The coefficient of variation (CV) is calculated as the standard deviation (SD) divided by the mean and multiplied by 100. CV indicates variability of the test results. This depends upon the test methodology, the instrument being used, and the range of results.
Examples:
Sodium (Na) of 138 mmol/L is probably between 137.5 and 138.5 mmol/L
Hgb of 10.0 g/dL is probably between 9.8 and 10.2 g/dL
Glucose of 800 mg/dL is probably between 770 and 830 mg/dL
Thus, a change in test values from one day to the next generally has to be 10% or more to be of major significance. Just specimen handling, processing, and instrument variation can account for some changes. Running a test in duplicate will show this.
There is normally "physiologic variation" or biologic variation in patients that is dictated by factors such as the degree of hydration, diet, and exercise. Also, if you rely on specific "numbers" for decision points, you may run into trouble.
Example: it is late afternoon and the physician checks lab values on his patients. He notes one elderly patient's hemoglobin is now 9.9 g/dL, whereas the value was 10.1 g/dL early in the morning that same day. The physician's "set point" for ordering a transfusion is 10 g/dL, even though this is not a recognized practice standard. Using such criteria, an unnecessary transfusion, subjecting the patient to potential complications, would be given. But the two Hgb values could have come from either the morning or afternoon specimens run in duplicate!
Precision: How reproducible is the test under the same conditions? The laboratory tries to assure reproducibility by the use of control specimens with each run of patient specimens. The instruments have a routine maintenance and check procedure performed as well. You can be precise but not accurate by making the same error consistently.
Example: you may be using improper technique to measure blood pressure, but you will keep getting the same result, which is different from what the nurse (who is positioning the cuff properly and listening appropriately) records.
Accuracy: How well does the test measure what is really there? Agreement of the test results with the patient's condition is the best measure of accuracy.
Example: clinical diagnosis of acute appendicitis is about 90-95% accurate.
Bias: Since it is not practical to perform tests or measures on all members of a population, then one must obtain a sample of that population. There are methods available to randomize the sampling of the population. The closer the measurements are to the "real" or true value for a population, the more unbiased the study.
