将类风湿关节炎的检测前概率与 RF 和 ACPA 的似然比结合起来,提高类风湿关节炎自身抗体检测的临床实用性。

IF 3.2 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY Clinica Chimica Acta Pub Date : 2024-08-18 DOI:10.1016/j.cca.2024.119928
Daphné Vandebeek , Elke Lodewijckx , Lieve Van Hoovels , Patrick Verschueren , Xavier Bossuyt
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引用次数: 0

摘要

背景和目的:类风湿性关节炎(RA)表现为各种症状和全身症状。诊断涉及类风湿因子(RF)和抗瓜氨酸蛋白抗体(ACPA)等血清学标记物。过去的研究表明,似然比(LRs)在结果判读中具有附加价值。似然比可与检测前的概率相结合,以估计RA的检测后概率。目前缺乏有关检测前概率的信息。本研究旨在估算 RA 的检测前概率:这项回顾性研究包括在风湿病门诊就诊的 133 名连续 RA 患者和 651 名连续疾病对照者。研究记录了疾病特征、与 RA 相关的风险因素和实验室参数,以计算检测前概率和 LRs:结果:关节受累、糜烂、晨僵以及 CRP 和 ESR 检测呈阳性与 RA 有显著相关性。结果:关节受累、晨僵、CRP 阳性、ESR 阳性与 RA 有明显相关性,根据这些因素估算出 RA 的概率。此外,还确定了 RF 和 ACPA 及其组合的 RA LRs。LRs随抗体水平的升高而增加,双高阳性者的LRs最高。根据检测前概率和 LR 估算出检测后概率:结论:通过利用 RA 的检测前概率以及 RF 和 ACPA 的 LR,可以估算出检测后概率。这种方法提高了诊断的准确性,使实验室专业人员和临床医生在诊断过程中对血清学检测的价值有了更深入的了解。
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Integrating pretest probability for rheumatoid arthritis with likelihood ratios of RF and ACPA to improve clinical utility of rheumatoid arthritis autoantibody testing

Background and aims

Rheumatoid arthritis (RA) manifests through various symptoms and systemic manifestations. Diagnosis involves serological markers like rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA). Past studies have shown the added value of likelihood ratios (LRs) in result interpretation. LRs can be combined with pretest probability to estimate posttest probability for RA. There is a lack of information on pretest probability. This study aimed to estimate pretest probabilities for RA.

Materials and methods

This retrospective study included 133 consecutive RA patients and 651 consecutive disease controls presenting at a rheumatology outpatient clinic. Disease characteristics, risk factors associated with RA and laboratory parameters were documented for calculating pretest probabilities and LRs.

Results

Joint involvement, erosions, morning stiffness, and positive CRP, ESR tests significantly correlated with RA. Based on these factors, probabilities for RA were estimated. Besides, LRs for RA were established for RF and ACPA and combinations thereof. LRs increased with antibody levels and were highest for double high positivity. Posttest probabilities were estimated based on pretest probability and LR.

Conclusion

By utilizing pretest probabilities for RA and LRs for RF and ACPA, posttest probabilities were estimated. Such approach enhances diagnostic accuracy, offering laboratory professionals and clinicians insights in the value of serological testing during the diagnostic process.

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来源期刊
Clinica Chimica Acta
Clinica Chimica Acta 医学-医学实验技术
CiteScore
10.10
自引率
2.00%
发文量
1268
审稿时长
23 days
期刊介绍: The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.
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