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!

Predicting the Risk of Weight Loss After Esophageal Cancer Surgery

医学 食管癌 减肥 接收机工作特性 食管切除术 队列 外科肿瘤学 癌症 外科 体质指数 队列研究 人口 内科学 肥胖 环境卫生
作者
Anna Schandl,Joonas H. Kauppila,Poorna Anandavadivelan,Asif Johar,Pernilla Lagergren
出处
期刊:Annals of Surgical Oncology [Springer Nature]
卷期号:26 (8): 2385-2391 被引量:27
标识
DOI:10.1245/s10434-019-07352-5
摘要

Malnutrition after esophageal cancer surgery is associated with reduced health-related qualify of life. Therefore, a prediction model identifying patients at risk for severe weight loss after surgery was developed.Data from a Swedish population-based cohort study, including 616 patients undergoing esophageal cancer surgery in 2001-2005, was used. Candidate predictors included risk factors available before and immediately after surgery. Severe weight loss was defined as ≥ 15% loss of body weight between the time of surgery and 6 months postoperatively. The prediction model was developed using multivariable models. The accuracy of the model was measured by the area under the receiver operating characteristics curve (AUC) with bootstrap validation. The model was externally validated in a hospital-based cohort of 91 surgically treated esophageal cancer patients in the United Kingdom in 2011-2016. Each predictor in the final model was assigned a corresponding risk score. The sum of risk scores was equivalent to an estimated probability for severe weight loss.Among the 351 patients with 6 months follow-up data, 125 (36%) suffered from severe postoperative weight loss. The final prediction model included body mass index at diagnosis, preoperative weight loss, and neoadjuvant therapy. The AUC for the model was 0.78 (95% CI 0.74-0.83). In the validation cohort, the AUC was 0.76. A clinical risk assessment guide was derived from the prediction model.This prediction model can preoperatively identify individuals with high risk of severe weight loss after esophageal cancer surgery. Intensive nutritional interventions for these patients are recommended.

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
Criminology34应助纯真的傲玉采纳,获得10
4秒前
21秒前
26秒前
陳.发布了新的文献求助10
33秒前
36秒前
bji完成签到,获得积分10
44秒前
兰球的仙人掌完成签到 ,获得积分10
54秒前
1分钟前
科研通AI2S应助科研通管家采纳,获得10
1分钟前
科研通AI2S应助科研通管家采纳,获得10
1分钟前
BowieHuang应助科研通管家采纳,获得10
1分钟前
af完成签到,获得积分10
1分钟前
1分钟前
勤劳的渊思完成签到 ,获得积分10
1分钟前
两个榴莲完成签到,获得积分0
1分钟前
大胆易巧完成签到 ,获得积分10
2分钟前
2分钟前
3分钟前
hu发布了新的文献求助10
3分钟前
3分钟前
香蕉觅云应助杨泽宇采纳,获得10
3分钟前
简单的莫言完成签到,获得积分10
4分钟前
文承杰完成签到 ,获得积分10
4分钟前
沿途有你完成签到 ,获得积分10
4分钟前
jarrykim完成签到,获得积分10
4分钟前
5分钟前
ajing发布了新的文献求助10
5分钟前
5分钟前
5分钟前
温暖的芷烟完成签到,获得积分10
5分钟前
量子星尘发布了新的文献求助10
5分钟前
5分钟前
笑点低的斑马完成签到,获得积分10
5分钟前
tt完成签到,获得积分10
6分钟前
量子星尘发布了新的文献求助10
6分钟前
6分钟前
块块发布了新的文献求助10
6分钟前
鸿俦鹤侣完成签到,获得积分10
6分钟前
科研通AI2S应助科研通管家采纳,获得10
7分钟前
李健的小迷弟应助威菡采纳,获得10
7分钟前
高分求助中
(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
Binary Alloy Phase Diagrams, 2nd Edition 8000
Building Quantum Computers 800
Translanguaging in Action in English-Medium Classrooms: A Resource Book for Teachers 700
Natural Product Extraction: Principles and Applications 500
Exosomes Pipeline Insight, 2025 500
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5664524
求助须知:如何正确求助?哪些是违规求助? 4864111
关于积分的说明 15107906
捐赠科研通 4823161
什么是DOI,文献DOI怎么找? 2582004
邀请新用户注册赠送积分活动 1536099
关于科研通互助平台的介绍 1494513