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Advancing ICU patient care with a Real-Time predictive model for mechanical Power to mitigate VILI.
Ruiz-Botella, M; Manrique, S; Gomez, J; Bodí, M.
Afiliação
  • Ruiz-Botella M; Departament of Chemical Engineering, Universitat Rovira I Virgili, Tarragona, Spain; Instituto de Investigación Sanitaria Pere i Virgili, Universidad Rovira i Virgili, Tarragona, Spain. Electronic address: manuel.ruiz@urv.cat.
  • Manrique S; Instituto de Investigación Sanitaria Pere i Virgili, Universidad Rovira i Virgili, Tarragona, Spain; Critical Care department, Hospital Universitario Joan XXIII, Tarragona, Spain.
  • Gomez J; Instituto de Investigación Sanitaria Pere i Virgili, Universidad Rovira i Virgili, Tarragona, Spain; Critical Care department, Hospital Universitario Joan XXIII, Tarragona, Spain.
  • Bodí M; Instituto de Investigación Sanitaria Pere i Virgili, Universidad Rovira i Virgili, Tarragona, Spain; Critical Care department, Hospital Universitario Joan XXIII, Tarragona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES). Instituto de Salud Carlos III, Spain.
Int J Med Inform ; 189: 105511, 2024 Sep.
Article em En | MEDLINE | ID: mdl-38851133
ABSTRACT

BACKGROUND:

Invasive Mechanical Ventilation (IMV) in Intensive Care Units (ICU) significantly increases the risk of Ventilator-Induced Lung Injury (VILI), necessitating careful management of mechanical power (MP). This study aims to develop a real-time predictive model of MP utilizing Artificial Intelligence to mitigate VILI.

METHODOLOGY:

A retrospective observational study was conducted, extracting patient data from Clinical Information Systems from 2018 to 2022. Patients over 18 years old with more than 6 h of IMV were selected. Continuous data on IMV variables, laboratory data, monitoring, procedures, demographic data, type of admission, reason for admission, and APACHE II at admission were extracted. The variables with the highest correlation to MP were used for prediction and IMV data was grouped in 15-minute intervals using the mean. A mixed neural network model was developed to forecast MP 15 min in advance, using IMV data from 6 h before the prediction and current patient status. The model's ability to predict future MP was analyzed and compared to a baseline model predicting the future value of MP as equal to the current value.

RESULTS:

The cohort consisted of 1967 patients after applying inclusion criteria, with a median age of 63 years and 66.9 % male. The deep learning model achieved a mean squared error of 2.79 in the test set, indicating a 20 % improvement over the baseline model. It demonstrated high accuracy (94 %) in predicting whether MP would exceed a critical threshold of 18 J/min, which correlates with increased mortality. The integration of this model into a web platform allows clinicians real-time access to MP predictions, facilitating timely adjustments to ventilation settings.

CONCLUSIONS:

The study successfully developed and integrated in clinical practice a predictive model for MP. This model will assist clinicians allowing for the adjustment of ventilatory parameters before lung damage occurs.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Respiração Artificial / Lesão Pulmonar Induzida por Ventilação Mecânica / Unidades de Terapia Intensiva Limite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Int J Med Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Respiração Artificial / Lesão Pulmonar Induzida por Ventilação Mecânica / Unidades de Terapia Intensiva Limite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Int J Med Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article
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