45 days, adjudicated by 3 independent reviewers.

RESULTS

Fifty-one of 1,114 enrolled patients (4.5%; 95% confidence interval [CI] 3.4% to 6.0%) developed acute coronary syndrome within 45 days, including 4 of 991 (0.4%; 95% CI 0.1% to 1.0%) patients, discharged after a negative chest pain unit evaluation result, who developed acute coronary syndrome. Unstructured estimate identified 293 patients with pretest probability less than or equal to 2%, 2 had acute coronary syndrome, yielding sensitivity of 96.1% (95% CI 86.5% to 99.5%) and specificity of 27.4% (95% CI 24.7% to 30.2%). Attribute matching identified 304 patients with pretest probability less than or equal to 2%; 1 had acute coronary syndrome, yielding a sensitivity of 98.0% (95% CI 89.6% to 99.9%) and a specificity of 26.1% (95% CI 23.6% to 28.7%). ACI-TIPI identified 56 patients; none had acute coronary syndrome, yielding sensitivity of 100% (95% CI 93.0% to 100%) and specificity of 6.1% (95% CI 4.7% to 7.9%).

CONCLUSION

In a low-risk ED population with symptoms suggestive of acute coronary syndrome, patients with a quantitative pretest probability less than or equal to 2%, determined by attribute matching, unstructured estimate, or logistic regression, may not require additional diagnostic testing.

Artificial Neural Network Models for Prediction of Acute Coronary Syndromes Using Clinical Data From the Time of Presentation

STUDY OBJECTIVE

Clinical and ECG data from presentation are highly discriminatory for diagnosis of acute coronary syndromes, whereas definitive diagnosis from serial ECG and cardiac marker protein measurements is usually not available for several hours. Artificial neural networks are computer programs adept at pattern recognition tasks and have been used to analyze data from chest pain patients with a view to developing diagnostic algorithms that might improve triage practices in the emergency department. The aim of this study is to develop and optimize artificial neural network models for diagnosis of acute coronary syndrome, to test these models on data collected prospectively from different centers, and to establish whether the performance of these models was superior to that of models derived using a standard statistical technique, logistic regression.

METHODS

The study used data from 3,147 patients presenting to 3 hospitals with acute chest pain. Data from hospital 1 were used to train the models, which were then tested on independent data from the other 2 hospitals. From 40 potential factors, variables were selected according to the logarithm of their likelihood ratios to produce models using 8, 13, 20, and 40 factors. Identical data were used for logistic regression and artificial neural network models. Calibration and performance were assessed, the latter using receiver operating characteristic (ROC) curve analysis.

RESULTS

Although the performance of artificial neural network models generally increased with increasing numbers of factors, this was insignificant. The 13-factor model was therefore used for the rest of the study owing to its marginally improved calibration over the smallest model. Area under the ROC curve (with standard error) was 0.97 (0.006). The overall sensitivity and specificity of this model for acute coronary syndrome diagnosis using the training data was 0.93. ROC curves for logistic regression and artificial neural network models applied to data from the 3 hospitals were identical. For the 13-factor artificial neural network model tested on data from hospitals 2 and 3, area under the ROC curves (standard error) were 0.93 (0.006) and 0.95 (0.009), respectively. Investigation of the performance of the artificial neural network models throughout the range of predicted probabilities showed that they were well calibrated.

CONCLUSION

This study confirms that artificial neural networks can offer a useful approach for developing diagnostic algorithms for chest pain patients; however, the exceptional performance and simplicity of the logistic model militates in favor of logistic regression for the present task. Our artificial neural network models were well calibrated and performed well on unseen data from different centers. These issues have not been addressed in previous studies. However, and unlike in previous studies, we did not find the performance of artificial neural network models to be significantly different from that of suitably optimized logistic regression models.