Sigma Metric Evaluation of Hematological Parameters: A Retrospective Quality Assessment

Clinical hematology laboratories play a pivotal role in ensuring accurate diagnosis and optimal patient care. Quality control (QC) is essential for monitoring the analytical phase and detecting errors that may arise due to instrument malfunction, environmental factors, or operator-related issues. Sigma metrics, a statistical tool widely used in clinical biochemistry, offers a more objective measure of analytical performance, though its adoption in hematology is still evolving.

(1) To evaluate the internal quality control (IQC) performance of five key hematological parameters using sigma metrics; (2) to determine optimal IQC frequency based on sigma analysis; and (3) to assess the utility of sigma metrics as a comprehensive QC tool in hematology.

A retrospective cross-sectional observational study was conducted over six months (September 2024 to February 2025) in the hematology laboratory of a tertiary care teaching hospital. Three-level QC materials (L1, L2, L3) were analyzed daily for five parameters: hemoglobin, WBC, RBC, hematocrit, and platelets. Sigma values were calculated using the formula (total allowable error {TEa} values were taken from Clinical Laboratories Improvement Act guidelines): [Formula: see text] Results: Hemoglobin and WBC exhibited sigma values >6, indicating excellent analytical performance. RBC and platelets demonstrated acceptable performance, with sigma values ranging between 4 and 6. Hematocrit showed a marginal sigma value of 3.74, falling between 3 and 4, which suggests the need for improvement in its quality control processes. Notably, none of the analytes recorded a sigma value below 3. These findings reflect an overall satisfactory performance of the hematology parameters, with selective areas requiring enhanced QC strategies.

Sigma metric analysis is a valuable tool for assessing analytical performance in hematology. While high-performing parameters can follow simpler QC protocols (e.g., 13s rule), marginal parameters necessitate stricter multi-rule QC. Overall, adopting parameter-specific sigma-based QC enhances laboratory reliability and efficiency.