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Prediction of prolonged mechanical ventilation
This study aims to develop a predictive model of the ventilatory status 21 days after the start of invasive mechanical ventilation in patients admitted to intensive care units in two Argentine hospitals.
Using a retrospective cohort , models based on logistic regression and machine learning algorithms will be built.
This research seeks to optimize clinical decision-making in the first week of ventilatory support , in order to improve outcomes in critical patients.
Investigador principal:
Gustavo Olaizola
2
Centros Hospitalarios con Comité de Ética aprobado
Estado del proyecto:
Statistical analysis
Reclutamiento de investigadores asociados cerrado
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