Background: Several studies have investigated the correlation between physiological parameters and the risk of acute respiratory distress syndrome (ARDS); however, cause-specific ARDS and its early prediction have not been well-studied. We aimed to develop and validate a machine-learning model for the early prediction of inhalation-induced ARDS.
Methods: Clinical expertise was applied with data-driven analysis. Using data from electronic intensive care units (retrospective derivation cohort) and the three most accessible vital signs (i.e. heart rate, temperature, and respiratory rate) together with feature engineering, we applied a random forest approach during the time window of 90 hours that ended 6 hours prior to the onset of moderate-to-severe respiratory failure (the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen ≤200 mmHg).
Results: The trained random forest classifier was validated using an independent validation cohort, with an area under the curve of 0.9026 (95% confidence interval 0.8075-1) and optimal threshold probability, the accuracy, sensitivity, and specificity values were 0.8947, 1, and 0.875, respectively. A Stable and Interpretable RUle Set (SIRUS) was used to extract rules from the RF to provide guidelines for clinicians. We identified several predictive factors, including resp_96h_6h_min < 9, resp_96h_6h_mean ≥ 16.1, HR_96h_6h_mean ≥ 102, and temp_96h_6h_max > 100, that could be used for predicting inhalation-induced ARDS (moderate-to-severe condition) 6 hours prior to onset in critical care units. (‘xxx_96h_6h_min/mean/max’: the minimum/mean/maximum values of the xxx vital sign collected during a 90 h time window beginning 96 h prior to the onset of ARDS and ending 6 h prior to the onset from every recorded blood gas test)
Conclusions: This newly established random forest‑based interpretable model shows good predictive ability for inhalation-induced ARDS and may assist clinicians in decision-making, as well as facilitate the enrolment of patients in prevention programmes to improve their outcomes.