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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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Miao Q, Lei S, Chen F, Niu Q, Luo H, Cai B. A preliminary study on the reference intervals of serum tumor marker in apparently healthy elderly population in southwestern China using real-world data. BMC Cancer 2024; 24:657. [PMID: 38811867 PMCID: PMC11137896 DOI: 10.1186/s12885-024-12408-1] [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: 02/05/2024] [Accepted: 05/21/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND The aim is to establish and verify reference intervals (RIs) for serum tumor markers for an apparently healthy elderly population in Southwestern China using an indirect method. METHODS Data from 35,635 apparently healthy elderly individuals aged 60 years and above were obtained in West China Hospital from April 2020 to December 2021. We utilized the Box-Cox conversion combined with the Tukey method to normalize the data and eliminate outliers. Subgroups are divided according to gender and age to examine the division of RIs. The Z-test was used to compare differences between groups, and 95% distribution RIs were calculated using a nonparametric method. RESULTS In the study, we observed that the RIs for serum ferritin and Des-γ-carboxy prothrombin (DCP) were wider for men, ranging from 64.18 to 865.80 ng/ml and 14.00 to 33.00 mAU/ml, respectively, compared to women, whose ranges were 52.58 to 585.88 ng/ml and 13.00 to 29.00 mAU/ml. For other biomarkers, the overall RIs were established as follows: alpha-fetoprotein (AFP) 0-6.75 ng/ml, carcinoembryonic antigen (CEA) 0-4.85 ng/ml, carbohydrate antigen15-3 (CA15-3) for females 0-22.00 U/ml, carbohydrate antigen19-9 (CA19-9) 0-28.10 U/ml, carbohydrate antigen125 (CA125) 0-20.96 U/ml, cytokeratin 19 fragment (CYFRA21-1) 0-4.66 U/ml, neuron-specific enolase (NSE) 0-19.41 ng/ml, total and free prostate-specific antigens (tPSA and fPSA) for males 0-5.26 ng/ml and 0-1.09 ng/ml. The RIs for all these biomarkers have been validated through our rigorous processes. CONCLUSION This study preliminarily established 95% RIs for an apparently healthy elderly population in Southwestern China. Using real-world data and an indirect method, simple and reliable RIs for an elderly population can be both established and verified, which are suitable for application in various clinical laboratories.
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Affiliation(s)
- Qiang Miao
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Sichuan Clinical Research Center for Laboratory Medicine, Chengdu, Sichuan, China
- Clinical Laboratory Medicine Research Center of West China Hospital, No.37, Guoxue Xiang, Wuhou District, Chengdu, Sichuan, 610041, China
| | - Shuting Lei
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Fengyu Chen
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qian Niu
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Sichuan Clinical Research Center for Laboratory Medicine, Chengdu, Sichuan, China
- Clinical Laboratory Medicine Research Center of West China Hospital, No.37, Guoxue Xiang, Wuhou District, Chengdu, Sichuan, 610041, China
| | - Han Luo
- Division of Thyroid and Parathyroid Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
- Sichuan Clinical Research Center for Laboratory Medicine, Chengdu, Sichuan, China.
- Clinical Laboratory Medicine Research Center of West China Hospital, No.37, Guoxue Xiang, Wuhou District, Chengdu, Sichuan, 610041, China.
| | - Bei Cai
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
- Sichuan Clinical Research Center for Laboratory Medicine, Chengdu, Sichuan, China.
- Clinical Laboratory Medicine Research Center of West China Hospital, No.37, Guoxue Xiang, Wuhou District, Chengdu, Sichuan, 610041, China.
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Zhang Q, Chen H, Wang M, Lai H, Liu W, Wang L, Zhang J, Li C, Zhou W. Age- and sex-specific 99th percentile upper reference limits for high-sensitivity cardiac troponin T in Chinese older people: Real-world data mining. Clin Biochem 2024; 127-128:110762. [PMID: 38582381 DOI: 10.1016/j.clinbiochem.2024.110762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND This study aims to investigate the impact of age and sex on high-sensitivity cardiac troponin T (hs-cTnT) and establish 99th percentile upper reference limits (URLs) in older individuals utilizing large-scale real-world data. METHODS 40,530 outpatient hs-cTnT results were obtained from the laboratory database from January 1, 2018, to December 31, 2023. Our study included 4,199 elderly outpatients (aged ≥ 60) without cardiovascular disease or other heart-related chronic conditions. Nested analysis of variance was used to explore the necessity of partitioning reference intervals (RIs) by sex and age groups. RIs were established by the refineR algorithm and assessed based on ≤ 10% test results of validation data set outside the new RIs. RESULTS RIs for hs-cTnT in the older population needed to be partitioned by sex and age groups ([standard deviation ratio] SDRage = 0.75; SDRsex = 0.49). URLs in older Chinese adults were 21.8 ng/L for males, 16.5 ng/L for females, and 20.7 ng/L for the overall participant group. URLs for males aged 60-69, 70-79, and ≥ 80 were 13.7, 19.4, and 31.0 ng/L, respectively. Female values were 10.1, 17.2, and 22.0 ng/L. Importantly, manufacturer-reported RIs do not suffice for Chinese individuals aged ≥ 70. Validation data showed that 2.7-5.2% of test results fell outside the new RIs, confirming the validity of the results. CONCLUSION This study establishes age- and sex-specific 99th percentile URLs for hs-cTnT in Chinese older individuals, thereby enhancing the accuracy of clinical assessments.
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Affiliation(s)
- Qian Zhang
- Department of Clinical Laboratory, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, P.R. China
| | - Huiyi Chen
- Department of Biological Products, Chinese Pharmacopoeia Commission, Beijing, P.R. China
| | - Meng Wang
- Department of Clinical Laboratory, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, P.R. China
| | - Huiying Lai
- Department of Clinical Laboratory, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, P.R. China
| | - Wensong Liu
- Department of Clinical Laboratory, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, P.R. China
| | - Lijuan Wang
- Department of Clinical Laboratory, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, P.R. China
| | - Jiaqi Zhang
- Department of Clinical Laboratory, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, P.R. China
| | - Chuanbao Li
- Department of Clinical Laboratory, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, P.R. China.
| | - Weiyan Zhou
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, P.R. China.
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Ma S, Yu J, Qin X, Liu J. Current status and challenges in establishing reference intervals based on real-world data. Crit Rev Clin Lab Sci 2023; 60:427-441. [PMID: 37038925 DOI: 10.1080/10408363.2023.2195496] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 01/29/2023] [Accepted: 03/22/2023] [Indexed: 04/12/2023]
Abstract
Reference intervals (RIs) are the cornerstone for evaluation of test results in clinical practice and are invaluable in judging patient health and making clinical decisions. Establishing RIs based on clinical laboratory data is a branch of real-world data mining research. Compared to the traditional direct method, this indirect approach is highly practical, widely applicable, and low-cost. Improving the accuracy of RIs requires not only the collection of sufficient data and the use of correct statistical methods, but also proper stratification of heterogeneous subpopulations. This includes the establishment of age-specific RIs and taking into account other characteristics of reference individuals. Although there are many studies on establishing RIs by indirect methods, it is still very difficult for laboratories to select appropriate statistical methods due to the lack of formal guidelines. This review describes the application of real-world data and an approach for establishing indirect reference intervals (iRIs). We summarize the processes for establishing iRIs using real-world data and analyze the principle and applicable scope of the indirect method model in detail. Moreover, we compare different methods for constructing growth curves to establish age-specific RIs, in hopes of providing laboratories with a reference for establishing specific iRIs and giving new insight into clinical laboratory RI research. (201 words).
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Affiliation(s)
- Sijia Ma
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Liaoning Clinical Research Center for Laboratory Medicine, Shenyang, P.R. China
| | - Juntong Yu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Liaoning Clinical Research Center for Laboratory Medicine, Shenyang, P.R. China
| | - Xiaosong Qin
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Liaoning Clinical Research Center for Laboratory Medicine, Shenyang, P.R. China
| | - Jianhua Liu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Liaoning Clinical Research Center for Laboratory Medicine, Shenyang, P.R. China
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Coskun A, Sandberg S, Unsal I, Serteser M, Aarsand AK. Personalized reference intervals: from theory to practice. Crit Rev Clin Lab Sci 2022; 59:501-516. [PMID: 35579539 DOI: 10.1080/10408363.2022.2070905] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Using laboratory test results for diagnosis and monitoring requires a reliable reference to which the results can be compared. Currently, most reference data is derived from the population, and patients in this context are considered members of a population group rather than individuals. However, such reference data has limitations when used as the reference for an individual. A patient's test results preferably should be compared with their own, individualized reference intervals (RI), i.e. a personalized RI (prRI).The prRI is based on the homeostatic model and can be calculated using an individual's previous test results obtained in a steady-state situation and estimates of analytical (CVA) and biological variation (BV). BV used to calculate the prRI can be obtained from the population (within-subject biological variation, CVI) or an individual's own data (within-person biological variation, CVP). Statistically, the prediction interval provides a useful tool to calculate the interval (i.e. prRI) for future observation based on previous measurements. With the development of information technology, the data of millions of patients is stored and processed in medical laboratories, allowing the implementation of personalized laboratory medicine. PrRI for each individual should be made available as part of the laboratory information system and should be continually updated as new test results become available.In this review, we summarize the limitations of population-based RI for the diagnosis and monitoring of disease, provide an outline of the prRI concept and different approaches to its determination, including statistical considerations for deriving prRI, and discuss aspects which must be further investigated prior to implementation of prRI in clinical practice.
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Affiliation(s)
- Abdurrahman Coskun
- Acibadem Labmed Clinical Laboratories, Istanbul, Turkey.,Department of Medical Biochemistry, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Sverre Sandberg
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital, Bergen, Norway.,Norwegian Porphyria Centre and Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway.,Department of Global Health and Primary Care, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway
| | - Ibrahim Unsal
- Acibadem Labmed Clinical Laboratories, Istanbul, Turkey
| | - Mustafa Serteser
- Acibadem Labmed Clinical Laboratories, Istanbul, Turkey.,Department of Medical Biochemistry, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Aasne K Aarsand
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital, Bergen, Norway.,Norwegian Porphyria Centre and Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway
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Ma C, Zou Y, Hou L, Yin Y, Zhao F, Hu Y, Wang D, Li L, Cheng X, Qiu L. Validation and comparison of five data mining algorithms using big data from clinical laboratories to establish reference intervals of thyroid hormones for older adults. Clin Biochem 2022; 107:40-49. [DOI: 10.1016/j.clinbiochem.2022.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 05/16/2022] [Accepted: 05/25/2022] [Indexed: 11/03/2022]
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Ma C, Li L, Wang X, Hou L, Xia L, Yin Y, Cheng X, Qiu L. Establishment of Reference Interval and Aging Model of Homocysteine Using Real-World Data. Front Cardiovasc Med 2022; 9:846685. [PMID: 35433869 PMCID: PMC9005842 DOI: 10.3389/fcvm.2022.846685] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 03/02/2022] [Indexed: 12/03/2022] Open
Abstract
Objective The level of Homocysteine (Hcy) in males is generally higher than that of females, but the same reference interval (RI) is often used in clinical practice. This study aims to establish a sex-specific RI of Hcy using five data mining algorithms and compare these results. Furthermore, age-related continuous RI was established in order to show the relationship between Hcy concentration distribution and age. Methods A total of 20,801 individuals were included in the study and Tukey method was used to identify outliers in subgroups by sex and age. Multiple linear regression and standard deviation ratio (SDR) was used to determine whether the RI for Hcy needs to be divided by sex and age. Five algorithms including Hoffmann, Bhattacharya, expectation maximization (EM), kosmic and refineR were utilized to establish the RI of Hcy. Generalized Additive Models for Location Scale and Shape (GAMLSS) algorithm was used to determine the aging model of Hcy and calculate the age-related continuous RI. Results RI of Hcy needed to be partitioned by sex (SDR = 0.735 > 0.375). RIs established by Hoffmann, Bhattacharya, EM (for females) and kosmic are all within the 95% CI of reference limits established by refine R. The Sex-specific aging model of Hcy showed that the upper limits of the RI of Hcy declined with age beginning at age of 18 and began to rise approximately after age of 40 for females and increased with age for males. Conclusion The RI of Hcy needs to be partitioned by sex. The RIs established by the five data mining algorithms showed good consistency. The dynamic sex and age-specific model of Hcy showed the pattern of Hcy concentration with age and provide more personalized tools for clinical decisions.
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Affiliation(s)
- Chaochao Ma
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Lei Li
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xinlu Wang
- Department of Medical Laboratory Technology, Public Health College, Nanchang University, Nanchang, China
| | - Li’an Hou
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Liangyu Xia
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yicong Yin
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xinqi Cheng
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- *Correspondence: Xinqi Cheng,
| | - Ling Qiu
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College and 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 and Chinese Academy of Medical Sciences, Beijing, China
- Ling Qiu,
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Yang D, Su Z, Zhao M. Big data and reference intervals. Clin Chim Acta 2022; 527:23-32. [PMID: 34999059 DOI: 10.1016/j.cca.2022.01.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/29/2021] [Accepted: 01/03/2022] [Indexed: 12/12/2022]
Abstract
Although reference intervals (RIs) play an important role in clinical diagnosis, there remain significant differences with respect to race, gender, age and geographic location. Accordingly, the Clinical Laboratory Standards Institute (CLSI) EP28-A3c has recommended that clinical laboratories establish RIs appropriate to their subject population. Unfortunately, the traditional and direct approach to establish RIs relies on the recruitment of a sufficient number of healthy individuals of various age groups, collection and testing of large numbers of specimens and accurate data interpretation. The advent of the big data era has, however, created a unique opportunity to "mine" laboratory information. Unfortunately, this indirect method lacks standardization, consensus support and CLSI guidance. In this review we provide a historical perspective, comprehensively assess data processing and statistical methods, and post-verification analysis to validate this big data approach in establishing laboratory specific RIs.
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Affiliation(s)
- Dan Yang
- National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, PR China; Department of Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, PR China; Units of Medical Laboratory, Chinese Academy of Medical Sciences, PR China
| | - Zihan Su
- National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, PR China; Department of Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, PR China; Units of Medical Laboratory, Chinese Academy of Medical Sciences, PR China
| | - Min Zhao
- National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, PR China; Department of Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, PR China; Units of Medical Laboratory, Chinese Academy of Medical Sciences, PR China.
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Wang S, Zhao M, Su Z, Mu R. Annual biological variation and personalized reference intervals of clinical chemistry and hematology analytes. Clin Chem Lab Med 2021; 60:606-617. [PMID: 34773728 DOI: 10.1515/cclm-2021-0479] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 10/28/2021] [Indexed: 11/15/2022]
Abstract
OBJECTIVES A large number of people undergo annual health checkup but accurate laboratory criterion for evaluating their health status is limited. The present study determined annual biological variation (BV) and derived parameters of common laboratory analytes in order to accurately evaluate the test results of the annual healthcare population. METHODS A total of 43 healthy individuals who had regular healthcare once a year for six consecutive years, were enrolled using physical, electrocardiogram, ultrasonography and laboratory. The annual BV data and derived parameters, such as reference change value (RCV) and index of individuality (II) were calculated and compared with weekly data. We used annual BV and homeostatic set point to calculate personalized reference intervals (RIper) which were compared with population-based reference intervals (RIpop). RESULTS We have established the annual within-subject BV (CVI), RCV, II, RIper of 24 commonly used clinical chemistry and hematology analytes for healthy individuals. Among the 18 comparable measurands, CVI estimates of annual data for 11 measurands were significantly higher than the weekly data. Approximately 50% measurands of II were <0.6, the utility of their RIpop were limited. The distribution range of RIper for most measurands only copied small part of RIpop with reference range index for 8 measurands <0.5. CONCLUSIONS Compared with weekly BV, for annual healthcare individuals, annual BV and related parameters can provide more accurate evaluation of laboratory results. RIper based on long-term BV data is very valuable for "personalized" diagnosis on annual health assessments.
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Affiliation(s)
- Shuo Wang
- Department of Laboratory Medicine, The First Hospital of China Medical University, National Clinical Research Center for Laboratory Medicine, Shenyang, Liaoning, P.R. China
| | - Min Zhao
- Department of Laboratory Medicine, The First Hospital of China Medical University, National Clinical Research Center for Laboratory Medicine, Shenyang, Liaoning, P.R. China
| | - Zihan Su
- Department of Laboratory Medicine, The First Hospital of China Medical University, National Clinical Research Center for Laboratory Medicine, Shenyang, Liaoning, P.R. China
| | - Runqing Mu
- Department of Laboratory Medicine, The First Hospital of China Medical University, National Clinical Research Center for Laboratory Medicine, Shenyang, Liaoning, P.R. China
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Ma C, Wang X, Xia L, Cheng X, Qiu L. Effect of sample size and the traditional parametric, nonparametric, and robust methods on the establishment of reference intervals: Evidence from real world data. Clin Biochem 2021; 92:67-70. [PMID: 33753113 DOI: 10.1016/j.clinbiochem.2021.03.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 11/18/2022]
Abstract
Sample size and statistical methods are critical for establishing reference intervals (RIs) but they tend to be overlooked. In this study, we used R (3.6.3) to stratify the reference individuals by sex, and then stratified them using the random sampling method. Fourteen sub-data sets with a sample size of 40, 80, 120, 160, 200, 500, 800, 1000, 1500, 2000, 2500, 3000, 3500, and 4000 were extracted, respectively. The sex ratios of all sub-data sets were 1:1. Transformed parametric (using log transformation), nonparametric, and robust approaches as described in the Clinical and Laboratory Standards Institute guidelines were adopted to establish the RIs and the 90% confidence interval of the thyroid-stimulating hormone (TSH) using data from the sub-data sets. The Bland-Altman plot was used to evaluate the consistency of the upper and lower limits of the RIs established using the three methods. The upper and lower limits of TSH RI tended to be stable starting from the data set with a sample size of 1500. The RIs established using the three methods were more consistent when using a sample size greater than or equal to 2000.
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Affiliation(s)
- Chaochao Ma
- Department of Clinical Laboratory, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, PR China
| | - Xinlu Wang
- Public Health College of Nanchang University, Xuefu Street, Nanchang, Jiangxi 330031, PR China
| | - Liangyu Xia
- Department of Clinical Laboratory, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, PR China
| | - Xinqi Cheng
- Department of Clinical Laboratory, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, PR China
| | - Ling Qiu
- Department of Clinical Laboratory, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing 100730, PR China.
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