Paul Braun

NMR measurement of LDL particle number using the Vantera® Clinical Analyzer

BACKGROUND The Vantera Clinical Analyzer was developed to enable fully-automated, high-throughput nuclear magnetic resonance (NMR) spectroscopy measurements in a clinical laboratory setting. NMR-measured low-density lipoprotein particle number (LDL-P) has been shown to be more strongly associated with cardiovascular disease outcomes than LDL cholesterol (LDL-C) in individuals for whom these alternate measures of LDL are discordant. OBJECTIVE The aim of this study was to assess the analytical performance of the LDL-P assay on the Vantera Clinical Analyzer as per Clinical Laboratory Standards Institute (CLSI) guidelines. RESULTS Sensitivity and linearity were established within the range of 300-3500 nmol/L. For serum pools containing low, medium and high levels of LDL-P, the inter-assay, intra-assay precision and repeatability gave coefficients of variation (CVs) between 2.6 and 5.8%. The reference interval was determined to be 457-2282 nmol/L and the assay was compatible with multiple specimen collection tubes. Of 30 substances tested, only 2 exhibited the potential for assay interference. Moreover, the LDL-P results from samples run on two NMR platforms, Vantera Clinical Analyzer and NMR Profiler, showed excellent correlation (R(2)=0.96). CONCLUSIONS The performance characteristics suggest that the LDL-P assay is suitable for routine testing in the clinical laboratory on the Vantera Clinical Analyzer, the first automated NMR platform that supports NMR-based clinical assays.

HDL particle number measured on the Vantera®, the first clinical NMR analyzer.

OBJECTIVES Nuclear magnetic resonance (NMR) spectroscopy has been successfully applied to the measurement of high-density lipoprotein (HDL) particles, providing particle concentrations for total HDL particle number (HDL-P), HDL subclasses (small, medium, large) and weighted, average HDL size for many years. Key clinical studies have demonstrated that NMR-measured HDL-P was more strongly associated with measures of coronary artery disease and a better predictor of incident cardiovascular disease (CVD) events than HDL-cholesterol (HDL-C). Recently, an NMR-based clinical analyzer, the Vantera(®), was developed to allow lipoprotein measurements to be performed in the routine, clinical laboratory setting. The aim of this study was to evaluate and report the performance characteristics for HDL-P quantified on the Vantera(®) Clinical Analyzer. DESIGN AND METHODS Assay performance was evaluated according to Clinical and Laboratory Standards Institute (CLSI) guidelines. In order to ensure that quantification of HDL-P on the Vantera(®) Clinical Analyzer was similar to the well-characterized HDL-P assay on the NMR profiler, a method comparison was performed. RESULTS The within-run and within-lab imprecision ranged from 2.0% to 3.9%. Linearity was established within the range of 10.0 to 65.0 μmol/L. The reference intervals were different between men (22.0 to 46.0 μmol/L) and women (26.7 to 52.9 μmol/L). HDL-P concentrations between two NMR platforms, Vantera(®) Clinical Analyzer and NMR Profiler, demonstrated excellent correlation (R(2) = 0.98). CONCLUSIONS The performance characteristics, as well as the primary tube sampling procedure for specimen analysis on the Vantera(®) Clinical Analyzer, suggest that the HDL-P assay is suitable for routine clinical applications.

Classification of factor deficiencies from coagulation assays using neural networks

Activated partial thromboplastin time (APTT) and prothrombin time (PT) assays are widely used to screen for coagulation disorders and to monitor administration of therapeutic drugs. The analysis of data from coagulation assays has traditionally concentrated on determination of clot times (for APTT and PT) and magnitude of signal change during coagulation (e.g. for PT-based fibrinogen quantitation). The purpose of this study was to determine if the diagnostic power of these assays could be increased by using neural networks to interpret multiple parameters from these assays. Error back-propagation neural networks were trained using multiple variables derived from APTT and PT optical data for 200 normal and abnormal patient specimens. These networks were used to: (1) classify samples as either deficient or non-deficient with respect to individual blood components; and (2) estimate the approximate concentration of specific coagulation factors. Results indicated that these networks could be successfully trained to identify specific factor deficiencies at less than 30% normal levels with good specificity and variable sensitivity, but that they estimated actual concentrations poorly in most cases. These results support possible applications for neural networks identifying specific coagulation abnormalities from non-specific APTT and PT assays using expanded data parameter sets.

PREDICTING THE PRESENCE OF PLASMA HEPARIN USING NEURAL NETWORKS TO ANALYZE COAGULATION SCREENING ASSAY OPTICAL PROFILES

A method for predicting the presence of heparin from coagulation screening assays is described and data are presented. This method incorporates the use of a multilayer perceptron trained through an error back-propagation algorithm in analyzing clotting optical data profiles. This method may lead to the identification of abnormalities from screening assays that might otherwise go undetected, or require additional testing to isolate.