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Rigo-Bonnin R, Aliart-Fernández I, Escalante-Vilanova A, Brunet M, Parra-Robert M, Morales-Ruiz M. Calculation of reference intervals for the concentrations of α-tocopherol and retinol in serum using indirect data-mining procedures. Clin Chim Acta 2024; 561:119822. [PMID: 38908772 DOI: 10.1016/j.cca.2024.119822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 06/24/2024]
Abstract
BACKGROUND Establishing adequate reference intervals (RIs) for vitamins A and E is essential for diagnosing and preventing deficiencies. Due to the current boom in data mining and its easy applicability, more laboratories are establishing RIs using indirect methods. Our study aims to obtain RIs using four indirect data-mining procedures (Bhattacharya, Hoffmann, Kosmic, and RefineR) for vitamins A and E. MATERIAL AND METHODS 8943 individuals were collected to establish the RIs. After using different data cleaning steps and checking whether these data should be divided according to age and gender based on multiple linear regression and variance component analyses, indirect RIs were calculated using specific Excel spreadsheets or R-packages software. RESULTS A total of 2004 records were eligible. For vitamin A, the RIs obtained were (1.11 - 2.68) µmol/L, (1.13 - 2.70) µmol/L, (1.13 - 2.71) µmol/L, and (1.17 - 2.66) µmol/L using the Bhattacharya, Hoffmann, Kosmic and RefineR approaches, respectively. For vitamin E, these intervals were (17.3 - 49.9) µmol/L (Bhattacharya), (17.3 - 48.9) µmol/L (Hoffmann), (19.6 - 50.3) µmol/L (Kosmic), and (19.4 - 50.9) µmol/L (RefineR). In all cases, the RIs were comparable. CONCLUSIONS Suitable RIs for vitamins A and E were calculated using four indirect methods that are suitable and adapted to our population's demographic characteristics.
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Affiliation(s)
- Raúl Rigo-Bonnin
- Servei de Bioquímica i Genètica Molecular, Centre de Diagnòstic Biomèdic (CDB), Hospital Clínic, Barcelona, Spain.
| | - Irene Aliart-Fernández
- Servei de Bioquímica i Genètica Molecular, Centre de Diagnòstic Biomèdic (CDB), Hospital Clínic, Barcelona, Spain
| | - Anna Escalante-Vilanova
- Servei de Bioquímica i Genètica Molecular, Centre de Diagnòstic Biomèdic (CDB), Hospital Clínic, Barcelona, Spain
| | - Mercè Brunet
- Servei de Bioquímica i Genètica Molecular, Centre de Diagnòstic Biomèdic (CDB), Hospital Clínic, Barcelona, Spain; Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Marina Parra-Robert
- Servei de Bioquímica i Genètica Molecular, Centre de Diagnòstic Biomèdic (CDB), Hospital Clínic, Barcelona, Spain; Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Manuel Morales-Ruiz
- Servei de Bioquímica i Genètica Molecular, Centre de Diagnòstic Biomèdic (CDB), Hospital Clínic, Barcelona, Spain; Fundació de Recerca Clínic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Spain
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Ma C, Yu Z, Qiu L. Development of next-generation reference interval models to establish reference intervals based on medical data: current status, algorithms and future consideration. Crit Rev Clin Lab Sci 2024; 61:298-316. [PMID: 38146650 DOI: 10.1080/10408363.2023.2291379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/30/2023] [Indexed: 12/27/2023]
Abstract
Evidence derived from laboratory medicine plays a pivotal role in the diagnosis, treatment monitoring, and prognosis of various diseases. Reference intervals (RIs) are indispensable tools for assessing test results. The accuracy of clinical decision-making relies directly on the appropriateness of RIs. With the increase in real-world studies and advances in computational power, there has been increased interest in establishing RIs using big data. This approach has demonstrated cost-effectiveness and applicability across diverse scenarios, thereby enhancing the overall suitability of the RI to a certain extent. However, challenges persist when tests results are influenced by age and sex. Reliance on a single RI or a grouping of RIs based on age and sex can lead to erroneous interpretation of results with significant implications for clinical decision-making. To address this issue, the development of next generation of reference interval models has arisen at an historic moment. Such models establish a curve relationship to derive continuously changing reference intervals for test results across different age and sex categories. By automatically selecting appropriate RIs based on the age and sex of patients during result interpretation, this approach facilitates clinical decision-making and enhances disease diagnosis/treatment as well as health management practices. Development of next-generation reference interval models use direct or indirect sampling techniques to select reference individuals and then employed curve fitting methods such as splines, polynomial regression and others to establish continuous models. In light of these studies, several observations can be made: Firstly, to date, limited interest has been shown in developing next-generation reference interval models, with only a few models currently available. Secondly, there are a wide range of methods and algorithms for constructing such models, and their diversity may lead to confusion. Thirdly, the process of constructing next-generation reference interval models can be complex, particularly when employing indirect sampling techniques. At present, normative documents pertaining to the development of next-generation reference interval models are lacking. In summary, this review aims to provide an overview of the current state of development of next-generation reference interval models by defining them, highlighting inherent advantages, and addressing existing challenges. It also describes the process, advanced algorithms for model building, the tools required and the diagnosis and validation of models. Additionally, a discussion on the prospects of utilizing big data for developing next-generation reference interval models is presented. The ultimate objective is to equip clinical laboratories with the theoretical framework and practical tools necessary for developing and optimizing next-generation reference interval models to establish next-generation reference intervals while enhancing the use of medical data resources to facilitate precision medicine.
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Affiliation(s)
- Chaochao Ma
- Department of Laboratory Medicine, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Zheng Yu
- Department of Operations Research and Financial Engineering, Princeton University, Princeton University, Princeton, NJ, USA
| | - Ling Qiu
- Department of Laboratory Medicine, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Science, Beijing, China
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Freire MDC, Dias PRTP, Souza TSP, Hirose CK, Araujo PBMC, Neves MFT. Insulin reference intervals in Brazilian adolescents by direct and indirect approaches: validation of a data mining method from laboratory data. J Pediatr (Rio J) 2024:S0021-7557(24)00042-1. [PMID: 38670169 DOI: 10.1016/j.jped.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 04/28/2024] Open
Abstract
OBJECTIVE To determine reference intervals (RI) for fasting blood insulin (FBI) in Brazilian adolescents, 12 to 17 years old, by direct and indirect approaches, and to validate indirectly determined RI. METHODS Two databases were used for RI determination. Database 1 (DB1), used to obtain RI through a posteriori direct method, consisted of prospectively selected healthy individuals. Database 2 (DB2) was retrospectively mined from an outpatient laboratory information system (LIS) used for the indirect method (Bhattacharya method). RESULTS From DB1, 29345 individuals were enrolled (57.65 % female) and seven age ranges and sex partitions were statistically determined according to mean FBI values: females: 12 and 13 years-old, 14 years-old, 15 years-old, 16 and 17 years-old; and males: 12, 13 and 14 years-old, 15 years-old, 16 and 17 years-old. From DB2, 5465 adolescents (67.5 % female) were selected and grouped according to DB1 partitions. The mean FBI level was significantly higher in DB2, on all groups. The RI upper limit (URL) determined by Bhattacharya method was slightly lower than the 90 % CI URL directly obtained on DB1, except for group female 12 and 13 years old. High agreement rates for diagnosing elevated FBI in all groups on DB1 validated indirect RI presented. CONCLUSION The present study demonstrates that Bhattacharya indirect method to determine FBI RI in adolescents can overcome some of the difficulties and challenges of the direct approach.
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Affiliation(s)
- Monica D C Freire
- Universidade do Estado do Rio de Janeiro, Pós Graduação em Ciências Médicas, Rio de Janeiro, RJ, Brazil.
| | - Paulo R T P Dias
- Universidade Federal Fluminense, Instituto de Saúde Coletiva, Departamento de Epidemiologia e Bioestatística, Niterói, RJ, Brazil; Instituto de Ensino e Pesquisa DASA, São Paulo, SP, Brazil; Universidade do Estado do Rio de Janeiro, Núcleo de Estudos e Pesquisas em Atenção ao Uso de Drogas, Rio de Janeiro, RJ, Brazil
| | - Thiago S P Souza
- Universidade do Estado do Rio de Janeiro, Instituto de Matemática e Estatística, Rio de Janeiro, RJ, Brazil
| | | | - Paula B M C Araujo
- Universidade Federal do Rio de Janeiro, Faculdade de Medicina, Pós-graduação em Endocrinologia, Rio de Janeiro, RJ, Brazil
| | - Mario F T Neves
- Universidade do Estado do Rio de Janeiro, Faculdade de Ciências Médicas, Rio de Janeiro, RJ, Brazil
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Chen J, Fan L, Yang Z, Yang D. Comparison of results and age-related changes in establishing reference intervals for CEA, AFP, CA125, and CA199 using four indirect methods. Pract Lab Med 2024; 38:e00353. [PMID: 38221990 PMCID: PMC10787276 DOI: 10.1016/j.plabm.2023.e00353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 01/16/2024] Open
Abstract
•The reference intervals calculated using RefineR, Kosmic, TMC, and non-parametric methods are similar.•TMC algorithm is more robust, demonstrates a high pass rate among the four methods and has the ability to automatically isolate outliers.•The reference intervals of CA125 and CA199 showed significant differences between age and sex.
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Affiliation(s)
- Juping Chen
- Department of Laboratory Medicine, Liangzhu Branch of the First Affiliated Hospital of Zhejiang University, Zhejiang, China
- School of Public Health, Zhejiang University School of Medicine, Zhejiang, China
| | - Lina Fan
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, China
| | - Zheng Yang
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, China
| | - Dagan Yang
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, China
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