Developing and evaluating a machine-learning-based algorithm to predict the incidence and severity of ARDS with continuous non-invasive parameters from ordinary monitors and ventilators.

OBJECTIVES

Major observational studies report that the mortality rate of acute respiratory distress syndrome (ARDS) is close to 40%. Different treatment strategies are required for each patient, according to the degree of ARDS. Early prediction of ARDS is helpful to implement targeted drug therapy and mechanical ventilation strategies for patients with different degrees of potential ARDS. In this paper, a new dynamic prediction machine learning model for ARDS incidence and severity is established and evaluated based on 28 parameters from ordinary monitors and ventilators, capable of dynamic prediction of the incidence and severity of ARDS. This new method is expected to meet the clinical practice requirements of user-friendliness and timeliness for wider application.

METHODS

A total of 4738 hospitalized patients who required ICU care from 159 hospitals are employed in this study. The models are trained by standardized data from electronic medical records. There are 28 structured, continuous non-invasive parameters that are recorded every hour. Seven machine learning models using only continuous, non-invasive parameters are developed for dynamic prediction and compared with methods trained by complete parameters and the traditional risk adjustment method (i.e., oxygenation saturation index method).

RESULTS

The optimal prediction performance (area under the curve) of the ARDS incidence and severity prediction models built using continuous noninvasive parameters reached0.8691 and 0.7765, respectively. In terms of mild and severe ARDS prediction, the AUC values are both above 0.85. The performance of the model using only continuous non-invasive parameters have an AUC of 0.0133 lower, in comparison with that employing a complete feature set, including continuous non-invasive parameters, demographic information, laboratory parameters and clinical natural language text.

CONCLUSIONS

A machine learning method was developed in this study using only continuous non-invasive parameters for ARDS incidence and severity prediction. Because the continuous non-invasive parameters can be easily obtained from ordinary monitors and ventilators, the method presented in this study is friendly and convenient to use. It is expected to be applied in pre-hospital setting for early ARDS warning.