Abstract
Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subsequently spread globally. COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making, considering that baseline comorbidities, age, and patient conditions at admission have been associated to poorer outcomes. Supervised machine learning techniques are increasingly diffuse in clinical medicine and can predict mortality and test associations reaching high predictive performance. We assessed performances of a machine learning approach to predict mortality in COVID-19 patients admitted to ICU using data from the Lombardy ICU Network.Methods: this is a secondary analysis of prospectively collected data from Lombardy ICU network. To predict survival at 7-,14- and 28 days we built two different models; model A included patient demographics, medications before admission and comorbidities, while model B also included the data of the first day since ICU admission. 10-fold cross validation was repeated 2500 times, to ensure optimal hyperparameter choice. The only constrain imposed to model optimization was the choice of logistic regression as final layer to increase clinical interpretability. Different imputation and over-sampling techniques were employed in model training.Results 1503 patients were included, with 766 deaths (51%). Exploratory analysis and Kaplan-Meier curves demonstrated mortality association with age and gender. Model A and B reached the greatest predictive performance at 28 days (AUC 0.77 and 0.79), with lower performance at 14 days (AUC 0.72 and 0.74) and 7 days (AUC 0.68 and 0.71). Male gender, age and number of comorbidities were strongly associated with mortality in both models. Among comorbidities, chronic kidney disease and chronic obstructive pulmonary disease demonstrated association. Mode of ventilatory assistance at ICU admission and Fraction of Inspired oxygen were associated with mortality in model B.Conclusions Supervised machine learning models demonstrated good performance in prediction of 28-day mortality. 7-days and 14-days predictions demonstrated lower performance. Machine learning techniques may be useful in emergency phases to reach higher predictive performance with reduced human supervision using complex data.
Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation
