Comparing analytic performance criteria: evaluation of HbA1c certification criteria as an example
Curt L Rohlfing1, Curtis A Parvin2, David B Sacks3
1Department of Pathology & Anatomical Sciences, University of Missouri, Columbia School of Medicine, 1 Hospital Drive, Columbia, MO 65212, United States.
Summary
Comparing analytical performance criteria for hemoglobin A1c (HbA1c) analysis is complex. A mathematical approach found that requiring 37 of 40 results within ±7% of the target best aligns NGSP and CAP criteria, enhancing consistency.
Area of Science:
- Clinical Chemistry
- Laboratory Medicine
- Analytical Chemistry
Background:
- Direct comparison of analytical performance criteria using different statistical methods poses challenges.
- Standardization programs like NGSP and CAP use distinct statistical approaches for HbA1c analysis criteria.
- Inconsistency between these schemes can complicate performance evaluations.
Purpose of the Study:
- To develop a mathematical approach for comparing analytical performance criteria for HbA1c.
- To enhance consistency between the NGSP and CAP standardization schemes.
- To mathematically compare CAP and proposed NGSP criteria for HbA1c analysis.
Main Methods:
- Calculated imprecision (CV) and bias combinations for passing criteria at various probabilities (0.95, 0.99, 0.999).
- Constructed contour plots to visually compare criteria.
- Mathematically determined CV/bias requirements for CAP (3/3 results within ±7%) and NGSP (33/40 to 40/40 results within ±7%) criteria; used simulations for existing NGSP criterion (95% CI of differences within ±0.75% HbA1c).
Main Results:
- Requiring 37 of 40 results to be within ±7% of the NGSP target closely matched the CAP criterion at zero bias.
- This specific NGSP criterion demonstrated a 95% chance of passing when aligned with the CAP standard.
Conclusions:
- The NGSP Steering Committee recommended a certification criterion of 37 of 40 results within ±7% (later reduced to ±6%).
- The developed mathematical evaluation approach can be applied to compare other performance criteria.
- This method aids in harmonizing performance standards across different laboratory accreditation bodies.
Related Concept Videos
01:25Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis
17
Type 2 diabetes mellitus develops gradually and is often asymptomatic in early stages.Clinical ManifestationsWhen symptoms appear, they include fatigue, blurred vision, pruritus, delayed wound healing, and recurrent infections, particularly candidal infections. Peripheral neuropathy may present as numbness or tingling in the extremities. Classic hyperglycemia symptoms—polyuria, polydipsia, and polyphagia—are less common. Most patients are overweight and frequently have associated...
17
01:15Receiver Operating Characteristic Plot
584
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
584
01:29Hyperglycemia
25
Hyperglycemia is an abnormally high blood glucose level. It is diagnosed by fasting glucose ≥126 mg/dL, 2-hour oral glucose tolerance test (or OGTT) ≥200 mg/dL, random glucose ≥200 mg/dL with symptoms, or HbA1c ≥6.5%. However, HbA1c results may be unreliable in certain conditions, such as anemia or hemoglobinopathies, and the diagnosis should be confirmed unless classic symptoms are present. Postprandial hyperglycemia is typically considered significant when glucose...
25
01:19Testing a Claim about Standard Deviation
2.1K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.1K
01:26Hypoglycemia
30
Hypoglycemia is a blood glucose level below 70 mg/dL. It commonly occurs in individuals using insulin or insulin-secreting drugs, but may also arise in non-diabetic conditions. People with type 1 diabetes are at the highest risk because they depend on exogenous insulin. People with type 2 diabetes are also at risk, especially when treated with insulin or medications such as sulfonylureas, which increase insulin release regardless of blood glucose levels. It develops when insulin levels exceed...
30
01:13Accuracy and Errors in Hypothesis Testing
716
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
716