scholarly journals Verification of reference intervals in routine clinical laboratories: practical challenges and recommendations

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Yesim Ozarda ◽  
Victoria Higgins ◽  
Khosrow Adeli
Keyword(s):  
Clinical Laboratory ◽  
Local Population ◽  
Age Groups ◽  
External Source ◽  
Reference Intervals ◽  
Laboratory Method ◽  
Test Results ◽  
Multicenter Studies ◽  
Clinical Laboratories ◽  
Laboratory Test Results

Abstract Reference intervals (RIs) are fundamental tools used by healthcare and laboratory professionals to interpret patient laboratory test results, ideally enabling differentiation of healthy and unhealthy individuals. Under optimal conditions, a laboratory should perform its own RI study to establish RIs specific for its method and local population. However, the process of developing RIs is often beyond the capabilities of an individual laboratory due to the complex, expensive and time-consuming process to develop them. Therefore, a laboratory can alternatively verify RIs established by an external source. Common RIs can be established by large, multicenter studies and can subsequently be received by local laboratories using various verification procedures. The standard approach to verify RIs recommended by the Clinical Laboratory Standards Institute (CLSI) EP28-A3c guideline for routine clinical laboratories is to collect and analyze a minimum of 20 samples from healthy subjects from the local population. Alternatively, “data mining” techniques using large amounts of patient test results can be used to verify RIs, considering both the laboratory method and local population. Although procedures for verifying RIs in the literature and guidelines are clear in theory, gaps remain for the implementation of these procedures in routine clinical laboratories. Pediatric and geriatric age-groups also continue to pose additional challenges in respect of acquiring and verifying RIs. In this article, we review the current guidelines/approaches and challenges to RI verification and provide a practical guide for routine implementation in clinical laboratories.

Establishing and using reference intervals

2020 ◽  
Vol 45 (1) ◽  
pp. 1-10
Author(s):  
Yesim Ozarda
Keyword(s):  
Clinical Chemistry ◽  
Laboratory Data ◽  
Age Groups ◽  
Clinical Decision ◽  
Reference Population ◽  
External Source ◽  
Reference Intervals ◽  
Multicenter Studies ◽  
Indirect Methods ◽  
Laboratory Test Results

AbstractReference intervals (RIs) and clinical decision limits (CDLs) are fundamental tools used by healthcare and laboratory professionals to interpret patient laboratory test results. The traditional method for establishing RIs, known as the direct approach, is based on collecting samples from members of a preselected reference population, making the measurements and then determining the intervals. For challenging groups such as pediatric and geriatric age groups, indirect methods are appointed for the derivation of RIs in the EP28-A3c guideline. However, there has been an increasing demand to use the indirect methods of deriving RIs by the use of routine laboratory data stored in the laboratory information system. International Federation of Clinical Chemistry (IFCC), Committee on Reference Intervals and Decision Limits (C-RIDL) is currently working on the study for the comparison of the conventional (direct) and alternative (indirect) approaches for the determination of reference intervals. As a matter of fact that, the process of developing RIs is often beyond the capabilities of an individual laboratory due to the complex, expensive and time-consuming process to develop them. Therefore, a laboratory can alternatively transfer and verify RIs established by an external source (i.e. manufacturers’ package inserts, publications). IFCC, C-RIDL has focused primarily on RIs and has performed multicenter studies to obtain common RIs in recent years. However, as the broader responsibility of the Committee, from its name, includes “decision limits”, the C-RIDL also emphasizes the importance of the correct use of both RIs and CDLs and to encourage laboratories to specify the appropriate information to clinicians as needed.

scholarly journals LabRS: A Rosetta stone for retrospective standardization of clinical laboratory test results

2017 ◽  
Vol 25 (2) ◽  
pp. 121-126 ◽  
Author(s):  
Ronald George Hauser ◽  
Douglas B Quine ◽  
Alex Ryder
Keyword(s):  
Laboratory Tests ◽  
Standardized Test ◽  
Clinical Laboratory ◽  
Secondary Data ◽  
The United States ◽  
Test Results ◽  
Original Result ◽  
Clinical Laboratories ◽  
Laboratory Test Results ◽  
Standardization Process

Abstract Objective Clinical laboratories in the United States do not have an explicit result standard to report the 7 billion laboratory tests results they produce each year. The absence of standardized test results creates inefficiencies and ambiguities for secondary data users. We developed and tested a tool to standardize the results of laboratory tests in a large, multicenter clinical data warehouse. Methods Laboratory records, each of which consisted of a laboratory result and a test identifier, from 27 diverse facilities were captured from 2000 through 2015. Each record underwent a standardization process to convert the original result into a format amenable to secondary data analysis. The standardization process included the correction of typos, normalization of categorical results, separation of inequalities from numbers, and conversion of numbers represented by words (eg, “million”) to numerals. Quality control included expert review. Results We obtained 1.266 × 109 laboratory records and standardized 1.252 × 109 records (98.9%). Of the unique unstandardized records (78.887 × 103), most appeared <5 times (96%, eg, typos), did not have a test identifier (47%), or belonged to an esoteric test with <100 results (2%). Overall, these 3 reasons accounted for nearly all unstandardized results (98%). Conclusion Current results suggest that the tool is both scalable and generalizable among diverse clinical laboratories. Based on observed trends, the tool will require ongoing maintenance to stay current with new tests and result formats. Future work to develop and implement an explicit standard for test results would reduce the need to retrospectively standardize test results.

High-resolution pediatric reference intervals for 15 biochemical analytes described using fractional polynomials

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jakob Zierk ◽  
Hannsjörg Baum ◽  
Alexander Bertram ◽  
Martin Boeker ◽  
Armin Buchwald ◽  
...  
Keyword(s):  
Information Systems ◽  
Laboratory Test ◽  
Clinical Decision Making ◽  
Age Groups ◽  
Clinical Decision ◽  
Reference Intervals ◽  
Test Results ◽  
Laboratory Test Results ◽  
Glutamyl Transferase ◽  
Laboratory Information Systems

Abstract Objectives Assessment of children’s laboratory test results requires consideration of the extensive changes that occur during physiological development and result in pronounced sex- and age-specific dynamics in many biochemical analytes. Pediatric reference intervals have to account for these dynamics, but ethical and practical challenges limit the availability of appropriate pediatric reference intervals that cover children from birth to adulthood. We have therefore initiated the multi-center data-driven PEDREF project (Next-Generation Pediatric Reference Intervals) to create pediatric reference intervals using data from laboratory information systems. Methods We analyzed laboratory test results from 638,683 patients (217,883–982,548 samples per analyte, a median of 603,745 test results per analyte, and 10,298,067 test results in total) performed during patient care in 13 German centers. Test results from children with repeat measurements were discarded, and we estimated the distribution of physiological test results using a validated statistical approach (kosmic). Results We report continuous pediatric reference intervals and percentile charts for alanine transaminase, aspartate transaminase, lactate dehydrogenase, alkaline phosphatase, γ-glutamyl-transferase, total protein, albumin, creatinine, urea, sodium, potassium, calcium, chloride, anorganic phosphate, and magnesium. Reference intervals are provided as tables and fractional polynomial functions (i.e., mathematical equations) that can be integrated into laboratory information systems. Additionally, Z-scores and percentiles enable the normalization of test results by age and sex to facilitate their interpretation across age groups. Conclusions The provided reference intervals and percentile charts enable precise assessment of laboratory test results in children from birth to adulthood. Our findings highlight the pronounced dynamics in many biochemical analytes in neonates, which require particular consideration in reference intervals to support clinical decision making most effectively.

scholarly journals Creating ethnicity-specific reference intervals for lab tests from EHR data

2017 ◽  
Author(s):  
Nadav Rappoport ◽  
Hyojung Paik ◽  
Boris Oskotsky ◽  
Ruth Tor ◽  
Elad Ziv ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Clinical Laboratory ◽  
Local Population ◽  
Reference Intervals ◽  
Medical Decision ◽  
Specific Reference ◽  
Test Results ◽  
Electronic Health Record Data ◽  
Novel Method

AbstractThe results of clinical lab tests are an essential component of medical decision-making. To guide interpretation, test results are returned with reference intervals defined by the range in which 95% of values occur in healthy individuals. Clinical laboratories often set their own reference intervals to accommodate local population and instruments variations. This approach is costly and can be biased. We describe a novel data-driven method for using electronic health record data to extract healthy patients’ information to define reference intervals. We found that the distributions of many clinical lab tests differ among self-identified racial and ethnic groups (SIREs) in healthy patients. Finally, we derived SIRE-specific reference intervals and provide evidence that these intervals have clinical prognostic value. Specifically, we show that for two lab tests, serum creatinine level and hemoglobin A1C, SIRE-specific reference intervals are more predictive for need for dialysis and development type 2 diabetes than existing reference intervals.One Sentence SummaryA novel method for defining population-specific reference intervals of common clinical laboratory tests from electronical health records has better prognostic value than existing reference intervals.

scholarly journals Comparing Ethnicity-Specific Reference Intervals for Clinical Laboratory Tests from EHR Data

2018 ◽  
Vol 3 (3) ◽  
pp. 366-377 ◽  
Author(s):  
Nadav Rappoport ◽  
Hyojung Paik ◽  
Boris Oskotsky ◽  
Ruth Tor ◽  
Elad Ziv ◽  
...  
Keyword(s):  
Race And Ethnicity ◽  
Laboratory Tests ◽  
Clinical Laboratory ◽  
Local Population ◽  
Reference Intervals ◽  
Medical Decision ◽  
Specific Reference ◽  
Healthy Individuals ◽  
Electronic Health Record Data ◽  
Novel Approach

Abstract Background The results of clinical laboratory tests are an essential component of medical decision-making. To guide interpretation, test results are returned with reference intervals defined by the range in which the central 95% of values occur in healthy individuals. Clinical laboratories often set their own reference intervals to accommodate variation in local population and instrumentation. For some tests, reference intervals change as a function of sex, age, and self-identified race and ethnicity. Methods In this work, we develop a novel approach, which leverages electronic health record data, to identify healthy individuals and tests for differences in laboratory test values between populations. Results We found that the distributions of >50% of laboratory tests with currently fixed reference intervals differ among self-identified racial and ethnic groups (SIREs) in healthy individuals. Conclusions Our results confirm the known SIRE-specific differences in creatinine and suggest that more research needs to be done to determine the clinical implications of using one-size-fits-all reference intervals for other tests with SIRE-specific distributions.

Herbal Remedies: Effects on Clinical Laboratory Tests

2006 ◽  
Vol 130 (4) ◽  
pp. 521-528 ◽  
Author(s):  
Amitava Dasgupta ◽  
David W. Bernard
Keyword(s):  
Chinese Medicine ◽  
Laboratory Test ◽  
Alternative Medicine ◽  
Clinical Laboratory ◽  
Herbal Remedies ◽  
Test Results ◽  
Abnormal Laboratory ◽  
St John's Wort ◽  
Laboratory Test Results ◽  
Laboratory Results

AbstractContext.—Complementary and alternative medicine (herbal medicines) can affect laboratory test results by several mechanisms.Objective.—In this review, published reports on effects of herbal remedies on abnormal laboratory test results are summarized and commented on.Data Sources.—All published reports between 1980 and 2005 with the key words herbal remedies or alternative medicine and clinical laboratory test, clinical chemistry test, or drug-herb interaction were searched through Medline. The authors' own publications were also included. Important results were then synthesized.Data Synthesis.—Falsely elevated or falsely lowered digoxin levels may be encountered in a patient taking digoxin and the Chinese medicine Chan Su or Dan Shen, owing to direct interference of a component of Chinese medicine with the antibody used in an immunoassay. St John's wort, a popular herbal antidepressant, increases clearance of many drugs, and abnormally low cyclosporine, digoxin, theophylline, or protease inhibitor concentrations may be observed in a patient taking any of these drugs in combination with St John's wort. Abnormal laboratory results may also be encountered owing to altered pathophysiology. Kava-kava, chaparral, and germander cause liver toxicity, and elevated alanine aminotransferase, aspartate aminotransferase, and bilirubin concentrations may be observed in a healthy individual taking such herbal products. An herbal product may be contaminated with a Western drug, and an unexpected drug level (such as phenytoin in a patient who never took phenytoin but took a Chinese herb) may confuse the laboratory staff and the clinician.Conclusions.—Use of alternative medicines may significantly alter laboratory results, and communication among pathologists, clinical laboratory scientists, and physicians providing care to the patient is important in interpreting these results.

scholarly journals Personalized Reference Intervals in Laboratory Medicine: A New Model Based on Within-Subject Biological Variation

2020 ◽  
Author(s):  
Abdurrahman Coşkun ◽  
Sverre Sandberg ◽  
Ibrahim Unsal ◽  
Coskun Cavusoglu ◽  
Mustafa Serteser ◽  
...  
Keyword(s):  
Steady State ◽  
Personalized Medicine ◽  
Clinical Chemistry ◽  
Reference Interval ◽  
Reference Intervals ◽  
Biological Variation ◽  
Test Results ◽  
Steady State Condition ◽  
Laboratory Test Results ◽  
Measurement Results

Abstract Background The concept of personalized medicine has received widespread attention in the last decade. However, personalized medicine depends on correct diagnosis and monitoring of patients, for which personalized reference intervals for laboratory tests may be beneficial. In this study, we propose a simple model to generate personalized reference intervals based on historical, previously analyzed results, and data on analytical and within-subject biological variation. Methods A model using estimates of analytical and within-subject biological variation and previous test results was developed. We modeled the effect of adding an increasing number of measurement results on the estimation of the personal reference interval. We then used laboratory test results from 784 adult patients (>18 years) considered to be in a steady-state condition to calculate personalized reference intervals for 27 commonly requested clinical chemistry and hematology measurands. Results Increasing the number of measurements had little impact on the total variation around the true homeostatic set point and using ≥3 previous measurement results delivered robust personalized reference intervals. The personalized reference intervals of the study participants were different from one another and, as expected, located within the common reference interval. However, in general they made up only a small proportion of the population-based reference interval. Conclusions Our study shows that, if using results from patients in steady state, only a few previous test results and reliable estimates of within-subject biological variation are required to calculate personalized reference intervals. This may be highly valuable for diagnosing patients as well as for follow-up and treatment.

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