Laboratory Administration & Management of Pathology Practices

Contributed by Duy K. Doan, M.D. and Lewis A. Hassell, M.D.


Validation of reportable ranges

Standard values	Observed values			Means
	Replicate 1	Replicate 2	Replicate 3
0	2	0	4	3
200	220	210	200	210
400	400	400	400	400
600	620	630	610	620
800	760	760	790	770
1,000	920	900	880	900

• In this example:
  • Expected reportable ranges: 0 - 1,000 mg/dL
  • Number of concentration levels: 5
  • Number of replicates: 3

• Direct approach: subjects representing the reference population are selected and sampled and the specimens are analyzed to determine the reference intervals
• Indirect approach: results from specimens collected for routine purposes, such as for screening, diagnostic or monitoring purposes, are used to determine the reference intervals
• Reference: Clin Chem Lab Med 2018;57:20

Direct approach	Indirect approach
New data are generated from the population Higher cost to perform Preanalytical and analytical conditions may not match the routine conditions Hard to repeat in different locations Ethical issues Requires basic statistical knowledge and expertise	Use the data that are already generated Lower cost to perform Preanalytical and analytical conditions match the routine conditions Easy to repeat in different locations No ethical issues Requires significant statistical knowledge and expertise

Direct approach

1. Selection of reference individuals	Determine healthy candidates by medical history, physical examination and laboratory investigations Implement inclusion, exclusion and partitioning criteria Obtain informed consent from participants Take samples from reference individuals; 2 sampling approaches: A priori approach: inclusion of reference individuals is determined before samples are collected A posteriori approach: the definition of reference individuals includes information applied after the samples have been collected Reference: Turk J Biochem 2020;45:1
2. Analysis of the samples	Preanalytical considerations: Biological factors: circadian rhythms, fasting status, physical activities Methodological factors: collection techniques, types of additives, specimen handling, transportation, storage Analytical aspects: methods of measurement, equipment, reagents, calibration standards and calculation methods
3. Evaluation	Outlier removal: very important step to make RIs reliable Dixton's Q test: simple and effective Q = D/R (D is the absolute difference between the outlier in question and the closest number to it; R is the entire range of the observation) If D/R > 1/3, the outlier should be discarded Drawback of Dixton's Q test: if there is more than one outlier, the test become less sensitive Tukey fence method: more sophisticated Q1 = lower quartile, Q3 = higher quartile; IQR (interquartile range) = Q3 - Q1 If number < Q1 – 1.5 IQR or > Q3 + 1.5 IQR, it is an outlier and should be removed RIs calculation: parametric and nonparametric methods Parametric method: only apply to Gaussian distribution data Reference values need to be a normal distribution or transformed to a normal distribution Box-Cox power transformation is often used RIs = mean ± 1.96 SD Nonparametric method: applied to data irrespective of distribution characteristics IFCC recommended method for estimating reference intervals is a nonparametric method that essentially involves simply excluding the lowest and highest 2.5% of reference values Partitioning: stratification by age and gender is important for establishing more accurate RIs Calculate the confidence intervals of the lower and upper RIs; at least 120 samples are needed for analysis References: Biochem Med (Zagreb) 2016;26:5, Am J Clin Pathol 2010;133:180, CLSI: EP28 - Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory, 3rd Edition, 2010

Indirect approach

1. Select the population

Data from outpatient settings are preferred; pathological changes in admitted patient populations make the laboratory results less reliable Number of subjects: more is better; 1,000 may be small number, 10,000 can be seen as large number Stability: it is imperative to ensure that the analytical method and the population are stable during the time data are collected Partitioning: all data should be evaluated for cofactors such as age and gender; if standard deviation ratio between subgroups exceeds 1.5, a separated reference interval is highly recommended (Clin Chem 1990;36:265) Exclusions: eliminate results from subjects that are more likely to have diseases based on hospital settings, clinical information and other laboratory results

2. Statistical evaluation

Standard parametric and nonparametric statistics can be used in indirect RI studies; outlier removal is very important because standard statistic techniques are significantly affected by these extreme results Hoffmann method is a simple graphical method for estimating reference intervals from routine laboratory data Distribution of results could be represented by a mixture of 2 underlying Gaussian distributions, corresponding to the healthy and diseased subpopulations, with the healthy subpopulation dominating the sample Drawback of the Hoffmann procedure is that it is influenced by the size of the diseased subpopulation (Am J Clin Pathol 2019;151:328) Bhattacharya method is also a graphical method for estimating reference intervals Bhattacharya method has been shown to be less influenced by data not included in the Gaussian distribution compared with the Hoffmann method (Am J Clin Pathol 2019;151:328) Special programs: Arzideh developed a method that is more sophisticated than Hoffman's and Bhattacharya's First, kernel density estimation is used to generate the distribution of the combined data of the nondiseased and diseased subjects Central part of the distribution of all data should represent the nondiseased population Then, Box-Cox transformation is used to produce a truncated Gaussian distribution of the nondiseased values Finally, a goodness-of-fit statistic is applied to find the main part of the data Reference limits are calculated as the 2.5 and 97.5 percentiles of the estimated power normal distribution for the nondiseased values (Clin Chem Lab Med 2018;57:20)

	Reference interval	Sample size
Ceruloplasmin (mg/L)	159 - 414	603

A group of investigators from Saudi Arabia was studying the reference intervals of serum ceruloplasmin in adult healthy individuals. The results of their study are shown above. How many of the 603 subjects are expected to have a ceruloplasmin concentration of greater than 454 mg/L?

1. 3
2. 6
3. 9
4. 12

A. 3. The data show a normal distribution; mean: 286.5. By definition, reference interval = mean ± 1.96 SD → 1 SD = 65. Ceruloplasmin levels of more than 454 will be approximately 2.58 SD from the mean / median (454 - 286.5 = 167.5 = 2.58 x 65 = 2.58 SD). 99% of values will be between (mean - 2.58 SD) and (mean + 2.58 SD), 0.5% values will be < (mean - 2.58 SD) and 0.5% values will be > (mean + 2.58 SD). Number of values > 454 will be 0.5% x 603 (n) = 3.

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Reference: Validation of reference intervals and reportable range

Which of the following is a disadvantage of using the indirect approach to establish reference intervals?

1. Calculation method is complicated
2. More expensive to perform
3. Need informed consent to use samples
4. Repeating the evaluation is technically challenging

A. Calculation method is complicated

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Reference: Validation of reference intervals and reportable range