Selected article for: "accurately identify and machine learning"

Author: Tice, Connor; Seigerman, Matthew; Fiorilli, Paul; Pugliese, Steven C.; Khandhar, Sameer; Giri, Jay; Kobayashi, Taisei
Title: Management of Acute Pulmonary Embolism
  • Cord-id: wg49p0yb
  • Document date: 2020_10_6
  • ID: wg49p0yb
    Snippet: PURPOSE OF THE REVIEW: Over 100,000 cardiovascular-related deaths annually are caused by acute pulmonary embolism (PE). While anticoagulation has historically been the foundation for treatment of PE, this review highlights the recent rapid expansion in the interventional strategies for this condition. RECENT FINDINGS: At the time of diagnosis, appropriate risk stratification helps to accurately identify patients who may be candidates for advanced therapeutic interventions. While systemic thrombo
    Document: PURPOSE OF THE REVIEW: Over 100,000 cardiovascular-related deaths annually are caused by acute pulmonary embolism (PE). While anticoagulation has historically been the foundation for treatment of PE, this review highlights the recent rapid expansion in the interventional strategies for this condition. RECENT FINDINGS: At the time of diagnosis, appropriate risk stratification helps to accurately identify patients who may be candidates for advanced therapeutic interventions. While systemic thrombolytics (ST) is the mostly commonly utilized intervention for high-risk PE, the risk profile of ST for intermediate-risk PE limits its use. Assessment of an individualized patient risk profile, often via a multidisciplinary pulmonary response team (PERT) model, there are various interventional strategies to consider for PE management. Novel therapeutic options include catheter-directed thrombolysis, catheter-based embolectomy, or mechanical circulatory support for certain high-risk PE patients. Current data has established safety and efficacy for catheter-based treatment of PE based on surrogate outcome measures. However, there is limited long-term data or prospective comparisons between treatment modalities and ST. While PE diagnosis has improved with modern cross-sectional imaging, there is interest in improved diagnostic models for PE that incorporate artificial intelligence and machine learning techniques. SUMMARY: In patients with acute pulmonary embolism, after appropriate risk stratification, some intermediate and high-risk patients should be considered for interventional-based treatment for PE.

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