Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain.
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Rodríguez Alejandro, Ruiz-Botella Manuel, Martín-Loeches Ignacio, Jimenez Herrera María, Solé-Violan Jordi, Gómez Josep, Bodí María, Trefler Sandra, Papiol Elisabeth, Díaz Emili, Suberviola Borja, Vallverdu Montserrat, Mayor-Vázquez Eric, Albaya Moreno Antonio, Canabal Berlanga Alfonso, Sánchez Miguel, Del Valle Ortíz María, Ballesteros Juan Carlos, Martín Iglesias Lorena, Marín-Corral Judith, López Ramos Esther, Hidalgo Valverde Virginia, Vidaur Tello Loreto Vidaur, Sancho Chinesta Susana, Gonzáles de Molina Francisco Javier, Herrero García Sandra, Sena Pérez Carmen Carolina, Pozo Laderas Juan Carlos, Rodríguez García Raquel, Estella Angel, Ferrer Ricard, COVID-19 SEMICYUC Working Group
Аннотация
METHODS: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient's factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves.
RESULTS: The database included a total of 2022 patients (mean age 64 [IQR 5-71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10-17]) and SOFA score (5 [IQR 3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.
CONCLUSION: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a "one-size-fits-all" model in practice.