• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Development of a Medicare Claims-Based Model to Predict Persistent High-Dose Opioid Use After Total Knee Replacement.开发一种基于医疗保险索赔的模型,以预测全膝关节置换术后持续高剂量阿片类药物使用。
Arthritis Care Res (Hoboken). 2022 Aug;74(8):1342-1348. doi: 10.1002/acr.24559. Epub 2022 Apr 22.
2
Association of Preoperative Opioid Use With Mortality and Short-term Safety Outcomes After Total Knee Replacement.术前阿片类药物使用与全膝关节置换术后死亡率和短期安全性结局的关联。
JAMA Netw Open. 2019 Jul 3;2(7):e198061. doi: 10.1001/jamanetworkopen.2019.8061.
3
A Safe Number of Perioperative Opioids to Reduce the Risk of New Persistent Usage Among Opioid-Naïve Patients Following Total Joint Arthroplasty.全关节置换术后,降低阿片类药物初治患者新的持续使用风险的围手术期阿片类药物安全剂量
J Arthroplasty. 2023 Jan;38(1):18-23.e1. doi: 10.1016/j.arth.2022.08.018. Epub 2022 Aug 18.
4
Dosing profiles of concurrent opioid and benzodiazepine use associated with overdose risk among US Medicare beneficiaries: group-based multi-trajectory models.美国医疗保险受益人中与过量用药风险相关的阿片类药物和苯二氮䓬类药物同时使用的给药情况:基于群体的多轨迹模型
Addiction. 2022 Jul;117(7):1982-1997. doi: 10.1111/add.15857. Epub 2022 Apr 19.
5
Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?预测模型工具能否识别 ACL 重建术后阿片类药物使用时间延长的高风险患者?
Clin Orthop Relat Res. 2020 Jul;478(7):0-1618. doi: 10.1097/CORR.0000000000001251.
6
Increased opioid use following rotator cuff repair associated with prior opioid use and surgeon prescription patterns.肩袖修复术后阿片类药物使用增加与术前阿片类药物使用和外科医生处方模式有关。
J Shoulder Elbow Surg. 2020 Jul;29(7S):S115-S125. doi: 10.1016/j.jse.2020.04.037. Epub 2020 Jun 9.
7
Trajectories of prescription opioid dose and risk of opioid-related adverse events among older Medicare beneficiaries in the United States: A nested case-control study.美国老年医疗保险受益人群中处方类阿片类药物剂量与阿片类药物相关不良事件风险的轨迹:一项嵌套病例对照研究。
PLoS Med. 2022 Mar 15;19(3):e1003947. doi: 10.1371/journal.pmed.1003947. eCollection 2022 Mar.
8
Total Inpatient Morphine Milligram Equivalents Can Predict Long-term Opioid Use After Transforaminal Lumbar Interbody Fusion.全住院吗啡毫克当量可预测经椎间孔腰椎体间融合术后长期阿片类药物使用。
Spine (Phila Pa 1976). 2019 Oct 15;44(20):1465-1470. doi: 10.1097/BRS.0000000000003106.
9
Factors associated with persistent opioid use 6-12 months after primary total knee arthroplasty.初次全膝关节置换术后 6-12 个月内持续使用阿片类药物的相关因素。
Anaesthesia. 2022 Aug;77(8):882-891. doi: 10.1111/anae.15783. Epub 2022 Jun 27.
10
The Effect of Preoperative Opioid Dosage on Postoperative Outcomes in Patients Undergoing Knee Surgery.术前阿片类药物剂量对膝关节手术患者术后结局的影响。
Pain Physician. 2020 Jan;23(1):73-85.

引用本文的文献

1
A Review of Leveraging Artificial Intelligence to Predict Persistent Postoperative Opioid Use and Opioid Use Disorder and its Ethical Considerations.利用人工智能预测术后持续使用阿片类药物和阿片类药物使用障碍及其伦理考量的综述
Curr Pain Headache Rep. 2025 Jan 23;29(1):30. doi: 10.1007/s11916-024-01319-2.

本文引用的文献

1
Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes.利用电子病历中的行政索赔数据进行机器学习方法与传统模型预测心力衰竭结局的比较。
JAMA Netw Open. 2020 Jan 3;3(1):e1918962. doi: 10.1001/jamanetworkopen.2019.18962.
2
Predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients.预测阿片类药物初治腰椎手术患者的延长阿片类药物处方。
Spine J. 2020 Jun;20(6):888-895. doi: 10.1016/j.spinee.2019.12.019. Epub 2019 Dec 31.
3
Association of Early Postoperative Pain Trajectories With Longer-term Pain Outcome After Primary Total Knee Arthroplasty.初次全膝关节置换术后早期术后疼痛轨迹与长期疼痛结局的关联。
JAMA Netw Open. 2019 Nov 1;2(11):e1915105. doi: 10.1001/jamanetworkopen.2019.15105.
4
Trends in opioid use disorder and overdose among opioid-naive individuals receiving an opioid prescription in Massachusetts from 2011 to 2014.2011 年至 2014 年期间,马萨诸塞州接受阿片类药物处方的阿片类药物初治人群中阿片类药物使用障碍和过量的趋势。
Addiction. 2020 Mar;115(3):493-504. doi: 10.1111/add.14867. Epub 2019 Dec 21.
5
Racial/Ethnic and Age Group Differences in Opioid and Synthetic Opioid-Involved Overdose Deaths Among Adults Aged ≥18 Years in Metropolitan Areas - United States, 2015-2017.大都市地区≥18 岁成年人中阿片类药物和合成阿片类药物相关过量死亡的种族/民族和年龄组差异 - 美国,2015-2017 年。
MMWR Morb Mortal Wkly Rep. 2019 Nov 1;68(43):967-973. doi: 10.15585/mmwr.mm6843a3.
6
Association of Preoperative Opioid Use With Mortality and Short-term Safety Outcomes After Total Knee Replacement.术前阿片类药物使用与全膝关节置换术后死亡率和短期安全性结局的关联。
JAMA Netw Open. 2019 Jul 3;2(7):e198061. doi: 10.1001/jamanetworkopen.2019.8061.
7
Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty.机器学习算法在全髋关节置换术后持续阿片类药物处方预测中的开发。
J Arthroplasty. 2019 Oct;34(10):2272-2277.e1. doi: 10.1016/j.arth.2019.06.013. Epub 2019 Jun 13.
8
Pain Management Strategies To Reduce Opioid Use Following Total Knee Arthroplasty.全膝关节置换术后减少阿片类药物使用的疼痛管理策略
Surg Technol Int. 2019 Nov 10;35:301-310.
9
Validation of a Claims-Based Frailty Index Against Physical Performance and Adverse Health Outcomes in the Health and Retirement Study.基于索赔的衰弱指数对健康与退休研究中身体表现和不良健康结果的验证。
J Gerontol A Biol Sci Med Sci. 2019 Jul 12;74(8):1271-1276. doi: 10.1093/gerona/gly197.
10
The relative benefits of claims and electronic health record data for predicting medication adherence trajectory.用于预测药物依从性轨迹的索赔和电子健康记录数据的相对益处。
Am Heart J. 2018 Mar;197:153-162. doi: 10.1016/j.ahj.2017.09.019. Epub 2017 Dec 2.

开发一种基于医疗保险索赔的模型,以预测全膝关节置换术后持续高剂量阿片类药物使用。

Development of a Medicare Claims-Based Model to Predict Persistent High-Dose Opioid Use After Total Knee Replacement.

机构信息

Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.

Northwestern University, Chicago, Illinois.

出版信息

Arthritis Care Res (Hoboken). 2022 Aug;74(8):1342-1348. doi: 10.1002/acr.24559. Epub 2022 Apr 22.

DOI:10.1002/acr.24559
PMID:33450136
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8280246/
Abstract

OBJECTIVE

To develop a claims-based model to predict persistent high-dose opioid use among patients undergoing total knee replacement (TKR).

METHODS

Using Medicare claims (2010-2014), we identified patients ages ≥65 years who underwent TKR with no history of high-dose opioid use (mean >25 morphine milligram equivalents [MMEs]/day) in the year prior to TKR. We used group-based trajectory modeling to identify distinct opioid use patterns. The primary outcome was persistent high-dose opioid use in the year after TKR. We split the data into training (2010-2013) and test (2014) sets and used logistic regression with least absolute shrinkage and selection operator regularization, utilizing a total of 83 preoperative patient characteristics as candidate predictors. A reduced model with 10 prespecified variables, which included demographic characteristics, opioid use, and medication history was also considered.

RESULTS

The final study cohort included 142,089 patients who underwent TKR. The group-based trajectory model identified 4 distinct trajectories of opioid use (group 1: short-term, low-dose; group 2: moderate-duration, low-dose; group 3: moderate-duration, high-dose; and group 4: persistent high-dose). The model predicting persistent high-dose opioid use achieved high discrimination (receiver operating characteristic area under the curve [AUC] 0.85 [95% confidence interval (95% CI) 0.84-0.86]) in the test set. The reduced model with 10 predictors performed equally well (AUC 0.84 [95% CI 0.84-0.85]).

CONCLUSION

In this cohort of older patients, 10.6% became persistent high-dose (mean 22.4 MME/day) opioid users after TKR. Our model with 10 readily available clinical factors may help identify patients at high risk of future adverse outcomes from persistent opioid use after TKR.

摘要

目的

开发一种基于索赔的模型,以预测接受全膝关节置换术(TKR)的患者中持续高剂量阿片类药物使用的情况。

方法

使用医疗保险索赔数据(2010-2014 年),我们确定了年龄≥65 岁的患者,他们在 TKR 前一年没有高剂量阿片类药物使用史(平均>25 吗啡毫克当量[MME]/天)。我们使用基于群组的轨迹建模来识别不同的阿片类药物使用模式。主要结局是 TKR 后一年内持续高剂量阿片类药物使用。我们将数据分为训练集(2010-2013 年)和测试集(2014 年),并使用逻辑回归与最小绝对收缩和选择算子正则化,利用总共 83 个术前患者特征作为候选预测因子。还考虑了一个具有 10 个预设变量的简化模型,其中包括人口统计学特征、阿片类药物使用和药物史。

结果

最终的研究队列包括 142089 名接受 TKR 的患者。基于群组的轨迹模型确定了 4 种不同的阿片类药物使用轨迹(第 1 组:短期、低剂量;第 2 组:中等持续时间、低剂量;第 3 组:中等持续时间、高剂量;第 4 组:持续高剂量)。在测试集中,预测持续高剂量阿片类药物使用的模型具有较高的区分度(受试者工作特征曲线下面积[AUC]为 0.85[95%置信区间(95%CI)为 0.84-0.86])。具有 10 个预测因子的简化模型表现同样出色(AUC 为 0.84[95%CI 为 0.84-0.85])。

结论

在这个老年患者队列中,10.6%的患者在 TKR 后成为持续高剂量(平均 22.4 MME/天)阿片类药物使用者。我们的模型具有 10 个易于获得的临床因素,可能有助于识别 TKR 后持续使用阿片类药物未来不良后果风险较高的患者。