Suppr超能文献

在Evolut低风险试验中使用索赔数据评估结果和治疗效果

Use of Claims to Assess Outcomes and Treatment Effects in the Evolut Low Risk Trial.

作者信息

Butala Neel M, Lalani Christina, Tale Archana, Song Yang, Kolte Dhaval, Baron Suzanne, Strom Jordan, Cohen David J, Yeh Robert W

机构信息

Division of Cardiology, Department of Medicine, Rocky Mountain Regional VA Medical Center, Aurora, CO (N.M.B.).

Division of Cardiology, Department of Medicine, University of Colorado School of Medicine, Aurora (N.M.B.).

出版信息

Circ Cardiovasc Interv. 2025 Jan;18(1):e014592. doi: 10.1161/CIRCINTERVENTIONS.124.014592. Epub 2025 Jan 21.

Abstract

BACKGROUND

Food and Drug Administration-mandated postmarket studies for transcatheter aortic valve replacement in low-risk populations plan to use passively collected registry data linked to claims for long-term follow-up out to 10 years. Therefore, it is critically important to understand the validity of these claims-based end points. We sought to evaluate the ability of administrative claims with () codes to identify trial-adjudicated end points and reproduce treatment comparisons of aortic valve replacement in the Evolut Low Risk Trial.

METHODS

We linked Evolut Low Risk trial patients to the Medicare Provider Analysis and Review database. We calculated sensitivity, specificity, positive predictive value, negative predictive value, and agreement statistic of claims to detect clinical end points through 2 years in trial patients. We additionally compared end points across treatment arms using trial-adjudicated outcomes versus claims-based outcomes.

RESULTS

Trial-adjudicated deaths were perfectly identified by claims. Claims had good performance in identifying trial-adjudicated disabling stroke (sensitivity 68.8%, specificity 99.0%, positive predictive value 64.7%, negative predictive value 99.1%, =0.66) and pacemaker placement (sensitivity 85.2%, specificity 98.4%, positive predictive value 90.4%, negative predictive value 97.5%, =0.86), but more modest performance in identifying trial-adjudicated myocardial infarction (=0.46) and vascular complications (=0.45). There was no difference between treatment arms for the primary end point of death or disabling stroke using trial data (hazard ratio, 0.83 [95% CI, 0.41-1.68]) or claims data (hazard ratio, 0.89 [95% CI, 0.43-1.81]; interaction =0.71).

CONCLUSIONS

Claims-based end points performed well in ascertaining death, disabling stroke, and pacemaker placement and were able to reproduce principal trial findings. These results support the selective use of claims-based end points for transcatheter aortic valve replacement postmarketing surveillance.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02701283.

摘要

背景

美国食品药品监督管理局要求对低风险人群进行经导管主动脉瓣置换术的上市后研究,计划使用被动收集的登记数据,并将其与索赔数据相链接,以进行长达10年的长期随访。因此,了解这些基于索赔的终点指标的有效性至关重要。我们试图评估带有()代码的行政索赔识别试验判定终点指标以及重现Evolut低风险试验中主动脉瓣置换术治疗比较结果的能力。

方法

我们将Evolut低风险试验患者与医疗保险提供者分析和审查数据库相链接。我们计算了索赔检测试验患者2年内临床终点指标的敏感性、特异性、阳性预测值、阴性预测值和一致性统计量。我们还使用试验判定的结果与基于索赔的结果比较了各治疗组的终点指标。

结果

索赔能够完美识别试验判定的死亡情况。索赔在识别试验判定的致残性卒中(敏感性68.8%,特异性99.0%,阳性预测值64.7%,阴性预测值99.1%,κ=0.66)和起搏器植入(敏感性85.2%,特异性98.4%,阳性预测值90.4%,阴性预测值97.5%,κ=0.86)方面表现良好,但在识别试验判定的心肌梗死(κ=0.46)和血管并发症(κ=0.45)方面表现一般。使用试验数据(风险比,0.83[95%CI,0.41-1.68])或索赔数据(风险比,0.89[95%CI,0.43-1.81];交互作用=0.71)时,各治疗组在死亡或致残性卒中主要终点指标方面没有差异。

结论

基于索赔的终点指标在确定死亡、致残性卒中和起搏器植入方面表现良好,并且能够重现试验的主要结果。这些结果支持在经导管主动脉瓣置换术上市后监测中选择性地使用基于索赔的终点指标。

注册

网址:https://www.clinicaltrials.gov;唯一标识符:NCT02701283。

相似文献

1
Use of Claims to Assess Outcomes and Treatment Effects in the Evolut Low Risk Trial.在Evolut低风险试验中使用索赔数据评估结果和治疗效果
Circ Cardiovasc Interv. 2025 Jan;18(1):e014592. doi: 10.1161/CIRCINTERVENTIONS.124.014592. Epub 2025 Jan 21.

本文引用的文献

1
Use of Discharge Disposition to Determine Stroke Severity After TAVR.利用出院处置情况确定经导管主动脉瓣置换术后的卒中严重程度
Circ Cardiovasc Interv. 2024 Sep;17(9):e013698. doi: 10.1161/CIRCINTERVENTIONS.123.013698. Epub 2024 Jun 5.
5
STS-ACC TVT Registry of Transcatheter Aortic Valve Replacement.经导管主动脉瓣置换术的STS-ACC TVT注册研究
J Am Coll Cardiol. 2020 Nov 24;76(21):2492-2516. doi: 10.1016/j.jacc.2020.09.595.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验