Evidence-based approach to harmonised reference intervals

(ii) The method for the determination of reference limits according to the original procedure and the conditions, which should be used. (iii) A simple procedure allowing the medical laboratories to fulfill the requirements of the regulation and standards. The updated document proposes to verify that published reference limits are applicable to the laboratory involved. Finally, the strengths and limits of the revised recommendations (especially the selection of the reference population, the maintenance of the analytical quality, the choice of the statistical method used…) will be briefly discussed.

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“…Koerbin et al [24] determined the bias between eight major analytical platforms using a similar method of comparison.…”

Section: Adeli Et Al and Those From Koerbin Et Al In Australiamentioning

confidence: 99%

Recommendation for the review of biological reference intervals in medical laboratories

Henny

Vassault²,

Boursier

et al. 2016

Clinical Chemistry and Laboratory Medicine (CCLM)

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“…Several studies have established reference intervals for classical chemistry values such as serum potassium and sodium levels, that are accepted as being suitable for use across institutions [24, 35–37]. Such reference intervals will be referred to as ‘harmonized’ hereafter.…”

Section: Methodsmentioning

confidence: 99%

“…Patients with the corresponding ICD9 and CPT codes must then be excluded from the cohort before the reference intervals are calculated. This a posteriori process has been shown to give accurate results [2, 12, 21], and is currently the most commonly used system for defining reference intervals from data, with the Australasian Association of Biochemists (AACB) suggesting the possible use of this method at an individual laboratory level [24]. However, this approach does not overcome the problem of scalability, as exclusion rules must be developed manually for each test and patient population of interest.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised learning method to identify reference intervals from a clinical database

Poole

Schroeder

Shah

2016

Journal of Biomedical Informatics

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Reference intervals are critical for the interpretation of laboratory results. The development of reference intervals using traditional methods is time consuming and costly. An alternative approach, known as an a posteriori method, requires an expert to enumerate diagnoses and procedures that can affect the measurement of interest. We develop a method, LIMIT, to use laboratory test results from a clinical database to identify ICD9 codes that are associated with extreme laboratory results, thus automating the a posteriori method. LIMIT was developed using sodium serum levels, and validated using potassium serum levels, both tests for which harmonized reference intervals already exist. To test LIMIT, reference intervals for total hemoglobin in whole blood were learned, and were compared with the hemoglobin reference intervals found using an existing a posteriori approach. In addition, prescription of iron supplements were used to identify individuals whose hemoglobin levels were low enough for a clinician to choose to take action. This prescription data indicating clinical action was then used to estimate the validity of the hemoglobin reference interval sets. Results show that LIMIT produces usable reference intervals for sodium, potassium and hemoglobin laboratory tests. The hemoglobin intervals produced using the data driven approaches consistently had higher positive predictive value and specificity in predicting an iron supplement prescription than the existing intervals. LIMIT represents a fast and inexpensive solution for calculating reference intervals, and shows that it is possible to use laboratory results and coded diagnoses to learn laboratory test reference intervals from clinical data warehouses.

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“…[6][7][8][9][10][11] While calculating the reference intervals, it is considered incorrect by NCCLS to use standard statistical methods and arbitrary implementations in order to divide the population into more homogenious sub-groups, thus suggesting the Sinton and Harris&Boyd methods for such cases [3]. The Harris&Boyd method was frequently used in recent studies [17][18][19][20][21][22][23]. Additionally, the Sinton method was used by Manning et al while the Lahti method was preferred by Ridefelt et al [24][25].…”

Section: Discussionmentioning

confidence: 99%

Determination of sub-groups in reference interval calculations and an application

Temel¹,

Kanik²,

Yıldırım³

et al. 2015

Turkish Journal of Biochemistry

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Objective: The main objective of this study is to determine the methods used for dividing reference data into sub-groups and propose a new solution. Therefore, the newly proposed method is used to determine age sub-groups while calculating the reference interval of AST variable.Methods: The data of this study are composed of the registered patients who applied only once to the polyclinics at the Health Research and Application Hospital, Clinical Bio-Chemistry Laboratory of Mersin University. The main variables selected for the study are sex, age and aspartate transaminase (AST). MARS 2.0 package software was used for the statistical analysis. Harris&Boyd method was used for sex and age while the MARS method was used for the determination of age sub-groups.Results: According to the Harris&Boyd method, it was concluded that it is not necessary to create sub-groups for sex and age variables. Age sub-groups are determined with the MARS method by taking into account the correlations between AST variables and age. Age sub-groups are calculated as 5-9, 10-18 and 19+. Correlation between the age and AST variable is investigated for these sub-groups. It is obtained that the correlation is statistically not significant for the 5-9 age group (p=0.3189), significant for the 10-18 age group (p<0.001) and significant for 19 and above (p<0.001).Conclusion: The selection of transformation and outlier value elimination method and especially determination criteria for separation of sub-groups are made according to researcher's subjective desicions in the application of many studies. Because of this reason, MARS can be used as an alternative and objective method for determining subgroups. In addition, results of MARS method being parallel with the findings of the producing company supported the usage of this method for determination of sub-groups.

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Evidence-based approach to harmonised reference intervals

Cited by 44 publications

References 40 publications

Recommendation for the review of biological reference intervals in medical laboratories

Recommendation for the review of biological reference intervals in medical laboratories

An unsupervised learning method to identify reference intervals from a clinical database

Determination of sub-groups in reference interval calculations and an application

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