Quality control technique to reduce the variability of longitudinal measurement of hemoglobin mass

C E Gough1, K Sharpe, M J Ashenden

  • 1Department of Physiology, Australian Institute of Sport, Belconnen, Australian Capital Territory, Australia. clare.gough@ausport.gov.au

The sensitivity of the athlete blood passport to detect blood doping may be improved by the inclusion of total hemoglobin mass (Hb(mass)), but the comparability of Hb(mass) from different laboratories is unknown. To optimize detection sensitivity, the analytical variability associated with Hb(mass) measurement must be minimized. The aim of this study was to investigate the efficacy of using quality controls to minimize the variation in Hb(mass) between laboratories. Three simulated laboratories were set up in one location. Nine participants completed three carbon monoxide (CO) re-breathing tests in each laboratory. One participant completed two CO re-breathing tests in each laboratory. Simultaneously, quality controls containing Low (1-3%) and High (8-11%) concentrations of percent carboxyhemoglobin (%HbCO) were measured to compare hemoximeters in each laboratory. Linear mixed modeling was used to estimate the within-subject variation in Hb(mass), expressed as the coefficient of variation, and to estimate the effect of different laboratories. The analytic variation of Hb(mass) was 2.4% when tests were conducted in different laboratories, which reduced to 1.6% when the model accounted for between-laboratory differences. Adjustment of Hb(mass) values using quality controls achieved a comparable analytic variation of 1.7%. The majority of between-laboratory variation in Hb(mass) originated from the difference between hemoximeters, which could be eliminated using appropriate quality controls.

Related Concept Videos

Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
Instrument Calibration01:12

Instrument Calibration

Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Data Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...