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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.