Roberto Noorduijn Londono

217 Neighborhood Socioeconomic Disadvantage and STI Positivity Among Emergency Department Patients in New York City

Rachel Emily Solnick, Carmen C. Vargas-Torres, Patricia Martínez‐Sánchez, Roberto Noorduijn Londono et autres

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Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning

Tariq A. Dam, Luca F. Roggeveen, Fuda van Diggelen, Lucas M. Fleuren et autres

For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor …

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Predictors for extubation failure in COVID-19 patients using a machine learning approach

Lucas M. Fleuren, Tariq A. Dam, Michele Tonutti, Daan P. de Bruin et autres

INTRODUCTION: Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. METHODS: We used highly granular data from 3464 adult critically ill …

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Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Lucas M. Fleuren, Michele Tonutti, Daan P. de Bruin, Robbert C. A. Lalisang et autres

Abstract Background The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a …

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