Accuracy study of Angiotensin 1–7 composite index test to predict pulmonary fibrosis and guide treatment

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY Clinica Chimica Acta Pub Date : 2024-08-15 DOI:10.1016/j.cca.2024.119926

Nathalie De Vos , Marie Bruyneel , Alain Roman , Mathieu Antoine , Anne-Violette Bruyneel , Stephane Alard , Stéphanie André , Hafid Dahma , Audrey Chirumberro , Frédéric Cotton

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Abstract

Background

Pulmonary fibrosis can develop after acute respiratory distress syndrome (ARDS). The hypothesis is we are able to measure phenotypes that lie at the origin of ARDS severity and fibrosis development. The aim is an accuracy study of prognostic circulating biomarkers.

Methods

A longitudinal study followed COVID-related ARDS patients with medical imaging, pulmonary function tests and biomarker analysis, generating 444 laboratory data. Comparison to controls used non-parametrical statistics; p < 0·05 was considered significant. Cut-offs were obtained through receiver operating curve. Contingency tables revealed predictive values. Odds ratio was calculated through logistic regression.

Results

Angiotensin 1–7 beneath 138 pg/mL defined Angiotensin imbalance phenotype. Hyper-inflammatory phenotype showed a composite index test above 34, based on high Angiotensin 1–7, C-Reactive Protein, Ferritin and Transforming Growth Factor-β. Analytical study showed conformity to predefined goals. Clinical performance gave a positive predictive value of 95 % (95 % confidence interval, 82 %–99 %), and a negative predictive value of 100 % (95 % confidence interval, 65 %–100 %). Those severe ARDS phenotypes represented 34 (Odds 95 % confidence interval, 3–355) times higher risk for pulmonary fibrosis development (p < 0·001).

Conclusions

Angiotensin 1–7 composite index is an early and objective predictor of ARDS evolving to pulmonary fibrosis. It may guide therapeutic decisions in targeted phenotypes.

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血管紧张素 1-7 综合指数测试预测肺纤维化和指导治疗的准确性研究。

背景：急性呼吸窘迫综合征（ARDS）后可出现肺纤维化。我们的假设是，我们能够测量 ARDS 严重程度和肺纤维化发展的表型。我们的目标是对预后循环生物标志物进行精确研究：一项纵向研究对 COVID 相关 ARDS 患者进行了医学影像、肺功能测试和生物标志物分析，共生成了 444 项实验室数据。与对照组的比较采用非参数统计；P 结果：血管紧张素 1-7 低于 138 pg/mL 定义了血管紧张素失衡表型。高炎症表型显示综合指数测试高于 34，基于高血管紧张素 1-7、C-反应蛋白、铁蛋白和转化生长因子-β。分析研究显示该指标符合预定目标。临床表现的阳性预测值为 95%（95% 置信区间为 82%-99%），阴性预测值为 100%（95% 置信区间为 65%-100%）。这些严重的 ARDS 表型代表肺纤维化发生风险高出 34 倍（Odds 95 % 置信区间，3-355）（P 结论：ARDS 表型是肺纤维化发生风险的主要来源：血管紧张素 1-7 综合指数是 ARDS 演变为肺纤维化的早期客观预测指标。它可为目标表型的治疗决策提供指导。

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来源期刊

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.