Publicações Científicas

J Crit Care, December 2018

Predictive factors of weaning from mechanical ventilation and extubation outcome: A systematic review

Highlight: Forty-three articles (7929 patients) were analyzed, presenting 56 different parameters related to weaning/extubation outcome.

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PLOS One, March 2021

Prediction of extubation outcome in mechanically ventilated patients: Development and validation of the Extubation Predictive Score (ExPreS)

Highlight: In the validation cohort, the use of ExPreS decreased the extubation failure rate from 8.2% to 2.4%, even in a cohort of more severe patients. It is a simple method and is easily applied at the bedside.

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J Intensive Care Med, September 2022

Predictive Factors of Extubation Failure in COVID-19 Mechanically Ventilated Patients

Highlight: COVID-19 patients had an extubation failure risk that was almost three times higher than non-COVID-19 patients, with the extubation of the former being delayed compared to the latter. Furthermore, an age ≥ 66 years, time of symptoms ≥ 31 days, need of dialysis, and PaO2/FiO2 ratio > 200 were independent predictors for extubation failure.

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Eur J Cancer Prev, March 2025

Predictive factors for the response to neoadjuvant treatment in patients with stage II and III breast cancer

Highlight: Age, molecular subtype, and staging, diabetes was significantly associated with the response to neoadjuvant chemotherapy, highlighting the importance of elucidating the mechanism by which diabetes may impair.

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Crit Care Sci , April 2025

Should anthropometric differences be considered when calculating the Rapid Shallow Breathing Index as a predictor of weaning outcomes in mechanically ventilated patients?

Highlight: this study shows that RSBI has different accuracies depending on the anthropometric specificities of patients and proposes a discussion of its limitations and possible ways to improve its accuracy, especially for patients whose average anthropometric characteristics are not known.

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J Crit Care, February 2026

Enhancing mechanical ventilation management with AI: Computer vision for automated detection of ventilatory modes, parameters and asynchrony

Highlight: A non-invasive, scalable solution that enables real-time ventilator monitoring using only mobile device photos. High performance in detecting PVA, with broad applicability across clinical environments and ventilator types.

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