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Diagnostic Value of Cardiac Biomarkers

Posted in Atherogenic Processes & Pathology, Best evidence, Ca2+ triggered activation, Calmodulin Kinase and Contraction, Cardiac & Vascular Repair Tools Subsegment, Cardiac and Cardiovascular Surgical Procedures, Cardiomyopathy, Cardiovascular Pharmacogenomics, Cardiovascular Research, Coagulation Therapy and Internal Bleeding, Curation, Diabetes Mellitus, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Drug Delivery Platform Technology, Evidence-based decision-making, Evolutionary cognition, Frontiers in Cardiology and Cardiovascular Disorders, Genomic Testing: Methodology for Diagnosis, Immunodiagnostics, Inferential analysis, Medical and Population Genetics, Medical Devices R&D and Inventions, Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound, Molecular Genetics & Pharmaceutical, Nephrology, Nutritional Supplements: Atherogenesis, lipid metabolism, Personalized and Precision Medicine & Genomic Research, Pharmacogenomics, Pharmacologic toxicities, Population Health Management, Genetics & Pharmaceutical, Proteomics, Quality Assurance, Systemic Inflammatory Response Related Disorders, Technology Advance Assessment of, Technology Transfer: Biotech and Pharmaceutical, Translational Effectiveness, Translational Science, tagged 2, Acute coronary syndrome, acute myocardial infarction, American College of Cardiology ACC, Antiplatelet drug, aspartate aminotransferse (AST), Atrial fibrillation, cardiac biomarkers, cardiomyocyte, Cardiomyopathy, CK isoenzyme MB, confounders, coronary artery plaque, Coumadin, Creatine kinase, endothelial cell, hs-troponins, hypercoagulable state, INR, Lactate dehydrogenase, LD isoenzyme(s)-1, miRNAs, myocardial contraction, plaque rupture, platelet activation, practice guidelines, PT, PTT, troponin I, Troponin T, World Health Organization on January 4, 2014| Leave a Comment »

Diagnostic Value of Cardiac Biomarkers

Author and Curator: Larry H Bernstein, MD, FCAP

These presentations covered several views of the utilization of cardiac markers that have evolved for over 60 years. The first stage was the introduction of enzymatic assays and isoenzyme measurements to distinguish acute hepatitis and acute myocardial infarction, which included lactate dehydrogenase (LD isoenzymes 1, 2) at a time that late presentation of the patient in the emergency rooms were not uncommon, with the creatine kinase isoenzyme MB declining or disappeared from the circulation. The world health organization (WHO) standard definition then was the presence of two of three:

1. Typical or atypical precordial pressure in the chest, usually with radiation to the left arm

2. Electrocardiographic changes of Q-wave, not previously seen, definitive; ST- elevation of acute myocardial injury with repolarization;
T-wave inversion.

3. The release into the circulation of myocardial derived enzymes –
creatine kinase – MB (which was adapted to measure infarct size), LD-1,
both of which were replaced with troponins T and I, which are part of the actomyosin contractile apparatus.

The research on infarct size elicited a major research goal for early diagnosis and reduction of infarct size, first with fibrinolysis of a ruptured plaque, and this proceeded into the full development of a rapidly evolving interventional cardiology as well as cardiothoracic surgery, in both cases, aimed at removal of plaque or replacement of vessel. Surgery became more imperative for multivessel disease, even if only one vessel was severely affected.

So we have clinical history, physical examination, and emerging biomarkers playing a large role for more than half a century. However, the role of biomarkers broadened. Patients were treated with antiplatelet agents, and a hypercoagulable state coexisted with myocardial ischemic injury. This made the management of the patient reliant on long term followup for Warfarin with the international normalized ratio (INR) for a standardized prothrombin time (PT), and reversal of the PT required transfusion with thawed fresh frozen plasma (FFP). The partial thromboplastin test (PPT) was necessary in hospitalization to monitor the heparin effect.

Thus, we have identified the use of traditional cardiac biomarkers for:

1. Diagnosis
2. Therapeutic monitoring

The story is only the beginning. Many patients who were atypical in presentation, or had cardiovascular ischemia without plaque rupture were problematic. This led to a concerted effort to redesign the troponin assays for high sensitivity with the concern that the circulation should normally be free of a leaked structural marker of myocardial damage. But of course, there can be a slow leak or a decreased rate of removal of such protein from the circulation, and the best example of this would be the patient with significant renal insufficiency, as TnT is clear only through the kidney, and TNI is clear both by the kidney and by vascular endothelium. The introduction of the high sensitivity assay has been met with considerable confusion, and highlights the complexity of diagnosis in heart disease. Another test that is used for the diagnosis of heart failure is in the class of natriuretic peptides (BNP, pro NT-BNP, and ANP), the last of which has been under development.

While there is an exponential increase in the improvement of cardiac devices and discovery of pharmaceutical targets, the laboratory support for clinical management is not mature. There are miRNAs that may prove valuable, matrix metalloprotein(s), and potential endothelial and blood cell surface markers, they require

1. codevelopment with new medications
2. standardization across the IVD industry
3. proficiency testing applied to all laboratories that provide testing
4. the measurement on multitest automated analyzers with high capability in proteomic measurement (MS, time of flight, MS-MS)

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Scale‑Free Diagnosis of AMI from Clinical Laboratory Values

Posted in Biomarkers & Medical Diagnostics, Computational Biology/Systems and Bioinformatics, HealthCare IT, Medical Devices R&D Investment, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Statistical Methods for Research Evaluation, tagged biomarker studies, Creatine kinase, Electrocardiography, Lactate dehydrogenase, Larry H. Bernstein, Medical laboratory, myocardial infarct, myocardial infarction, predictive analytics, probabilistic conjoint models, Rasch, statistical modeling, Thomas on August 13, 2012| 2 Comments »

Scale‑Free Diagnosis of AMI from Clinical Laboratory Values

Author: Larry H. Bernstein, MD, FCAP

Scale‑Free Diagnosis of AMI from Clinical Laboratory Values

William P. Fisher, Jr., Larry H. Bernstein, Thomas A Naegele, Arden

Forrey, Asadullah Qamar, Joseph Babb, Eugene W. Rypka, Donna Yasick

Objective. Clinicians are often challenged with interpreting myriads of laboratory test results with few resources for knowing which values are most relevant, when any given value indicates a need for action, or how urgent the need for action is. The arrival of the electronic health record creates a context in which computational resources for meeting these challenges will be readily available. The purpose of this study was to evaluate the feasibility of employing probabilistic conjoint (Rasch) measurement models for creating the needed scale‑free standard measures and data quality standards.

Methods. Pathology data from 144 clients suspected of suffering myocardial infarctions were obtained. Thirty indicators were converted from their original values to ratings indicating a worsening of condition. These conversions took advantage of the fact that serial measurement of creatine kinase (CK; EC 2.7.3.2) isoenzyme MB (CK‑MB) and lactic dehydrogenase (LD; EC 1.1.1.27) isoenzyme 1 (LD‑1) in serum have characteristic evolutions in acute myocardial infarction (AMI). CK‑MB concentration begins to rise within 4 to 8 hours, peaks at 12 to 24 hours, and returns to normal within 48 to 72 hours. LD‑1 becomes elevated as early as 8 to 24 hours after infarction, and reaches a peak in 48 to 72 hours. However, the ratio of serum activity of LD‑1/total LD may be more definitive than LD‑1 activity itself. While these are most important in ECG negative AMI, they are not by themselves a “gold standard” for diagnosis.

The additional information and functionality required for such standards, including probabilistic estimates of scale parameters whose values do not depend on the calibrating sample and the capacity to deal with missing data, were sought by fitting the data to a Rasch partial credit model. This model estimates separate rating step values for each group of items sharing a common rating structure, en route to testing the hypothesis that the items work together to delineate a unidimensional measurement continuum defined by the repetition of a single unit quantity.

Results. Twenty of the 30 items were identified as delineating a unidimensional continuum. Client measurement reliability was 0.90, and item calibration reliability was 0.96. Overall model fit is indicated by the client information‑ weighted mean square fit (infit) statistic (mean = .94, SD = .34) and outlier‑ sensitive mean square fit (outfit) statistic (mean = 1.02, SD = .72), and the item infit (mean = .99, SD = .41) and outfit (mean = 1.04, SD = .72). The data‑to‑ model global fit is also indicated by the chi‑square of 3094.5, with 164 maximum independent parameters, 2766 maximum degrees of freedom, and a probability (statistical significance) of less than .01 that this ora greater chi‑square would be observed with perfect data‑model fit.

Discussion. The analysis identified the 20 values most relevant to the diagnosis of AMI; these data may also support the construction of a unidimensional measure of AMI severity. If the construct supports both diagnostic and severity inferences, then the clinical action needed and its urgency will be indicated by the client’s measure. Similar analyses of data from other diagnostic groups will determine the extent to which lab value item relevance and hierarchies vary across diagnoses; such variation will be crucial to determining computer‑based decision support algorithms, which will match individual clients’ data with specific diagnostic profiles. Further analyses will also demonstrate the extent to which diagnosis is affected by missing data.