Medicina intensiva · 2024
Applicability of an unsupervised cluster model developed on first wave COVID-19 patients in second/third wave critically ill patients.
СтатьяНаблюдательное исследованиеМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Rodríguez Alejandro, Gómez Josep, Franquet Álvaro, Trefler Sandra, Díaz Emili, Sole-Violán Jordi, Zaragoza Rafael, Papiol Elisabeth, Suberviola Borja, Vallverdú Montserrat, Jimenez-Herrera María, Albaya-Moreno Antonio, Canabal Berlanga Alfonso, Del Valle Ortíz María, Carlos Ballesteros Juan, López Amor Lucía, Sancho Chinesta Susana, de Alba-Aparicio Maria, Estella Angel, Martín-Loeches Ignacio, Bodi María, COVID-19 SEMICYUC Working group
Аннотация
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.
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.