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. 2024 Jul 25;3(8):102149.
doi: 10.1016/j.jscai.2024.102149. eCollection 2024 Aug.

Outcomes From Mechanical Thrombectomy for Deep Vein Thrombosis: Insights From the PINC AI Healthcare Database

Derek Mittleider  1 , C Michael Gibson  2 , David Dexter  3
Affiliations

Outcomes From Mechanical Thrombectomy for Deep Vein Thrombosis: Insights From the PINC AI Healthcare Database

Derek Mittleider et al. J Soc Cardiovasc Angiogr Interv. .

Abstract

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.

Keywords: deep vein thrombosis; intervention; lower extremity; real-world data; venous thrombosis.

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Conflict of interest statement

Derek Mittleider is a consultant for Boston Scientific and Inari Medical. C. Michael Gibson received research grant support from Inari Medical, Boston Scientific, and Penumbra and consultant for Inari Medical. David Dexter is a consultant for AngioDynamics, Boston Scientific, Penumbra, and Inari Medical.

Figures

Figure 1
Figure 1
Mechanical thrombectomy subject identification from PINC AI Healthcare Database. The distillation of subjects into named device categories used for this analysis.
Figure 2
Figure 2
Observed outcomes and resource utilization. Observed rates of (A) in-hospital all-cause mortality, (B) all-cause 30-day readmission, (C) patients receiving any adjunctive thrombolytic therapy during the hospital encounter, and (D) patients receiving ICU monitoring postprocedure.
Central Illustration
Central Illustration
Outcomes from regression analysis. (A) Adjusted odds ratios for in-hospital mortality, all-cause 30-day readmission, postprocedural transfusion, and postprocedural ICU stay. (B) Adjusted estimates for postprocedural hospital and ICU LOS. Both panels compare encounters with AJ and IN with CT encounters. ∗Postprocedure. AJ, AngioJet; CT, ClotTriever; IN, Indigo.
Figure 3
Figure 3
Observed charges and costs. (A) Average total charges (graph) and median total charges (tabulated) per device encounter. (B) Average cost incurred by hospital (graph) and median cost incurred (tabulated) per device encounter.
See this image and copyright information in PMC

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