Evaluation of Measurement Performance of Routine Chemistry Analytes by Application of Sigma Metrics, Method Evaluation Decision (MEDx) Charts and Quality Goal Index (QGI)
Bibliographic record
Abstract
Objective: To evaluate performance of routine chemistry analytes in a tertiary care hospital laboratory by the application of sigma metrics. Introduction: Six sigma (6σ) is a popular Quality Management System (QMS) tool. Laboratories are increasingly using the six sigma method for the objective assessment and comparison of the analytical methods and instrument performance. Six sigma is about measuring or counting the number of defects. It quantifies the performance of a process as a rate of Defects-Per-Million-Opportunities (DPMO or DPM). The aim is to assess the performance and to eliminate or reduce the variation in a process. Materials and Methods: This prospective study was conducted over a period of six months duration. Sigma metrics were calculated using coefficient of variation (%CV), %Bias and total allowable error (%TEa). For %CV we used Internal Quality Control (IQC) samples, at two levels L1 and L2. Daily IQC results of L1 and L2 for 25 routine chemistry analytes were recorded in an excel sheet and %CV was calculated for each analyte for the period of study. For each analyte %Bias was calculated based on values obtained from monthly RIQAS-EQA program data. The total allowable error (%TEa) values for each analyte were extracted from various sources like Clinical Laboratories Improvement Amendment act (CLIA), Canadian Fixed Limits from the College of Physicians and Surgeons of Saskatchewan (CFX) and Spanish Society of Clinical Chemistry and Molecular Pathology (SEQC) table of Desirable Quality Specifications based on Biological Variation (BV) criteria for acceptable performance. Sigma values were calculated. The minimal acceptable performance criteria was considered as 3 sigma. Normalized MEDx charts were used to plot sigma metrics to visually present the performance. Quality Goal Index (QGI) analysis was carried out as a part of Root Cause Analysis (RCA). Results: Highest sigma value of 16.7 was noted for HDL-C and the lowest of 2.08 for chloride at level L1. Many analytes like ALP, Amylase, AST, CK, GGT, HDL-C, Magnesium and Uric Acid attained world class quality performance at both levels L1 and L2 with sigma levels of >6. Many other analytes showed satisfactory performance with sigma levels of >3. Sodium, potassium, chloride and urea did not show a satisfactory performance. Conclusion: Sigma metrics evaluation of analytical performance of our laboratory showed an acceptable performance for a wide range of analytes in patient samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.142 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".