Indirect reference intervals of plasma and serum thyrotropin (TSH) concentrations from intra-laboratory data bases from several German and Italian medical centres

2011 ◽  
Vol 49 (4) ◽  
Author(s):  
Farhad Arzideh ◽  
Werner Wosniok ◽  
Rainer Haeckel
Keyword(s):  
Laboratory Data ◽  
Reference Intervals ◽  
Data Bases ◽  
Indirect Reference

scholarly journals Separating disease and health for indirect reference intervals

2021 ◽  
Vol 45 (2) ◽  
pp. 55-68 ◽  
Author(s):  
Kenneth A. Sikaris
Keyword(s):  
A Priori ◽  
Laboratory Data ◽  
Reference Interval ◽  
Statistical Technique ◽  
Reference Intervals ◽  
Large Databases ◽  
Statistical Issues ◽  
Physiological Variations ◽  
Indirect Reference ◽  
Apparently Healthy

Abstract The indirect approach to defining reference intervals operates ‘a posteriori’, on stored laboratory data. It relies on being able to separate healthy and diseased populations using one or both of clinical techniques or statistical techniques. These techniques are also fundamental in a priori, direct reference interval approaches. The clinical techniques rely on using clinical data that is stored either in the electronic health record or within the laboratory database, to exclude patients with possible disease. It depends on the investigators understanding of the data and the pathological impacts on tests. The statistical technique relies on identifying a dominant, apparently healthy, typically Gaussian distribution, which is unaffected by the overlapping populations with higher (or lower) results. It depends on having large databases to give confidence in the extrapolation of the narrow portion of overall distribution representing unaffected individuals. The statistical issues involved can be complex, and can result in unintended bias, particularly when the impacts of disease and the physiological variations in the data are under appreciated.

Response to the editor: Limitations of the Hoffmann method for establishing reference intervals using clinical laboratory data

2019 ◽  
Vol 70 ◽  
pp. 51
Author(s):  
Ying Zhang ◽  
Weibo Ma ◽  
Guocheng Wang ◽  
Yaqi Lv ◽  
Yaguang Peng ◽  
...  
Keyword(s):  
Clinical Laboratory ◽  
Laboratory Data ◽  
Reference Intervals

A multi-stage Gaussian transformation algorithm for clinical laboratory data.

1982 ◽  
Vol 28 (8) ◽  
pp. 1735-1741 ◽  
Author(s):  
J C Boyd ◽  
D A Lacher
Keyword(s):  
Mean Squared Error ◽  
Clinical Laboratory ◽  
Statistical Tests ◽  
Random Noise ◽  
Laboratory Data ◽  
Reference Interval ◽  
Reference Intervals ◽  
Distributed Data ◽  
Multi Stage ◽  
Positive Coefficients

Abstract We have developed a multi-stage computer algorithm to transform non-normally distributed data to a normal distribution. This transformation is of value for calculation of laboratory reference intervals and for normalization of clinical laboratory variates before applying statistical procedures in which underlying data normality is assumed. The algorithm is able to normalize most laboratory data distributions with either negative or positive coefficients of skewness or kurtosis. Stepwise, a logarithmic transform removes asymmetry (skewness), then a Z-score transform and power function transform remove residual peakedness or flatness (kurtosis). Powerful statistical tests of data normality in the procedure help the user evaluate both the necessity for and the success of the data transformation. Erroneous assessments of data normality caused by rounded laboratory test values have been minimized by introducing computer-generated random noise into the data values. Reference interval endpoints that were estimated parametrically (mean +/- 2 SD) by using successfully transformed data were found to have a smaller root-mean-squared error than those estimated by the non-parametric percentile technique.

scholarly journals Establishment of PIVKA-Ⅱ Reference Intervals From Hospital-stored Data: A Comparison Analysis

2021 ◽  
Author(s):  
Lewei Zhou ◽  
Qiyuan Su ◽  
Yan Yao ◽  
Meixian Xiang ◽  
Jiesheng Zhen ◽  
...  
Keyword(s):  
Clinical Laboratory ◽  
Reference Intervals ◽  
Female Group ◽  
Comparison Analysis ◽  
Test Items ◽  
Health Group ◽  
Significant Difference ◽  
The Difference ◽  
Gender Specific ◽  
Indirect Reference

Abstract Objective The authors aimed to explore methods to establish indirect reference intervals for PIVKA-II from hospital-stored data.Method 7623 patient specimens of the Renmin Hospital of Wuhan University were collected. Indirect reference intervals were established based on the hospital-stored data with four different methods, including the Hoffmann method (HM), revised Hoffmann method (HMCDF), E-M algorithm-based method (EMBCT), and a recent estimator (KOSMIC). According to CLSI C28-A3 guidelines, 369 healthy specimens were collected. The authors tested the difference between reference intervals of gender-specific and age-specific subgroups using Harris and Boyd's test. Finally, the averaging result of estimates was calculated according to how likely each model was.Results The indirect reference intervals of PIVKA-II based on LIS data were 0 to 35.30 mAU/mL (HM), 0 to 31.48 mAU/mL (HMCDF), 0 to 30.78 mAU/mL (EMBCT), 0 to 36.17 mAU/mL (KOSMIC) and 0 to 31.48 mAU/mL (averaging) respectively, and the reference intervals based on healthy group were 0 to 32 mAU/mL. Compared with HM, EMBCT and KOSMIC, HMCDF and the averaging result was closer to those of the health group. Significant difference was detected between gender-partitioned subgroups, and the reference upper limit in the female group was smaller than the male group.Conclusions The authors established the indirect reference intervals of PIVKA-II for the Wuhan population, which could be used to the clinical reference intervals. The framework proposed could help clinical laboratory set their reference intervals of test items.

scholarly journals Gender- and age-specific reference intervals of common biochemical analytes in chinese population – derivation using real laboratory data

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Danchen Wang ◽  
Chaochao Ma ◽  
Yutong Zou ◽  
Songlin Yu ◽  
Honglei Li ◽  
...  
Keyword(s):  
Low Density Lipoprotein ◽  
Laboratory Data ◽  
Density Lipoprotein ◽  
Inorganic Phosphorus ◽  
Reference Intervals ◽  
Peking Union Medical College ◽  
Lipoprotein Cholesterol ◽  
Specific Reference ◽  
College Hospital ◽  
Medical College

Summary Background Indirect sampling methods are not only inexpensive but also efficient for establishing reference intervals (RIs) using clinical data. This study was conducted to select fully normal records to establish age- and gender-specific RIs for common biochemical analytes by laboratory data mining. Methods In total, 280,206 records from 2014 to 2018 were obtained from Peking Union Medical College Hospital. Common biochemical analytes [total protein, albumin, total bilirubin (TBil), direct bilirubin (DBil), alanine aminotransferase (ALT), glutamyltranspeptidase (GGT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), potassium, sodium, chlorine, calcium, urea, glucose, uric acid (UA), inorganic phosphorus, creatinine (Cr), total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol] were measured using an automatic analyzer. Sources of variation were identified by multiple regression analysis. The 2.5th and 97.5th percentiles were calculated as the lower and upper limits of the RIs, respectively. Results Metode indirektnog uzorkovanja nisu samo jeftine, već su i efikasne za uspostavljanje referentnih intervala (RI) korišćenjem velike količine kliničkih podataka. Ova studija je sprovedena sa ciljem da se odaberu potpuno normalni pojedinačni zapisi da bi se utvrdio RI za starost i pol za zajedničke biohemijske analite pomoću laboratorijski dobijenih podataka. Conclusions Ukupno je uzeto 280.206 pojedinačnih rezultata u periodu od 2014. do 2018. godine iz bolnice Peking Union Medical College-a. Uobičajeni biohemijski analiti [ukupni protein, albumin, ukupni bilirubin (TBil), direktni bilirubin (DBil), alanin aminotransferaza (ALT), glutamiltranspeptidaza (GGT), alkalna fosfataza (ALP), aspartat aminotransferaza (AST), laktat dehidrogenaza (LAT) kalijum, natrijum, hlor, kalcijum, ureja, glukoza, mokraćna kiselina (UA), neorganski fosfor, kreatinin (Cr), ukupni hole sterol, trigliceridi, lipoprotein holesterola visoke gustine i lipoprotein holesterola niske gustine] mereni su korišćenjem automatskog analizatora. Izvori varijacije identifikovani su višestrukom regresionom analizom. Postoci (2,5 i 97,5) su izračunati kao donja i gornja granica RI, svaki ponaosob.

Pediatric reference intervals for alkaline phosphatase

2017 ◽  
Vol 55 (1) ◽  
pp. 102-110 ◽  
Author(s):  
Jakob Zierk ◽  
Farhad Arzideh ◽  
Rainer Haeckel ◽  
Holger Cario ◽  
Michael C. Frühwald ◽  
...  
Keyword(s):  
Alkaline Phosphatase ◽  
Phosphatase Activity ◽  
Alkaline Phosphatase Activity ◽  
Clinical Laboratory ◽  
Clinical Care ◽  
Tertiary Care ◽  
Laboratory Data ◽  
Reference Intervals ◽  
Photometric Method ◽  
Laboratory Service

Abstract Background: Interpretation of alkaline phosphatase activity in children is challenging due to extensive changes with growth and puberty leading to distinct sex- and age-specific dynamics. Continuous percentile charts from birth to adulthood allow accurate consideration of these dynamics and seem reasonable for an analyte as closely linked to growth as alkaline phosphatase. However, the ethical and practical challenges unique to pediatric reference intervals have restricted the creation of such percentile charts, resulting in limitations when clinical decisions are based on alkaline phosphatase activity. Methods: We applied an indirect method to generate percentile charts for alkaline phosphatase activity using clinical laboratory data collected during the clinical care of patients. A total of 361,405 samples from 124,440 patients from six German tertiary care centers and one German laboratory service provider measured between January 2004 and June 2015 were analyzed. Measurement of alkaline phosphatase activity was performed on Roche Cobas analyzers using the IFCC’s photometric method. Results: We created percentile charts for alkaline phosphatase activity in girls and boys from birth to 18 years which can be used as reference intervals. Additionally, data tables of age- and sex-specific percentile values allow the incorporation of these results into laboratory information systems. Conclusions: The percentile charts provided enable the appropriate differential diagnosis of changes in alkaline phosphatase activity due to disease and changes due to physiological development. After local validation, integration of the provided percentile charts into result reporting facilitates precise assessment of alkaline phosphatase dynamics in pediatrics.

Use of total patient data for indirect estimation of reference intervals for 40 clinical chemical analytes in Turkey

2006 ◽  
Vol 44 (7) ◽  
Author(s):  
Yesim Ozarda Ilcol ◽  
Diler Aslan
Keyword(s):  
Binding Capacity ◽  
Clinical Chemistry ◽  
Reference Intervals ◽  
Thyroid Stimulating Hormone ◽  
Patient Data ◽  
Clinical Chemical ◽  
Laboratory Results ◽  
Total Triiodothyronine ◽  
Iron Binding Capacity ◽  
Indirect Reference

AbstractIn the present study we used patient data to calculate laboratory-specific indirect reference intervals. These values were compared with reference intervals obtained for a healthy group according to recommendations of the International Federation of Clinical Chemistry and Laboratory Medicine and manufacturer suggestions. Laboratory results (422,919 records) from all subjects of 18–45years of age over a 1-year period were retrieved from our laboratory information system and indirect reference intervals for 40 common analytes were estimated using a modified Bhattacharya procedure. Indirect reference intervals for most of the biochemical analytes were comparable, with small differences in lower [alkaline phosphatase (ALP) (male), alanine aminotransferase (ALT), creatine kinase, iron (male), total iron-binding capacity, folic acid, calcium (female), lactate dehydrogenase (LDH), lipoprotein (a) [Lp(a)], thyroid-stimulating hormone (TSH), total triiodothyronine (TClin Chem Lab Med 2006;44:867–76.

Selecting clinically relevant populations for reference intervals

2004 ◽  
Vol 42 (7) ◽  
Author(s):  
Robert F. Ritchie ◽  
Glenn Palomaki
Keyword(s):  
Laboratory Data ◽  
Reference Intervals ◽  
Laboratory Performance ◽  
Professional Groups ◽  
Usable Information ◽  
Life Threatening ◽  
Precision And Accuracy ◽  
Selection Of

AbstractThe selection of individuals to represent a group for any purpose can commit the effort to success or failure from the very outset. The description of this group must reflect what is, in reality, the purpose for which the group is being studied. Considering that reference intervals are designed to assist clinicians during diagnostic triage, criteria must reflect the nature of the subjects entering the system for care. The majority of people seeking care have non-life-threatening, yet troublesome complaints. In other words, persons who are in aCompounding this awkward situation has been the conviction that by increasing laboratory precision and accuracy, the utility of laboratory data, reference intervals in particular, will improve clinical understanding. This has, after many years, not proven to be the case. What has become very evident, however, is that unsustainably large sums of money are being devoted to improving laboratory performance. To what end, when we are unable to grasp the significance of what is available now including published reference intervals for common test values?A realistic perspective on the value of prevailing laboratory performance, as illustrated by reference intervals, must be adopted. The obligation of laboratories, its regulators and professional groups is to create a sustainable mechanism to provide clinically usable information, in a timely and affordable manner, to our population and to those legions of less fortunate humans.

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