深静脉血栓形成的机械血栓切除术效果:PINC AI 医疗保健数据库的启示

Derek Mittleider MD , C. Michael Gibson MD, MS , David Dexter MD
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引用次数: 0

摘要

背景机械血栓切除术(MT)在治疗深静脉血栓形成(DVT)方面发挥着越来越重要的作用。尽管独立研究显示了一定程度的安全性和有效性,但仍缺乏 MT 设备之间的比较证据。方法从 PINC AI 医疗保健数据库中识别出 2018 年 1 月至 2022 年 3 月间接受 MT 治疗深静脉血栓的患者,并将其分为 AngioJet ZelanteDVT (AJ)、ClotTriever 系统 (CT) 和 Indigo 系统 (IN) 的分析人群。比较了院内死亡率、资源利用率和 30 天再入院率。通过回归模型对潜在的协变量进行了调整,并对结果进行了比较。结果 共确定了 4455 例 MT 诊断并符合纳入标准(AJ,1753 例;CT,1344 例;IN,1358 例)。院内死亡率从 1.0% (CT)到 2.9% (IN)不等,建模预测 AJ 组(几率比 [OR],3.42)和 IN 组(OR,3.38)的几率明显更高。同样,与参照组(CT)相比,预测 AJ 组和 IN 组的资源利用率更高。平均费用从 29,549 美元(CT:标清值为 30,705 美元)到 42,705 美元(IN:标清值为 41,114 美元)不等。30天再入院率从10.0%(AJ)到14.6%(IN)不等,而建模预测IN组的几率明显更高(OR,1.47)。结论这些结果表明,所有MT干预在结果和资源方面可能是不平等的,CT设备与较低的院内死亡率和资源负担相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Outcomes From Mechanical Thrombectomy for Deep Vein Thrombosis: Insights From the PINC AI Healthcare Database

Background

Mechanical thrombectomy (MT) is playing an increasingly important role in treating deep vein thrombosis (DVT). Although degrees of safety and efficacy have been shown in independent studies, there remains a lack of comparative evidence between MT devices. To address this, we aimed to compare demographics, clinical outcomes, and resource metrics of patients receiving MT for DVT with 3 common devices using a real-world database.

Methods

Patients receiving MT for DVT between January 2018 and March 2022 were identified from the PINC AI Healthcare Database and divided into analysis populations for the AngioJet ZelanteDVT (AJ), the ClotTriever system (CT), and the Indigo system (IN). Rates of in-hospital mortality, resource utilization, and 30-day readmission were compared. Regression modeling was performed to adjust for potential covariates and compare outcomes.

Results

A total of 4455 MT encounters were identified and met inclusion criteria (AJ, 1753; CT, 1344; IN, 1358). In-hospital mortality ranged from 1.0% (CT) to 2.9% (IN), with modeling predicting significantly higher odds for the AJ (odds ratio [OR], 3.42) and IN (OR, 3.38) groups. Similarly, higher rates of resource utilization were predicted in the AJ and IN groups when compared with the reference group (CT). Average costs ranged from $29,549 (CT: SD, $30,705) to $42,705 (IN: SD, $41,114). Thirty-day readmissions ranged from 10.0% (AJ) to 14.6% (IN), while modeling predicted significantly greater odds for the IN group (OR, 1.47).

Conclusions

These results suggest that all MT interventions may be unequal in terms of outcomes and resources, with the CT device associated with lower in-hospital mortality and resource burden.

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