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 SIS. 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.