Validating the NIH LDL-C equation for provincial implementation in Alberta.

BACKGROUND: LDL-C, a cardiovascular disease risk assessment biomarker, is commonly calculated using the Friedewald equation. The NIH equation overcomes several limitations of the Friedewald equation. Consistent with the Canadian Society of Clinical Chemists (CSCC) lipid reporting recommendations, we assessed the NIH LDL-C equation in Alberta prior to its provincial implementation. METHODS: 1-year (01/01/2021-12/31/2021) of lipid results (n = 1,486,584 after data cleaning) were obtained from five analytical instrument groups used across Alberta. Analyses were performed on all data and after separating by age, analytical instrument group, and fasting status. The correlation between Friedewald- and NIH-calculated LDL-C and between Friedewald- and NIH-calculated LDL-C difference and each lipid parameter, was determined. The frequency of unreportable/inaccurate LDL-C results was compared between the two equations. The concordance between the two equations and with non-HDL-C was determined at LDL-C thresholds. Lastly, LDL-C calculated by Friedewald, NIH, and Martin-Hopkins equations was compared to density-gradient ultracentrifugation. RESULTS: Friedewald- and NIH-calculated LDL-C exhibit the strongest correlation when triglycerides ≤ 4.52 mmol/L. The difference between Friedewald- and NIH-calculated LDL-C increases with decreasing LDL-C concentration. The NIH equation yields fewer inaccurate results (0.35 % vs. 22.0 %). The percent agreement between equations was > 96 % at all LDL-C thresholds, suggesting most patients will not require treatment changes. NIH-calculated LDL-C exhibited better agreement with non-HDL-C when triglycerides ≤ 9.04 mmol/L and better correlated with LDL-C measured by ultracentrifugation (r2 = 0.926 vs. 0.775 (Friedewald) and 0.863 (Martin-Hopkins)). Results were consistent across age, analytical instrument group, and fasting status. CONCLUSIONS: Our findings demonstrate the benefits of implementing the NIH equation across Alberta.

Variation in processes and reporting of cerebrospinal fluid oligoclonal banding and associated tests and calculated indices across Canadian clinical laboratories.

OBJECTIVES: Multiple sclerosis is diagnosed based on clinical and laboratory findings, including cerebrospinal fluid (CSF) oligoclonal banding (OCB) analysis. The lack of updated CSF OCB laboratory guidelines in Canada has likely led to variation in processes and reporting across clinical laboratories. As a first step to developing harmonized laboratory recommendations, we examined current CSF OCB processes, reporting, and interpretation across all Canadian clinical laboratories currently performing this test. DESIGN AND METHODS: A survey of 39 questions was sent to clinical chemists at all 13 Canadian clinical laboratories performing CSF OCB analysis. The survey included questions regarding quality control processes, reporting practices for CSF gel electrophoresis pattern interpretation, and associated tests and calculated indices. RESULTS: The survey response rate was 100%. Most (10/13) laboratories use ≥2 CSF-specific bands (2017 McDonald Criteria) as their CSF OCB positivity cut-off and only 2/13 report the number of bands with every report. Most (8/13 and 9/13) laboratories report an inflammatory response pattern and monoclonal gammopathy pattern, respectively. However, the process for reporting and/or confirming a monoclonal gammopathy varies widely. Variation was observed for reference intervals, units, and the panel of reported associated tests and calculated indices. The maximum acceptable time interval between paired CSF and serum collections varied from 24 h to no limit. CONCLUSIONS: Profound variation exists in processes, reporting, and interpretation of CSF OCB and associated tests and indices across Canadian clinical laboratories. Harmonization of CSF OCB analysis is required to ensure continuity and quality of patient care. Our detailed assessment of current practice variation highlights the need for clinical stakeholder engagement and further data analysis to support optimal interpretation and reporting practices, which will aid in developing harmonized laboratory recommendations.