1
|
Coskun A. Prediction interval: A powerful statistical tool for monitoring patients and analytical systems. Biochem Med (Zagreb) 2024; 34:020101. [PMID: 38665871 PMCID: PMC11042565 DOI: 10.11613/bm.2024.020101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 01/23/2024] [Indexed: 04/28/2024] Open
Abstract
Monitoring is indispensable for assessing disease prognosis and evaluating the effectiveness of treatment strategies, both of which rely on serial measurements of patients' data. It also plays a critical role in maintaining the stability of analytical systems, which is achieved through serial measurements of quality control samples. Accurate monitoring can be achieved through data collection, following a strict preanalytical and analytical protocol, and the application of a suitable statistical method. In a stable process, future observations can be predicted based on historical data collected during periods when the process was deemed reliable. This can be evaluated using the statistical prediction interval. Statistically, prediction interval gives an "interval" based on historical data where future measurement results can be located with a specified probability such as 95%. Prediction interval consists of two primary components: (i) the set point and (ii) the total variation around the set point which determines the upper and lower limits of the interval. Both can be calculated using the repeated measurement results obtained from the process during its steady-state. In this paper, (i) the theoretical bases of prediction intervals were outlined, and (ii) its practical application was explained through examples, aiming to facilitate the implementation of prediction intervals in laboratory medicine routine practice, as a robust tool for monitoring patients' data and analytical systems.
Collapse
Affiliation(s)
- Abdurrahman Coskun
- Department of Medical Biochemistry, Acıbadem Mehmet Ali Aydinlar University, School of Medicine, Istanbul, Turkey
| |
Collapse
|
2
|
Dülgeroğlu Y, Ercan M. Biological variation of serum neopterin concentrations in apparently healthy individuals. Clin Chem Lab Med 2024; 62:706-712. [PMID: 37882748 DOI: 10.1515/cclm-2023-1030] [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: 12/23/2022] [Accepted: 10/17/2023] [Indexed: 10/27/2023]
Abstract
OBJECTIVES The aims of this study were to determine the biological variation (BV), reference change value (RCV), index of individuality (II), and quality specifications for serum neopterin concentrations; a measurand provided by clinical laboratories as an indicator of cellular immunity. METHODS The study delivered serum samples collected for 10 consecutive weeks from 12 apparently healthy individuals (3 male, 9 female). Serum neopterin concentrations were measured using high-performance liquid chromatography with fluorometric detection. The data analysis was performed using an online statistical tool and addressed published criteria for estimation of biological variation. RESULTS The mean neopterin concentration was 5.26 nmol/L. The within-subject biological variation (CVI) with 95 % confidence interval (CI) of neopterin serum concentrations was 11.54 % (9.98-13.59), and the between-subject biological variation (CVG) with 95 % CI was 43.27 % (30.52-73.67). The neopterin asymmetrical RCV was -24.9 %/+33.1 %, and the II was 0.27. The desirable quality specifications for neopterin were <5.77 % for precision, <11.20 % for bias, and <20.72 % for total allowable error (TEa). When analytical variation was used instead of CVI to calculate TEa, the desirable TEa was <18.39. CONCLUSIONS This study determined BV data for neopterin, an indicator of cell-mediated immune response. Asymmetric RCV values, of 24.9 % decrease or a 33.1 % increase between consecutive measurements indicate significant change. The II of 0.27 indicates a high degree of individuality, therefore that it is appropriate to consider the use of personal reference data and significance of change rather than the reference interval as points of reference for the evaluation of neopterin serum concentrations.
Collapse
Affiliation(s)
- Yakup Dülgeroğlu
- Department of Medical Biochemistry, Yenisehir State Hospital, Bursa, Turkiye
| | - Müjgan Ercan
- Department of Medical Biochemistry, Afyonkarahisar Health Sciences University, Afyonkarahisar, Turkiye
| |
Collapse
|
3
|
Coskun A, Lippi G. Personalized laboratory medicine in the digital health era: recent developments and future challenges. Clin Chem Lab Med 2024; 62:402-409. [PMID: 37768883 DOI: 10.1515/cclm-2023-0808] [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: 07/28/2023] [Accepted: 09/18/2023] [Indexed: 09/30/2023]
Abstract
Interpretation of laboratory data is a comparative procedure and requires reliable reference data, which are mostly derived from population data but used for individuals in conventional laboratory medicine. Using population data as a "reference" for individuals has generated several problems related to diagnosing, monitoring, and treating single individuals. This issue can be resolved by using data from individuals' repeated samples, as their personal reference, thus needing that laboratory data be personalized. The modern laboratory information system (LIS) can store the results of repeated measurements from millions of individuals. These data can then be analyzed to generate a variety of personalized reference data sets for numerous comparisons. In this manuscript, we redefine the term "personalized laboratory medicine" as the practices based on individual-specific samples and data. These reflect their unique biological characteristics, encompassing omics data, clinical chemistry, endocrinology, hematology, coagulation, and within-person biological variation of all laboratory data. It also includes information about individuals' health behavior, chronotypes, and all statistical algorithms used to make precise decisions. This approach facilitates more accurate diagnosis, monitoring, and treatment of diseases for each individual. Furthermore, we explore recent advancements and future challenges of personalized laboratory medicine in the context of the digital health era.
Collapse
Affiliation(s)
- Abdurrahman Coskun
- Acibadem Mehmet Ali Aydinlar University, School of Medicine, Istanbul, Türkiye
| | - Giuseppe Lippi
- Section of Clinical Biochemistry and School of Medicine, University of Verona, Verona, Italy
| |
Collapse
|
4
|
Coşkun A, Sandberg S, Unsal I, Cavusoglu C, Serteser M, Kilercik M, Aarsand AK. Personalized and Population-Based Reference Intervals for 48 Common Clinical Chemistry and Hematology Measurands: A Comparative Study. Clin Chem 2023; 69:1009-1030. [PMID: 37525518 DOI: 10.1093/clinchem/hvad113] [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: 03/15/2023] [Accepted: 07/10/2023] [Indexed: 08/02/2023]
Abstract
BACKGROUND Personalized reference intervals (prRIs) have the potential to improve individual patient follow-up as compared to population-based reference intervals (popRI). In this study, we estimated popRI and prRIs for 48 clinical chemistry and hematology measurands using samples from the same reference individuals and explored the effect of using group-based and individually based biological variation (BV) estimates to derive prRIs. METHODS 143 individuals (median age 28 years) were included in the study and had fasting blood samples collected once. From this population, 41 randomly selected subjects had samples collected weekly for 5 weeks. PopRIs were estimated according to Clinical Laboratory Standards Institute EP28 and within-subject BV (CVI) were estimated by CV-ANOVA. Data were assessed for trends and outliers prior to calculation of individual prRIs, based on estimates of (a) within-person BV (CVP), (b) CVI derived in this study, and (c) publically available CVI estimates. RESULTS For most measurands, the individual prRI ranges were smaller than the popRI range, but overall about half the study participants had a prRI wider than the popRI for 5 or more out of 48 measurands. The dispersion of prRIs based on CVP was wider than that of prRIs based on CVI. CONCLUSION The prRIs derived in our study varied significantly between different individuals, especially if based on CVP. Our results highlight the limitations of popRIs in interpreting test results of individual patients. If sufficient data from a steady-state situation are available, using prRI based on CVP estimates will provide a RI most specific for an individual patient.
Collapse
Affiliation(s)
- Abdurrahman Coşkun
- Acibadem Labmed Clinical Laboratories, Acibadem Mehmet Ali Aydinlar University, 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, 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, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Coskun Cavusoglu
- Acibadem Labmed Clinical Laboratories, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Mustafa Serteser
- Acibadem Labmed Clinical Laboratories, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
- Department of Medical Biochemistry, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Meltem Kilercik
- Acibadem Labmed Clinical Laboratories, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
- Department of Medical Biochemistry, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Aasne K Aarsand
- Norwegian Porphyria Centre, 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
| |
Collapse
|
5
|
Coskun A, Zarepour A, Zarrabi A. Physiological Rhythms and Biological Variation of Biomolecules: The Road to Personalized Laboratory Medicine. Int J Mol Sci 2023; 24:ijms24076275. [PMID: 37047252 PMCID: PMC10094461 DOI: 10.3390/ijms24076275] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 03/24/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023] Open
Abstract
The concentration of biomolecules in living systems shows numerous systematic and random variations. Systematic variations can be classified based on the frequency of variations as ultradian (<24 h), circadian (approximately 24 h), and infradian (>24 h), which are partly predictable. Random biological variations are known as between-subject biological variations that are the variations among the set points of an analyte from different individuals and within-subject biological variation, which is the variation of the analyte around individuals’ set points. The random biological variation cannot be predicted but can be estimated using appropriate measurement and statistical procedures. Physiological rhythms and random biological variation of the analytes could be considered the essential elements of predictive, preventive, and particularly personalized laboratory medicine. This systematic review aims to summarize research that have been done about the types of physiological rhythms, biological variations, and their effects on laboratory tests. We have searched the PubMed and Web of Science databases for biological variation and physiological rhythm articles in English without time restrictions with the terms “Biological variation, Within-subject biological variation, Between-subject biological variation, Physiological rhythms, Ultradian rhythms, Circadian rhythm, Infradian rhythms”. It was concluded that, for effective management of predicting, preventing, and personalizing medicine, which is based on the safe and valid interpretation of patients’ laboratory test results, both physiological rhythms and biological variation of the measurands should be considered simultaneously.
Collapse
|
6
|
Diaz-Garzon J, Fernandez-Calle P, Aarsand AK, Sandberg S, Coskun A, Equey T, Aikin R, Soto AB. Long-Term Within- and Between-Subject Biological Variation Data of Hematological Parameters in Recreational Endurance Athletes. Clin Chem 2023; 69:500-509. [PMID: 36786725 DOI: 10.1093/clinchem/hvad006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 12/28/2022] [Indexed: 02/15/2023]
Abstract
BACKGROUND Hematological parameters have many applications in athletes, from monitoring health to uncovering blood doping. This study aimed to deliver biological variation (BV) estimates for 9 hematological parameters by a Biological Variation Data Critical Appraisal Checklist (BIVAC) design in a population of recreational endurance athletes and to assess the effect of self-reported exercise and health-related variables on BV. METHODS Samples were drawn from 30 triathletes monthly for 11 months and measured in duplicate for hematological measurands on an Advia 2120 analyzer (Siemens Healthineers). After outlier and homogeneity analysis, within-subject (CVI) and between-subject (CVG) BV estimates were delivered (CV-ANOVA and log-ANOVA, respectively) and a linear mixed model was applied to analyze the effect of exercise and other related variables on the BV estimates. RESULTS CVI estimates ranged from 1.3% (95%CI, 1.2-1.4) for mean corpuscular volume to 23.8% (95%CI, 21.6-26.3) for reticulocytes. Sex differences were observed for platelets and OFF-score. The CVI estimates were higher than those reported for the general population based on meta-analysis of eligible studies in the European Biological Variation Database, but 95%CI overlapped, except for reticulocytes, 23.9% (95%CI, 21.6-26.5) and 9.7% (95%CI, 6.4-11.0), respectively. Factors related to exercise and athletes' state of health did not appear to influence the BV estimates. CONCLUSIONS This is the first BIVAC-compliant study delivering BV estimates that can be applied to athlete populations performing high-level aerobic exercise. CVI estimates of most parameters were similar to the general population and were not influenced by exercise or athletes' state of health.
Collapse
Affiliation(s)
- Jorge Diaz-Garzon
- Laboratory Medicine Department, La Paz University Hospital, Madrid, Spain
| | | | - Aasne K Aarsand
- Norwegian Porphyria Centre, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway.,Norwegian Organization for Quality Improvement of Laboratory Examinations (NOKLUS), Haraldsplass Deaconess Hospital, Bergen, Norway
| | - Sverre Sandberg
- Norwegian Porphyria Centre, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway.,Norwegian Organization for Quality Improvement of Laboratory Examinations (NOKLUS), Haraldsplass Deaconess Hospital, Bergen, Norway.,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Abdurrahman Coskun
- School of Medicine, Acibadem Mehmet Ali Aydınlar University, Istanbul, Turkey
| | - Tristan Equey
- Athlete Biological Passport, World Anti-Doping Agency (WADA), Montréal, Canada
| | - Reid Aikin
- Athlete Biological Passport, World Anti-Doping Agency (WADA), Montréal, Canada
| | - Antonio Buno Soto
- Laboratory Medicine Department, La Paz University Hospital, Madrid, Spain
| |
Collapse
|
7
|
Sandberg S, Carobene A, Bartlett B, Coskun A, Fernandez-Calle P, Jonker N, Díaz-Garzón J, Aarsand AK. Biological variation: recent development and future challenges. Clin Chem Lab Med 2022; 61:741-750. [PMID: 36537071 DOI: 10.1515/cclm-2022-1255] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 12/12/2022] [Indexed: 02/18/2023]
Abstract
Abstract
Biological variation (BV) data have many applications in laboratory medicine. However, these depend on the availability of relevant and robust BV data fit for purpose. BV data can be obtained through different study designs, both by experimental studies and studies utilizing previously analysed routine results derived from laboratory databases. The different BV applications include using BV data for setting analytical performance specifications, to calculate reference change values, to define the index of individuality and to establish personalized reference intervals. In this review, major achievements in the area of BV from last decade will be presented and discussed. These range from new models and approaches to derive BV data, the delivery of high-quality BV data by the highly powered European Biological Variation Study (EuBIVAS), the Biological Variation Data Critical Appraisal Checklist (BIVAC) and other standards for deriving and reporting BV data, the EFLM Biological Variation Database and new applications of BV data including personalized reference intervals and measurement uncertainty.
Collapse
Affiliation(s)
- Sverre Sandberg
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital , Bergen , Norway
- Department of Medical Biochemistry and Pharmacology , Norwegian Porphyria Centre, Haukeland University Hospital , Bergen , Norway
- Department of Global Public Health and Primary Care , University of Bergen , Bergen , Norway
| | - Anna Carobene
- Laboratory Medicine, IRCCS San Raffaele Scientific Institute , Milan , Italy
| | - Bill Bartlett
- School of Science and Engineering, University of Dundee , Dundee , Scotland
| | - Abdurrahman Coskun
- Acibadem Mehmet Ali Aydınlar University, School of Medicine , Istanbul , Türkiye
| | - Pilar Fernandez-Calle
- Hospital Universitario La Paz, Quality Analytical Commission of Spanish Society of Clinical Chemistry (SEQC) , Madrid , Spain
| | - Niels Jonker
- Certe, Wilhelmina Ziekenhuis Assen , Assen , The Netherlands
| | - Jorge Díaz-Garzón
- Hospital Universitario La Paz, Quality Analytical Commission of Spanish Society of Clinical Chemistry (SEQC) , Madrid , Spain
| | - Aasne K. Aarsand
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital , Bergen , Norway
- Department of Medical Biochemistry and Pharmacology , Norwegian Porphyria Centre, Haukeland University Hospital , Bergen , Norway
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Ross Dallas Jones G. Letter: Further issues with using Reference Change Values. Clin Chim Acta 2022; 528:13-14. [DOI: 10.1016/j.cca.2022.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 01/13/2022] [Accepted: 01/14/2022] [Indexed: 11/03/2022]
|