Spanish Influenza Score (Sis): Usefulness Of Machine Learning In The Development Of An Early Mortality Prediction Score In Severe Influenza

Objective: To develop a mortality prediction score (Spanish Influenza Score [SIS]) for patients with severe influenza considering only variables at ICU admission, and compare its performance respect of Random Forest (RF).Design: Sub-analysis from the GETGAG/SEMICYUC database.Scope: Intensive Care Medicine.Patients: Patients admitted to 184 Spanish ICUs (2009-2018) with influenza infection Intervention: None.Variables: Demographic data, severity of illness, times from symptoms onset until hospital admission (Gap-H), hospital to ICU (Gap-ICU) or hospital to diagnosis (Gap-Dg), antiviral vaccination, number of quadrants infiltrated, acute renal failure, invasive or noninvasive ventilation, shock and comorbidities. The study variable cut-off points and importance were obtained automatically. Logistic regression analysis with cross-validation was performed to develop the SIS score using the output coefficients. Accuracy and discrimination (AUC-ROC) were applied to evaluate SIS and RF. All analyses were performed using R (CRAN-R Project).Results: A total of 3959 patients were included. The mean age was 55 years (range 43-67), 60% were men, APACHE II 16 (12-21) and SOFA 5 (4-8), with ICU mortality 21.3%. Mechanical ventilation, shock, APACHE II, SOFA, acute renal failure and Gap-ICU were included in the 515. The latter was generated according to the ORs obtained by logistic regression, and showed an accuracy of 83% with an AUC-ROC of 82%, similar to RF (AUC-ROC 82%).Conclusions: The SIS score is easy to apply and shows adequate capacity to stratify the risk of ICU mortality. However, further studies are needed to validate the tool prospectively. (C) 2020 Elsevier Espana, S.L.U. y SEMICYUC. All rights reserved.