Publications by authors named "T E Lamas"

Extracorporeal carbon dioxide removal (ECCOR) is an emerging technique designed to reduce carbon dioxide (CO) levels in venous blood while enabling lung-protective ventilation or alleviating the work of breathing. Unlike high-flow extracorporeal membrane oxygenation (ECMO), ECCOR operates at lower blood flows (0.4-1.

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Integrating machine learning (ML) into intensive care units (ICUs) can significantly enhance patient care and operational efficiency. ML algorithms can analyze vast amounts of data from electronic health records, physiological monitoring systems, and other medical devices, providing real-time insights and predictive analytics to assist clinicians in decision-making. ML has shown promising results in predictive modeling for patient outcomes, early detection of sepsis, optimizing ventilator settings, and resource allocation.

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Extracorporeal life support (ECLS) for severe respiratory failure has seen an exponential growth in recent years. Extracorporeal membrane oxygenation (ECMO) and extracorporeal CO removal (ECCOR) represent two modalities that can provide full or partial support of the native lung function, when mechanical ventilation is either unable to achieve sufficient gas exchange to meet metabolic demands, or when its intensity is considered injurious. While the use of ECMO has defined indications in clinical practice, ECCOR remains a promising technique, whose safety and efficacy are still being investigated.

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Article Synopsis
  • Increasing cancer survival rates have led to more cancer patients being admitted to ICUs, prompting a need to assess their outcomes compared to non-cancer patients.
  • A study analyzed 236 oncological patients admitted to a polyvalent ICU from 2013 to 2017, finding that hematological patients experienced worse outcomes, longer ICU stays, and higher treatment requirements than solid tumor patients.
  • The study concluded that the SAPS II and APACHE II scores effectively predict mortality in cancer patients, with a survival rate of 48% at six months being higher than previously reported.
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In the original published article, the sentence "Nevertheless, semi-natural forest remnants continue to be harvested and fragmented (Svensson et al. 2018; Jonsson et al. 2019), and over 2000 forest-associated species (of 15 000 assessed) are listed as threatened on Sweden's red-list, largely represented by macro-fungi, beetles, lichens and butterflies (Sandström 2015).

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