The European Biological Variation Study (EuBIVAS): Biological Variation Data for Coagulation Markers Estimated by a Bayesian Model

变化(天文学) 贝叶斯概率 统计 数学 天体物理学 物理
作者
Aasne K. Aarsand,Ann Helen Kristoffersen,Sverre Sandberg,Bård Støve,Abdurrahman Coşkun,Pilar Fernández–Calle,Jorge Díaz–Garzón,Elena Guerra,Ferruccio Ceriotti,Niels Jonker,Thomas Røraas,Anna Carobene
出处
期刊:Clinical Chemistry [American Association for Clinical Chemistry]
卷期号:67 (9): 1259-1270 被引量:32
标识
DOI:10.1093/clinchem/hvab100
摘要

BACKGROUND: For biological variation (BV) data to be safely used, data must be reliable and relevant to the population in which they are applied. We used samples from the European Biological Variation Study (EuBIVAS) to determine BV of coagulation markers by a Bayesian model robust to extreme observations and used the derived within-participant BV estimates [CVP(i)] to assess the applicability of the BV estimates in clinical practice. METHOD: Plasma samples were drawn from 92 healthy individuals for 10 consecutive weeks at 6 European laboratories and analyzed in duplicate for activated partial thromboplastin time (APTT), prothrombin time (PT), fibrinogen, D-dimer, antithrombin (AT), protein C, protein S free, and factor VIII (FVIII). A Bayesian model with Student t likelihoods for samples and replicates was applied to derive CVP(i) and predicted BV estimates with 95% credibility intervals. RESULTS: For all markers except D-dimer, CVP(i) were homogeneously distributed in the overall study population or in subgroups. Mean within-subject estimates (CVI) were <5% for APTT, PT, AT, and protein S free, <10% for protein C and FVIII, and <12% for fibrinogen. For APTT, protein C, and protein S free, estimates were significantly lower in men than in women ≤50 years. CONCLUSION: For most coagulation markers, a common CVI estimate for men and women is applicable, whereas for APTT, protein C, and protein S free, sex-specific reference change values should be applied. The use of a Bayesian model to deliver individual CVP(i) allows for improved interpretation and application of the data.
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