Performance evaluation of a coagulation laboratory using Sigma metrics

Bias in Laboratory Medicine: The Dark Side of the Moon

Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.

Evaluation of Sigma metric approach for monitoring the performance of automated analyzers in hematology unit of Alexandria Main University Hospital

INTRODUCTION

Sigma metric offers a quantitative framework for evaluating process performance in clinical laboratories. This study aimed to evaluate the analytical performance of automated analyzers in hematology unit of Alexandria Main University Hospital using the sigma metric approach.

MATERIALS AND METHODS

Quality control data were collected for 6 months, and sigma value was calculated from hematology analyzers SYSMEX (XN 1000, XT 1800i), ADVIA (2120i, 2120), and coagulation analyzers SYSMEX CA 1500 (3610, 6336).

RESULTS

For the normal control level, satisfactory mean sigma value ≥3 was observed for all of the studied parameters by all analyzers. For the high control level, red blood cell count by ADVIA 2120, and hematocrit by ADVIA (2120i and 2120) performed poorly with a mean sigma value <3. For the low control level, red blood cell count by ADVIA (2120i and 2120), hemoglobin by ADVIA 2120, hematocrit by ADVIA (2120i and 2120) and SYSMEX XN 1000, platelet count by the SYSMEX XT 1800i also performed poorly with a mean sigma value <3. Satisfactory mean sigma value of ≥3 was observed for prothrombin time and activated partial thromboplastin time for both normal and pathological control levels and analyzers.

CONCLUSION

Sigma metrics can be used as a guide to make QC strategy and plan QC frequency and can facilitate the comparison of the same assay performance across multiple systems. Harmonization for TEa source is recommended to standardize sigma value calculation.