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Vol. 48. Issue 6.
Pages 326-340 (June 2024)
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Vol. 48. Issue 6.
Pages 326-340 (June 2024)
Original article
Applicability of an unsupervised cluster model developed on first wave COVID-19 patients in second/third wave critically ill patients
Aplicabilidad de un modelo no supervisado de conglomerados desarrollado en pacientes COVID-19 de primera oleada en pacientes críticos de segunda/tercera oleada
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Alejandro Rodrígueza,b,
,1
, Josep Gómezc,1, Álvaro Franquetc, Sandra Treflera, Emili Díazd, Jordi Sole-Violáne, Rafael Zaragozaf, Elisabeth Papiolg, Borja Suberviolah, Montserrat Vallverdúi, María Jimenez-Herreraj, Antonio Albaya-Morenok, Alfonso Canabal Berlangal, María del Valle Ortízm, Juan Carlos Ballesterosn, Lucía López Amoro, Susana Sancho Chinestap, Maria de Alba-Aparicioq, Angel Estellar, Ignacio Martín-Loechess..., María Bodia,b, on behalf of COVID-19 SEMICYUC Working group 2Ver más
a Critical Care Department — Hospital Universitari de Tarragona Joan XXIII, Tarragona, Spain
b Universidad Rovira & Virgili/Institut d’Investigació Sanitaria Pere Virigili/CIBERES, Tarragona, Spain
c Technical Secretary — Hospital Universitari de Tarragona Joan XXIII, Tarragona, Spain
d Critical Care Department — Hospital Parc Tauli, Sabadell, Spain
e Critical Care Department — Hospital Universitario Dr. Negrin/Universidad Fernando Pessoa, Las Palmas de Gran Canaria, Spain
f Critical Care Department — Hospital Dr. Peset, Valencia, Spain
g Critical Care Department — Hospital Universitari Vall d’Hebron, Barcelona, Spain
h Critical Care Department — Hospital Universitario Marqués de Valdecilla, Santander, Spain
i Critical Care Department — Hospital Universitari Arnau de Vilanova, Lleida, Spain
j Dean Nursing Faculty, Universitat Rovira i Virgili, Tarragona, Spain
k Critical Care Department — Hospital Universitario de Guadalajara, Guadalajara, Spain
l Critical Care Department — Hospital de La Princesa, Madrid, Spain
m Critical Care Department — Hospital Universitario de Burgos, Burgos, Spain
n Critical Care Department — Hospital Clínico de Salamanca, Salamanca, Spain
o Critical Care Department - Hospital Universitario Central de Asturias, Oviedo, Spain
p Critical Care Department — Hospital Universitario y Politécnico La Fe, Valencia, Spain
q Critical Care Department — Hospital Universitario Reina Sofía, Córdoba, Spain
r Critical Care Department — Hospital Universitario de Jerez, Jerez de la Frontera, Spain
s Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St. James’s Hospital, Dublin, Ireland
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Tables (3)
Table 1. Patient characteristics of the original and validation cohort and within each phenotype obtained by applying the original model in the validation population.
Table 2. Characteristics of 2330 COVID-19 critically ill patients in the validation group included in the study. The data are shown with the discretisation of the variables used to carry out the study.
Table 3. Models (GLM) performance comparison.
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Additional material (2)
Abstract
Objective

To validate the unsupervised cluster model (USCM) developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves.

Design

Observational, retrospective, multicentre study.

Setting

Intensive Care Unit (ICU).

Patients

Adult patients admitted with COVID-19 and respiratory failure during the second and third pandemic waves.

Interventions

None.

Main variables of interest

Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. To validate our original USCM, we assigned a phenotype to each patient of the validation cohort. The performance of the classification was determined by Silhouette coefficient (SC) and general linear modelling. In a post-hoc analysis we developed and validated a USCM specific to the validation set. The model’s performance was measured using accuracy test and area under curve (AUC) ROC.

Results

A total of 2330 patients (mean age 63 [53–82] years, 1643 (70.5%) male, median APACHE II score (12 [9–16]) and SOFA score (4 [3–6]) were included. The ICU mortality was 27.2%. The USCM classified patients into 3 clinical phenotypes: A (n = 1206 patients, 51.8%); B (n = 618 patients, 26.5%), and C (n = 506 patients, 21.7%). The characteristics of patients within each phenotype were significantly different from the original population. The SC was −0.007 and the inclusion of phenotype classification in a regression model did not improve the model performance (0.79 and 0.78 ROC for original and validation model). The post-hoc model performed better than the validation model (SC −0.08).

Conclusion

Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation.

Keywords:
Severe SARS-CoV-2 infection
Phenotypes
Risk factors
Prognosis
Machine Learning
Validation
Resumen
Objetivo

Validar el modelo de conglomerados no supervisado (USCM) desarrollado durante la primera ola pandémica en una cohorte de pacientes críticos de la segunda y tercera ola.

Diseño

Estudio observacional, retrospectivo y multicéntrico.

Entorno

Unidad de Cuidados Intensivos (UCI).

Pacientes

Pacientes adultos ingresados con COVID-19 e insuficiencia respiratoria durante la segunda/tercera ola pandémica.

Intervenciones

Ninguna.

Variables de interés principals

Se recogieron características demográficas y clínicas, comorbilidades, laboratorio y evolución en UCI. Para validar el USCM original, asignamos un fenotipo a cada paciente de la cohorte de validación. El rendimiento se determinó mediante análisis de silueta (AS) y modelización lineal general. En un análisis post-hoc desarrollamos y validamos un USCM específico para el conjunto de validación. El rendimiento del modelo se midió mediante la prueba de exactitud y el área bajo la curva (AUC) ROC.

Resultados

Se incluyeron 2033 pacientes (edad media 63 [53–-82] años, 1643 (70,5%) varones, APACHE II (12 [9–16]) y SOFA (4 [3–6]). La mortalidad en UCI fue del 27,2%. El USCM clasificó a los pacientes en 3 fenotipos clínicos: A (n = 1206 pacientes, 51.8%); B (n = 618 pacientes, 26.5%), and C (n = 506 pacientes, 21.7%). Las características de los pacientes dentro de cada fenotipo fueron significativamente diferentes de la población original. El AS fue −0.007 y la inclusión de la clasificación por fenotipos en un modelo de regresión no mejoró el rendimiento del modelo (ROC 0.79 y 0.78 para el modelo original y de validación). El modelo post-hoc obtuvo mejores resultados que el modelo de validación (AS −0.08).

Conclusiones

Los modelos desarrollados durante la primera oleada pandémica no pueden aplicarse con un rendimiento adecuado a los pacientes ingresados en oleadas posteriores sin una validación previa.

Palabras clave:
Infección grave por SARS-CoV-2
Fenotipos
Factores de riesgo
Pronóstico
Aprendizaje automático
Validación

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