Publications by authors named "Rachel Jooste"

Background: Artificial intelligence, through improved data management and automated summarisation, has the potential to enhance intensive care unit (ICU) care. Large language models (LLMs) can interrogate and summarise large volumes of medical notes to create succinct discharge summaries. In this study, we aim to investigate the potential of LLMs to accurately and concisely synthesise ICU discharge summaries.

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Background: Bleeding and thrombosis are major complications of veno-venous (VV) extracorporeal membrane oxygenation (ECMO).

Objectives: To assess thrombosis, major bleeding (MB), and 180-day survival in patients supported by VV-ECMO between the first (March 1 to May 31, 2020) and second (June 1, 2020, to June 30, 2021) waves of the COVID-19 pandemic.

Methods: An observational study of 309 consecutive patients (aged ≥18years) with severe COVID-19 supported by VV-ECMO was performed in 4 nationally commissioned ECMO centers in the United Kingdom.

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The National ECMO Service for patients in acute severe respiratory failure in England responded to the challenge of the coronavirus pandemic by implementing a central electronic referral system within days. Prior to this, each ECMO centre managed independently around 20 ECMO referrals per month. Early during the pandemic, we recognised the need for a referral system to co-ordinate the anticipated increased number of referrals.

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Bleeding and thrombosis are major complications in patients supported with extracorporeal membrane oxygenation (ECMO). In this multicentre observational study of 152 consecutive patients (≥18 years) with severe COVID-19 supported by veno-venous (VV) ECMO in four UK commissioned centres during the first wave of the COVID-19 pandemic (1 March to 31 May 2020), we assessed the incidence of major bleeding and thrombosis and their association with 180-day mortality. Median age (range) was 47 years (23-65) and 75% were male.

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