Sex Differences in Outcomes of Percutaneous Pulmonary Artery Thrombectomy in Patients With Pulmonary Embolism

Evolving patterns of intracranial hemorrhage in advanced therapies in patients with acute pulmonary embolism

BACKGROUND

Dissecting trends and contributing risk factors for intracranial hemorrhage (ICH) in patients treated for acute pulmonary embolism (PE) may allow for a better patient selection for existing and emerging treatment options.

METHODS

The German nationwide inpatient sample was screened for patients admitted due to PE 2005-2020. Hospitalizations were stratified for the occurrence of ICH; risk factors for ICH and temporal trends were investigated.

RESULTS

Overall, 816,653 hospitalizations due to acute PE in the period 2005-2020 were analyzed in the study. ICH was reported in 2516 (0.3 %) hospitalizations, and time trend analysis revealed a fluctuating but overall, largely unchanged annual incidence. There was an increase of ICH with age. Patients with ICH had a higher comorbidity burden (Charlson-Comorbidity-Index [CCI], 5.0 [4.0-7.0] vs. 4.0 [2.0-5.0]; P < 0.001), and higher CCI was associated with an OR of 1.26 (95%CI 1.24-1.27) for ICH. Further independent risk factors for ICH were age ≥ 70 years (OR 1.23 [1.12-1.34]), severe (versus low-risk) PE (OR 3.09 [2.84-3.35]), surgery (OR 1.59 [1.47-1.72]), acute kidney injury (OR 3.60 [3.09-4.18]), and ischemic stroke (OR 14.64 [12.61-17.00]). The identified risk factors for ICH varied among different reperfusion treatment groups. As expected, ICH had a substantial impact on case-fatality of PE (OR 6.16 [5.64-6.72]; P < 0.001).

CONCLUSIONS

Incidence of ICH in patients hospitalized for acute PE in Germany was overall low and depended on the patients' comorbidity burden. Identifying patients at risk for ICH allows tailored patient selection for the different reperfusion treatments and might prevent ICH.

Trends in Percutaneous Device Use for the Treatment of Venous Thromboembolism Over Time in the PINC AI Healthcare Database and the National Inpatient Sample

The number of different methods of reperfusion therapy to treat venous thromboembolism (VTE) has increased substantially. Nevertheless, investigation of data representativeness and device-level use in administrative databases has been limited. Using the National Inpatient Sample (NIS) and the PINC AI Healthcare Database (PHD), all hospital encounters with a diagnosis code of VTE were identified between January 1, 2016 and December 31, 2020. Patient demographics and trends in treatment modalities were evaluated over time. An algorithm was developed to identify specific devices used for VTE treatment in the PHD cohort. A total of 145,870 patients with VTE treated with reperfusion therapy were identified in the NIS (pulmonary embolism [PE] 88,725, isolated deep vein thrombosis [iDVT] 57,145) and 39,311 in the PHD (PE 25,383, iDVT 13,928). Patient demographics were qualitatively similar in the NIS and PHD. Over time, there was a significant increase in the use of mechanical thrombectomy in the PE and iDVT populations (p <0.05 in both databases), with catheter-directed thrombolysis use plateauing in PE (p = 0.83 and p = 0.14 in NIS and PHD, respectively) and significantly decreasing for the iDVT population (p <0.05 in both databases). In the PHD cohort, specific reperfusion devices were identified in 14,105 patients (PE 9,098, iDVT 5,007). In conclusion, the use of mechanical thrombectomy for the treatment of VTE has increased over time, whereas the rates of catheter-directed thrombolysis therapy have remained stagnant or decreased. Further research is needed to understand the uptake of these treatment modalities and the unique abilities of the PHD to study specific device therapy in the VTE population.

A cross-sectional study of outcomes for patients undergoing mechanical thrombectomy for pulmonary embolism during 2018-2022: Insights from the PINC AI Healthcare Database

Background and Aims

Mechanical thrombectomy (MT) treatments for pulmonary embolism (PE) have yet to be compared directly. We aimed to determine if patient outcomes varied following treatment of PE with different MT devices.

Methods

All PE encounters with an index treatment of MT between January 2018 and March 2022 were analyzed for in-hospital mortality, discharge to home, and 30-day readmission outcomes in the PINC AI™ Healthcare Database. MT devices used in each encounter were extracted from hospital charge description free-text fields using keyword text and fuzzy matching. Unadjusted and adjusted logistic regression was used to model outcomes by device.

Results

A total of 5893 encounters were identified using MT as the sole index PE treatment and 1812 using MT with another treatment. Of these, 41% had insufficient information to identify the devices used (unspecified MT), 33% used the FlowTriever System (large-bore volume-controlled aspiration MT), 23% the Indigo System (continuous aspiration MT), and 3% some other MT. Large-bore volume-controlled aspiration MT was used with other treatments 13% of the time compared with 23% and 39% for unspecified MT and continuous aspiration MT, respectively. Adjusted logistic regression modeling revealed the odds of in-hospital mortality were significantly higher for patients treated with unspecified MT ([OR] = 1.42, 95% confidence interval [CI]: [1.10-1.83], p = 0.008) or continuous aspiration MT (OR = 1.63, 95% CI: [1.21-2.19], p = 0.001) compared with large-bore volume-controlled aspiration MT. Discharge to home was significantly lower in these same groups (OR = 0.84, 95% CI: [0.73-0.96], p = 0.01, and OR = 0.63, 95% CI: [0.53-0.74], p < 0.001, respectively), but readmission risks at 30 days were comparable (OR = 1.08, 95% CI: [0.84-1.38], p = 0.56, and OR = 1.20, 95% CI: [0.89-1.62], p = 0.24, respectively).

Conclusion

PE outcomes and treatment patterns differ significantly based on the type of MT utilized. Clinical studies directly comparing MT treatments are needed to further understand optimal treatment of PE.

A Review of Healthcare Disparities Relevant to Interventional Radiology

Racial, ethnic, and gender disparities have received focused attention recently, as they became more visible in the COVID era. We continue to learn more about how healthcare disparities manifest for our patients and, more broadly, the structural underpinnings that result in predictable outcomes gaps. This review summarizes what we know about disparities relevant to interventional radiologists. The prevalence and magnitude of disparities are quantified and discussed where relevant. Specific examples are provided to demonstrate how factors like gender, ethnicity, social status, geography, etc. interact to create inequities in the delivery of interventional radiology (IR) care. Understanding and addressing health disparities in IR is crucial for improving real-world patient outcomes and reducing the economic burden associated with ineffective and low-value care. Finally, the importance of intentional mentorship, outreach, education, and equitable distribution of high-quality healthcare to mitigate these disparities and promote health equity in interventional radiology is discussed.