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Vol. 45. Issue 2.
Pages 69-79 (March 2021)
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Vol. 45. Issue 2.
Pages 69-79 (March 2021)
DOI: 10.1016/j.medine.2020.05.009
Spanish Influenza Score (SIS): Usefulness of machine learning in the development of an early mortality prediction score in severe influenza
Spanish Influenza Score (SIS): utilidad del Machine Learning en el desarrollo de una escala temprana de predicción de mortalidad en la gripe grave
Spanish Working Group in Severe Influenza A (GETGAG) of the Sociedad Española de Medicina Intensiva Crítica y Unidades Coronarias (SEMICYUC)
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Table 1. General characteristics of the 3959 patients included in the present analysis. The variables are those considered upon admission to the ICU and for the first 24 h of stay. The results are expressed as the number of patients (n) and percentage (%) or median and interquartile range (IQR), as applicable. COPD: chronic obstructive pulmonary disease; APACHE II: Acute Physiology And Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment; Gap hospital: time from symptoms onset to admission to hospital; Gap diagnosis: time from admission to hospital to diagnosis; Gap ICU: time from admission to hospital to admission to the ICU; vaccinated: patients that received influenza vaccination; BMI: body mass index).
Table 2. Variables independently associated to in-ICU mortality (multivariate analysis) (APACHE II: Acute Physiology And Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment; Gap ICU: time from admission to hospital to admission to the ICU).
Table 3. Spanish Influenza Score (SIS) derived from the ORs of the logistic regression analysis (ARF: acute renal failure; IMV: invasive mechanical ventilation; APACHE II: Acute Physiology and Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment; Gap ICU: time from admission to hospital to admission to the ICU).
Table 4. Predictive values of the Spanish Influenza Score (SIS) and of the random forest (RF) model for the 3959 patients included in the study.
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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 against the APACHE II, SOFA and Random Forest (RF).


Sub-analysis from the GETGAG / SEMICYUC database


Intensive Care Medicine.


Patients admitted to 184 Spanish ICUs (2009–2018) with influenza infection.




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, APACHE, SOFA and RF. All analyses were performed using R (CRAN-R Project).


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%, which is superior to APACHE (AUC-ROC 67%) and SOFA (AUC-ROC 71%), but similar to RF (AUC-ROC 82%).


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.

Severe influenza
Machine learning

Desarrollar una escala predictiva de mortalidad (SIS) en pacientes con gripe grave considerando las variables al ingreso a UCI y comparar su eficacia respecto del APACHE II, SOFA y un modelo Random Forrest (RF).


Sub-análisis de base de datos GETGAG/SEMICYUC.


Medicina Intensiva.




Pacientes ingresados en 184 UCI españolas (2009–2018) con infección por gripe.


Demográficas, nivel de gravedad, tiempo síntomas hasta el ingreso al hospital (Gap-H) o desde hospital a UCI (Gap-UCI), o al diagnóstico (Gap-Dg), vacunación, cuadrantes infiltrados, insuficiencia renal, ventilación no-invasiva o invasiva (VM), shock, y comorbilidades. Los puntos de corte y la importancia de las variables se obtuvieron de forma automática. Se realizó validación cruzada y regresión logística a partir de la cual se desarrolló la puntuación SIS. Se aplicó la puntuación y se calculó la exactitud y la discriminación (AUC-ROC) así como para APACHE, SOFA y RF. El análisis se realizó mediante CRAN-R Project.


Se incluyeron 3959 pacientes, edad 55 (43−67) años, 60% hombres, APACHE II de 16(12−21) y SOFA 5(4−8) puntos y una mortalidad del 21,3%. VM, shock, APACHEII, SOFA, insuficiencia renal aguda y Gap-UCI fueron incluidas en SIS. A partir de los OR se construyó el SIS que demostró una exactitud del 83% y un AUC-ROC del 82%, superior al APACHE (AUCROC 67%) y SOFA (AUC-ROC 71%) y similar al RF (AUC-ROC 82%).


La escala SIS de fácil aplicación, ha demostrado con adecuada capacidad de estratificación del riesgo de mortalidad en la UCI. Sin embargo, estos resultados deberán ser validados prospectivamente.

Palabras clave:
Gripe grave
Machine learning


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