Cardiac biomarkers by point-of-care testing - back to the future?

The measurement of the cardiac troponins (cTn), cardiac troponin T (cTnT) and cardiac troponin I (cTnI) are integral to the management of patients with suspected acute coronary syndromes (ACS). Patients without clear electrocardiographic evidence of myocardial infarction require measurement of cTnT or cTnI. It therefore follows that a rapid turnaround time (TAT) combined with the immediacy of results return which is achieved by point-of-care testing (POCT) offers a substantial clinical benefit. Rapid results return plus immediate decision-making should translate into improved patient flow and improved therapeutic decision-making. The development of high sensitivity troponin assays offer significant clinical advantages. Diagnostic algorithms have been devised utilising very low cut-offs at first presentation and rapid sequential measurements based on admission and 3 h sampling, most recently with admission and 1 h sampling. Such troponin algorithms would be even more ideally suited to point-of-care testing as the TAT achieved by the diagnostic laboratory of typically 60 min corresponds to the sampling interval required by the clinician using the algorithm. However, the limits of detection and analytical imprecision required to utilise these algorithms is not yet met by any easy-to-use POCT systems

Proteomics in cardiovascular disease: recent progress and clinical implication and implementation

Introduction: Although multiple efforts have been initiated to shed light into the molecular mechanisms underlying cardiovascular disease, it still remains one of the major causes of death worldwide. Proteomic approaches are unequivocally powerful tools that may provide deeper understanding into the molecular mechanisms associated with cardiovascular disease and improve its management. Areas covered: Cardiovascular proteomics is an emerging field and significant progress has been made during the past few years with the aim of defining novel candidate biomarkers and obtaining insight into molecular pathophysiology. To summarize the recent progress in the field, a literature search was conducted in PubMed and Web of Science. As a result, 704 studies from PubMed and 320 studies from Web of Science were retrieved. Findings from original research articles using proteomics technologies for the discovery of biomarkers for cardiovascular disease in human are summarized in this review. Expert commentary: Proteins associated with cardiovascular disease represent pathways in inflammation, wound healing and coagulation, proteolysis and extracellular matrix organization, handling of cholesterol and LDL. Future research in the field should target to increase proteome coverage as well as integrate proteomics with other omics data to facilitate both drug development as well as clinical implementation of findings

Tracing diagnosis trajectories over millions of patients reveal an unexpected risk in schizophrenia.

The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence < 1E-04) (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients

Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78–0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1–6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI

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

A Comparison of Patient History- and EKG-based Cardiac Risk Scores.

Patient-specific risk scores are used to identify individuals at elevated risk for cardiovascular disease. Typically, risk scores are based on patient habits and medical history - age, sex, race, smoking behavior, and prior vital signs and diagnoses. We explore an alternative source of information, a patient's raw electrocardiogram recording, and develop a score of patient risk for various outcomes. We compare models that predict adverse cardiac outcomes following an emergency department visit, and show that a learned representation (e.g. deep neural network) of raw EKG waveforms can improve prediction over traditional risk factors. Further, we show that a simple model based on segmented heart beats performs as well or better than a complex convolutional network recently shown to reliably automate arrhythmia detection in EKGs. We analyze a large cohort of emergency department patients and show evidence that EKG-derived scores can be more robust to patient heterogeneity

Elaboration and validation of an ICNP® terminology subset for patients with acute myocardial infarction

Objective: To elaborate a terminological subset for the International Classification for Nursing Practice (ICNP® ) for patients with acute myocardial infarction using the Activities of Living Model. Method: A methodological study which followed the guidelines of the International Nursing Council and was based on theoretical framework of the Activities of Living Model for its elaboration. Content validation was performed by 22 nursing specialists. Results: Twenty-two (22) diagnoses and 22 nursing outcomes were elaborated. Of these, 17 nursing diagnosis statements and 17 nursing outcome statements presented Content Validity Index (CVI) ≥ 0.80. Of the 113 elaborated nursing interventions, 42 reached a CVI ≥ 0.80, and 51 interventions made up the terminological subset after the expert suggestions. Conclusion: The ICNP® was suitable for use with the Activities of Living Model, having compatible terms with those used in clinical nursing practice, and valid for construction of the terminological subset for patients with acute myocardial infarction and most likely to facilitate clinical nursing judgment

Estimating the economic burden of cardiovascular events in patients receiving lipid-modifying therapy in the UK.

OBJECTIVES: To characterise the costs to the UK National Health Service of cardiovascular (CV) events among individuals receiving lipid-modifying therapy. DESIGN: Retrospective cohort study using Clinical Practice Research Datalink records from 2006 to 2012 to identify individuals with their first and second CV-related hospitalisations (first event and second event cohorts). Within-person differences were used to estimate CV-related outcomes. SETTING: Patients in the UK who had their first CV event between January 2006 and March 2012. PARTICIPANTS: Patients ≥18 years who had a CV event and received at least 2 lipid-modifying therapy prescriptions within 180 days beforehand. PRIMARY AND SECONDARY OUTCOME MEASURES: Direct medical costs (2014 £) were estimated in 3 periods: baseline (pre-event), acute (6 months afterwards) and long-term (subsequent 30 months). Primary outcomes included incremental costs, resource usage and total costs per period. RESULTS: There were 24 093 patients in the first event cohort of whom 5274 were included in the second event cohort. The mean incremental acute CV event costs for the first event and second event cohorts were: coronary artery bypass graft/percutaneous transluminal coronary angioplasty (CABG/PTCA) £5635 and £5823, myocardial infarction £4275 and £4301, ischaemic stroke £3512 and £4572, heart failure £2444 and £3461, unstable angina £2179 and £2489 and transient ischaemic attack £1537 and £1814. The mean incremental long-term costs were: heart failure £848 and £2829, myocardial infarction £922 and £1385, ischaemic stroke £973 and £682, transient ischaemic attack £705 and £1692, unstable angina £328 and £677, and CABG/PTCA £-368 and £599. Hospitalisation accounted for 95% of acute and 61% of long-term incremental costs. Higher comorbidity was associated with higher long-term costs. CONCLUSIONS: Revascularisation and myocardial infarction were associated with the highest incremental costs following a CV event. On the basis of real-world data, the economic burden of CV events in the UK is substantial, particularly among those with greater comorbidity burden

Electrocardiographic diagnosis of ST segment elevation myocardial infarction: An evaluation of three automated interpretation algorithms

To assess the validity of three different computerized electrocardiogram (ECG) interpretation algorithms in correctly identifying STEMI patients in the prehospital environment who require emergent cardiac intervention