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October 13, 2024Journal of Clinical Medicine5 citationsOpen Access

Pulmonary Embolism Management Audit and Machine Learning Analysis of Delayed Anticoagulation in a Swiss Teaching Hospital

CKCedrine KuengUniversity of BaselMBMaria BoesingUniversity of BaselSGStéphanie GiezendannerUniversity of Bern

Key Points

  • To audit clinical adherence to standard pulmonary embolism management protocols and evaluate factors contributing to delayed anticoagulation.
  • Conducted a clinical management audit alongside machine learning analysis of pulmonary embolism care at a Swiss teaching hospital.

Structured PICO

P
Population
Patients with pulmonary embolism managed at a Swiss teaching hospital (KSBL)
I
Intervention
Clinical audit and machine learning analysis of delayed anticoagulation

A clinical audit of pulmonary embolism management identified specific gaps in risk assessment and follow-up care that represent targets for quality improvement.

Abstract

In conclusion, while the management of PE at the KSBL generally adheres to high standards, there are areas for improvement, particularly in the morning performance, the use of a pretest probability assessment, D-dimer measurement, risk assessment via the PESI score, the performance of complementary leg ultrasounds, clarification of the anticoagulation duration, and follow-up management.

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Cite This Study

Kueng et al. (2024) studied this question.

synapsesocial.com/papers/69fa89aaaa3ec536f25120c4https://doi.org/10.3390/jcm13206103
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