Novel criteria to classify ARDS severity using a machine learning approach

BACKGROUND: 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(2)/(FiO(2)xPEEP) or P/FP(E)] for PEEP ≥ 5 to address Berlin’s definition gap for ARDS severity by using machine learning (ML) approaches. METHODS: We examined P/FP(E) 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(2)/FiO(2) criteria with P/FP(E) under three different scenarios. We extracted clinical data from the first 3 ICU days after ARDS onset (N = 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 (N = 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. RESULTS: P/FP(E) ratio outperformed PaO(2)/FiO(2) 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). CONCLUSIONS: The novel P/FP(E) ratio to assess ARDS severity after onset over time is markedly better than current PaO(2)/FiO(2) criteria. The use of P/FP(E) could help to manage ARDS patients with a more precise therapeutic regimen for each ARDS category of severity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-021-03566-w.