Validation of an oncology‐specific opioid risk calculator in cancer survivors

医学 队列 类阿片 癌症 队列研究 内科学 接收机工作特性 肿瘤科 受体
作者
Paul Riviere,Lucas K. Vitzthum,Vinit Nalawade,Rishi Deka,Timothy Furnish,Loren K. Mell,Brent S. Rose,Mark S. Wallace,James D. Murphy
出处
期刊:Cancer [Wiley]
卷期号:127 (9): 1529-1535 被引量:1
标识
DOI:10.1002/cncr.33410
摘要

Background Clinical guidelines recommend that providers risk‐stratify patients with cancer before prescribing opioids. Prior research has demonstrated that a simple cancer opioid risk score might help identify to patients with cancer at the time of diagnosis with a high likelihood of long‐term posttreatment opioid use. This current project validates this cancer opioid risk score in a generalizable, population‐based cohort of elderly cancer survivors. Methods This study identified 44,932 Medicare beneficiaries with cancer who had received local therapy. Longitudinal opioid use was ascertained from Medicare Part D data. A risk score was calculated for each patient, and patients were categorized into low‐, moderate‐, and high‐risk groups on the basis of the predicted probability of persistent opioid use. Model discrimination was assessed with receiver operating characteristic curves. Results In the study cohort, 5.2% of the patients were chronic opioid users 1 to 2 years after the initiation of cancer treatment. The majority of the patients (64%) were at low risk and had a 1.2% probability of long‐term opioid use. Moderate‐risk patients (33% of the cohort) had a 5.6% probability of long‐term opioid use. High‐risk patients (3.5% of the cohort) had a 75% probability of long‐term opioid use. The opioid risk score had an area under the receiver operating characteristic curve of 0.869. Conclusions This study found that a cancer opioid risk score could accurately identify individuals with a high likelihood of long‐term opioid use in a large, generalizable cohort of cancer survivors. Future research should focus on the implementation of these scores into clinical practice and how this could affect prescriber behavior and patient outcomes. Lay Summary A novel 5‐question clinical decision tool allows physicians treating patients with cancer to accurately predict which patients will persistently be using opioid medications after completing therapy.
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
刚刚
小yang发布了新的文献求助10
刚刚
动听元正完成签到,获得积分10
1秒前
晴天完成签到,获得积分10
1秒前
宋嘉新发布了新的文献求助10
1秒前
科研通AI6.2应助科研狗采纳,获得10
2秒前
2秒前
赵牛牛完成签到,获得积分20
3秒前
彭于晏应助冬云雀采纳,获得10
4秒前
5秒前
5秒前
脑洞疼应助1213采纳,获得10
5秒前
6秒前
献世完成签到,获得积分10
6秒前
6秒前
ZhiZhengWang完成签到,获得积分10
6秒前
8秒前
赵牛牛发布了新的文献求助10
8秒前
tjyangbo完成签到,获得积分10
8秒前
8秒前
9秒前
彭于晏应助nnnd77采纳,获得10
9秒前
Hello应助Jessy畅畅采纳,获得10
10秒前
cy完成签到,获得积分10
10秒前
10秒前
zzzllove完成签到 ,获得积分10
11秒前
万万完成签到,获得积分10
11秒前
11秒前
在水一方应助勤奋的鸿涛采纳,获得10
11秒前
12秒前
小yang完成签到,获得积分10
12秒前
lt1014发布了新的文献求助10
13秒前
richestchen完成签到,获得积分10
13秒前
13秒前
果冻完成签到,获得积分10
13秒前
15秒前
天天快乐应助整齐的泽洋采纳,获得10
15秒前
jane123发布了新的文献求助30
16秒前
小马完成签到,获得积分10
16秒前
16秒前
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
2026年中国辛酸癸酸聚乙二醇甘油酯行业市场规模及竞争格局分析报告 1000
48V Low-voltage Power Distribution Network (PDN) Architecture Industry Report, 2024 800
Fundamentals of Pharmaceutical and Biologics Regulations: A Global Perspective, Second Edition 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
适配Micro-LED色转换的高兼容性量子点负性光刻胶制备与工艺研究 500
Vander's Renal Physiology第10版 500
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 7314987
求助须知:如何正确求助?哪些是违规求助? 8931207
关于积分的说明 18930819
捐赠科研通 6975173
什么是DOI,文献DOI怎么找? 3213771
关于科研通互助平台的介绍 2381799
邀请新用户注册赠送积分活动 2192189