• Medientyp: E-Artikel
  • Titel: Novel criteria to classify ARDS severity using a machine learning approach
  • Beteiligte: Sayed, Mohammed; Riaño, David; Villar, Jesús
  • Erschienen: Springer Science and Business Media LLC, 2021
  • Erschienen in: Critical Care
  • Sprache: Englisch
  • DOI: 10.1186/s13054-021-03566-w
  • ISSN: 1364-8535
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: <jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>Usually, arterial oxygenation in patients with the acute respiratory distress syndrome (ARDS) improves substantially by increasing the level of positive end-expiratory pressure (PEEP). Herein, we are proposing a novel variable [PaO<jats:sub>2</jats:sub>/(FiO<jats:sub>2</jats:sub>xPEEP) or P/FP<jats:sub>E</jats:sub>] for PEEP ≥ 5 to address Berlin’s definition gap for ARDS severity by using machine learning (ML) approaches.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>We examined P/FP<jats:sub>E</jats:sub> values delimiting the boundaries of mild, moderate, and severe ARDS. We applied ML to predict ARDS severity after onset over time by comparing current Berlin PaO<jats:sub>2</jats:sub>/FiO<jats:sub>2</jats:sub> criteria with P/FP<jats:sub>E</jats:sub> under three different scenarios. We extracted clinical data from the first 3 ICU days after ARDS onset (<jats:italic>N</jats:italic> = 2738, 1519, and 1341 patients, respectively) from MIMIC-III database according to Berlin criteria for severity. Then, we used the multicenter database eICU (2014–2015) and extracted data from the first 3 ICU days after ARDS onset (<jats:italic>N</jats:italic> = 5153, 2981, and 2326 patients, respectively). Disease progression in each database was tracked along those 3 ICU days to assess ARDS severity. Three robust ML classification techniques were implemented using Python 3.7 (LightGBM, RF, and XGBoost) for predicting ARDS severity over time.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>P/FP<jats:sub>E</jats:sub> ratio outperformed PaO<jats:sub>2</jats:sub>/FiO<jats:sub>2</jats:sub> ratio in all ML models for predicting ARDS severity after onset over time (MIMIC-III: AUC 0.711–0.788 and CORR 0.376–0.566; eICU: AUC 0.734–0.873 and CORR 0.511–0.745).</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The novel P/FP<jats:sub>E</jats:sub> ratio to assess ARDS severity after onset over time is markedly better than current PaO<jats:sub>2</jats:sub>/FiO<jats:sub>2</jats:sub> criteria. The use of P/FP<jats:sub>E</jats:sub> could help to manage ARDS patients with a more precise therapeutic regimen for each ARDS category of severity.</jats:p> </jats:sec>
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