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基于社区中糖尿病患者代谢相关脂肪性肝病导致的纤维化存在情况,BIMAST 评分的推导和验证。

Derivation and validation of the BIMAST score for predicting the presence of fibrosis due to Metabolic dysfunction-associated steatotic liver disease among diabetic patients in the community.

机构信息

Liver Unit/Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom.

Department of Health Policy, London School of Economics and Political Science, London, United Kingdom.

出版信息

PLoS One. 2024 Sep 27;19(9):e0307500. doi: 10.1371/journal.pone.0307500. eCollection 2024.

DOI:10.1371/journal.pone.0307500
PMID:39331620
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11432895/
Abstract

BACKGROUND & AIMS: Current screening pathways, developed from tertiary care cohorts, underestimate the presence of Metabolic-dysfunction associated steatotic liver disease (MASLD) in patients with type 2 diabetes mellitus (T2DM) in the community. We developed, validated, and assessed cost-effectiveness of a new score for screening the presence of fibrosis due to MASLD in primary care.

METHODS

Consecutive T2DM patients underwent screening for liver diseases with transient elastography (TE). Based on predictors of significant/advanced fibrosis, we generated the BIMAST score (based on aspartate aminotransferase (AST) and body mass index (BMI)) and validated it internally and externally (Royal Free Hospital, London and Palermo Hospital). For cost-effectiveness analysis, 6 screening strategies were compared against standard of care: BIMAST score, ultrasound plus abnormal liver function tests, FIB-4, NAFLD fibrosis score, ELF and transient elastography (TE). A Markov model was built based on fibrosis status. Cost per quality-adjusted life year (QALY) gained and the incremental cost-effectiveness ratio (ICER) were estimated over a lifetime.

RESULTS

Among 300 patients enrolled, 64% (186) had MASLD and 10% (28) other causes of liver disease. In the whole population, patients with significant fibrosis, advanced fibrosis, and cirrhosis due to MASLD were 17% (50/287), 11% (31/287), and 3% (8/287), respectively. In primary care, BIMAST performed better than other non-invasive markers at predicting significant and advanced fibrosis. Moreover, BIMAST reduced false negatives from 54% (ELF) and 38% (FIB-4) to 10%. In both validation cohorts, BIMAST performance was as good as FIB-4. In the cost-utility analysis, ICER was £2,337.92/QALY for BIMAST.

CONCLUSION

The BIMAST predicts the presence of significant fibrosis in the community, reduces false negatives and is cost-effective. The BIMAST score should be included in the holistic assessment of diabetic patients.

摘要

背景与目的

当前的筛查途径是从三级护理队列中开发的,低估了社区 2 型糖尿病(T2DM)患者代谢功能障碍相关脂肪性肝病(MASLD)的存在。我们开发、验证并评估了一种新评分在初级保健中筛查 MASLD 相关纤维化的存在的成本效益。

方法

连续的 T2DM 患者接受了瞬态弹性成像(TE)筛查。基于显著/晚期纤维化的预测因素,我们生成了 BIMAST 评分(基于天门冬氨酸氨基转移酶(AST)和体重指数(BMI)),并在内部和外部(伦敦皇家自由医院和巴勒莫医院)进行了验证。为了进行成本效益分析,将 6 种筛查策略与标准护理进行比较:BIMAST 评分、超声加异常肝功能检查、FIB-4、NAFLD 纤维化评分、ELF 和瞬态弹性成像(TE)。根据纤维化状态建立了一个马尔可夫模型。在一生中,估计每获得一个质量调整生命年(QALY)的成本和增量成本效益比(ICER)。

结果

在纳入的 300 名患者中,64%(186 名)患有 MASLD,10%(28 名)患有其他肝病。在整个人群中,由于 MASLD 导致的显著纤维化、晚期纤维化和肝硬化患者分别为 17%(50/287)、11%(31/287)和 3%(8/287)。在初级保健中,BIMAST 在预测显著和晚期纤维化方面优于其他非侵入性标志物。此外,BIMAST 将假阴性率从 54%(ELF)和 38%(FIB-4)降低至 10%。在两个验证队列中,BIMAST 的性能与 FIB-4 一样好。在成本效益分析中,BIMAST 的 ICER 为 2337.92 英镑/QALY。

结论

BIMAST 可预测社区中显著纤维化的存在,减少假阴性并具有成本效益。BIMAST 评分应纳入糖尿病患者的整体评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32e2/11432895/4165e82f65f7/pone.0307500.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32e2/11432895/8e73f4498167/pone.0307500.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32e2/11432895/72633c444e98/pone.0307500.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32e2/11432895/5159ab5a0ed7/pone.0307500.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32e2/11432895/de159717fff1/pone.0307500.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32e2/11432895/4165e82f65f7/pone.0307500.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32e2/11432895/8e73f4498167/pone.0307500.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32e2/11432895/72633c444e98/pone.0307500.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32e2/11432895/5159ab5a0ed7/pone.0307500.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32e2/11432895/de159717fff1/pone.0307500.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32e2/11432895/4165e82f65f7/pone.0307500.g005.jpg

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