清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

Metabolomics as a tool to predict the risk of decompensation or liver-related death in patients with compensated cirrhosis

医学 失代偿 内科学 胃肠病学 肝硬化 门脉高压 安慰剂 代谢物 门静脉压 临床终点 比例危险模型 单变量分析 随机对照试验 多元分析 病理 替代医学
作者
Oana Nicoară-Farcău,Juan José Lozano,Cristina Alonso,Julia Sidorova,Càndid Villanueva,Agustı́n Albillos,Joan Genescà,Elba Llop,J Calleja,Carles Aracil,Rafael Bañares,Rosa M. Morillas,María Poca,Beatriz Peñas,Salvador Augustín,Marcel Tanțău,Marcos Thompson,Valeria Pérez‐Campuzano,Anna Baiges,Fanny Turón
出处
期刊:Hepatology [Lippincott Williams & Wilkins]
卷期号:77 (6): 2052-2062 被引量:11
标识
DOI:10.1097/hep.0000000000000316
摘要

Background and Aims: Patients with compensated cirrhosis with clinically significant portal hypertension (CSPH: HVPG > 10 mm Hg) have a high risk of decompensation. HVPG is, however, an invasive procedure not available in all centers. The present study aims to assess whether metabolomics can improve the capacity of clinical models in predicting clinical outcomes in these compensated patients. Approach and Results: This is a nested study from the PREDESCI cohort (an RCT of nonselective beta-blockers vs. placebo in 201 patients with compensated cirrhosis and CSPH), including 167 patients for whom a blood sample was collected. A targeted metabolomic serum analysis, using ultra-high-performance liquid chromatography-mass spectrometry, was performed. Metabolites underwent univariate time-to-event cox regression analysis. Top-ranked metabolites were selected using Log-Rank p -value to generate a stepwise cox model. Comparison between models was done using DeLong test. Eighty-two patients with CSPH were randomized to nonselective beta-blockers and 85 to placebo. Thirty-three patients developed the main endpoint (decompensation/liver-related death). The model, including HVPG, Child-Pugh, and treatment received ( HVPG/Clinical model ), had a C-index of 0.748 (CI95% 0.664–0.827). The addition of 2 metabolites, ceramide (d18:1/22:0) and methionine (HVPG/Clinical/Metabolite model), significantly improved the model’s performance [C-index of 0.808 (CI95% 0.735–0.882); p =0.032]. The combination of these 2 metabolites together with Child-Pugh and the type of treatment received (Clinical/Metabolite model) had a C-index of 0.785 (CI95% 0.710–0.860), not significantly different from the HVPG-based models including or not metabolites. Conclusions: In patients with compensated cirrhosis and CSPH, metabolomics improves the capacity of clinical models and achieves similar predictive capacity than models including HVPG.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
Criminology34应助jcksonzhj采纳,获得10
8秒前
蝎子莱莱xth完成签到,获得积分10
10秒前
氢锂钠钾铷铯钫完成签到,获得积分10
16秒前
Eatanicecube完成签到,获得积分10
21秒前
Square完成签到,获得积分10
23秒前
学渣前进应助科研通管家采纳,获得10
27秒前
Yewen完成签到,获得积分10
49秒前
turnado完成签到 ,获得积分10
54秒前
潇洒的惋清应助彦成采纳,获得10
59秒前
丘比特应助czj采纳,获得10
59秒前
彦成完成签到,获得积分10
1分钟前
1分钟前
小孟要努力完成签到,获得积分20
1分钟前
Magic完成签到 ,获得积分10
2分钟前
自然亦凝完成签到,获得积分10
2分钟前
naczx完成签到,获得积分0
2分钟前
Copyright应助科研通管家采纳,获得10
2分钟前
研友_VZG7GZ应助科研通管家采纳,获得10
2分钟前
saqi应助hahasun采纳,获得10
2分钟前
神经蛙完成签到 ,获得积分10
2分钟前
cmc完成签到,获得积分10
3分钟前
蛋卷完成签到 ,获得积分0
3分钟前
3分钟前
玛卡巴卡爱吃饭完成签到 ,获得积分10
3分钟前
DrSong完成签到 ,获得积分10
4分钟前
4分钟前
蓝意完成签到,获得积分0
4分钟前
czj发布了新的文献求助10
4分钟前
4分钟前
4分钟前
灿烂而孤独的八戒完成签到 ,获得积分0
4分钟前
不如看海完成签到 ,获得积分10
5分钟前
5分钟前
scholar1234完成签到,获得积分10
5分钟前
羞涩的烨华完成签到,获得积分10
5分钟前
李胖完成签到 ,获得积分10
5分钟前
chihiro完成签到 ,获得积分10
5分钟前
bkagyin应助儒雅的夏翠采纳,获得10
5分钟前
害羞的雁易完成签到 ,获得积分10
6分钟前
高分求助中
Principles of Economics, 11th Edition 10000
Prescott's Microbiology: 2026 Release ISE 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Environmental Leverage in Times of Climate Crisis: Product Standards, Carbon Border Measures and Preferential Trade Agreements 1000
Erwählung und Berufung bei Paulus: Bedeutung, Entwicklung und Funktion einer Vorstellung in ihrem frühjüdischen und griechisch-römischen Kontext 850
Matrix Methods in Data Mining and Pattern Recognition 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7203044
求助须知:如何正确求助?哪些是违规求助? 8837177
关于积分的说明 18651240
捐赠科研通 6848004
什么是DOI,文献DOI怎么找? 3179622
关于科研通互助平台的介绍 2337025
邀请新用户注册赠送积分活动 2154084