A differential therapeutic consideration for use of corticosteroids according to established COVID-19 clinical phenotypes in critically ill patients

Moreno, Gerard; Ruíz-Botella, Manuel; Martín‐Loeches, Ignacio; Álvarez, J. Gómez; Herrera, María; Bodí, M.; Arméstar, Fernándo; Parra, Asunción Marques; Estella, Ángel; Trefler, Sandra; García, Ruth Jorge; Payá, Josefa Murcia; Vidal, Pablo; Dı́az, Emili; Ferrer, Ruth González; Albaya-Moreno, Antonio; Socías-Crespi, Lorenzo; Goytisolo, Josep Maria Bonell; Chinesta, Susana Sancho; Loza, Ana; Espina, Lorena Forcelledo; Laderas, Juan Carlos Pozo; deAlba-Aparicio, María; Montori, Laura Sánchez; Perapoch, I. Vallverdú; Hidalgo, Virginia; Gutiérrez, Virginia Fraile; Ortega, Ana Mª Casamitjana; Serrano, Fátima Martín; Nieto, M.; Cortes, M. Blasco; Marín‐Corral, Judith; Solé‐Violán, Jordi; Rodríguez, Alejandro

doi:10.1016/j.medine.2021.10.016

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…La necesidad de VM (85,7%) es mayor que la descrita en los grandes estudios multicéntricos, que no alcanzan el 60% [12,16,10,17,18] pero comparable con la presentada en otros estudios unicéntricos de menor tamaño (con cifras que van desde el 73,2 al 94%) [11,[13][14][15]19]. Los datos publicados de las UCI de nuestro entorno muestran una puntuación en SOFA y una necesidad de ventilación en prono similar a la nuestra [18,20], sin embargo, nuestra mortalidad fue de 17,6% en UCI y del 21,6% en el hospital, sensiblemente menor al 30% descrito en los estudios multicéntricos de nuestro país [17,18]. La incidencia de coinfección bacteriana al ingreso en UCI en nuestra serie fue del 34,1% y el 96,1% de nuestros pacientes recibieron tratamiento antibiótico en las primeras 48 horas.…”

Section: Discussionunclassified

Bacterial coinfection in the critically-ill COVID-19 patient: incidence, impact and need for antimicrobial therapy

Vidal-Cortés,

Nieto del Olmo,

Tizón Varela

et al. 2023

Rev Esp Quimioter

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Objectives. To assess the frequency of bacterial coinfection upon ICU admission in SARS-CoV-2 pneumonia patients, its microbiology, and impact on prognosis.The secondary objective was to identify risk factors for coinfection on admission. Methods. Retrospective study, including patients with SARS-CoV-2 pneumonia admitted to the ICU.We defined bacterial coinfection by respiratory symptoms, radiological data, positive and clinically significant microbiological results in samples obtained in the first 48 h of admission and/or a determination of procalcitonin ≥ 0.5 ng/mL in the first 48 h.We evaluated demographic variables, comorbidities, SARS-CoV-2 infection data, severity scores, treatments received, need for respiratory support and outcomes (ICU and hospital mortality). Results. A total of 182 patients were analyzed, 62 (34.1%) with bacterial coinfection.The most frequent microbiology was S. pneumoniae and M. pneumoniae. 96.1% of the patients received antibiotic therapy on admission, 98.9% corticosteroids, 27.5% tocilizumab, and 7.7% remdesivir.85.7% required invasive mechanical ventilation.The SOFA score (OR: 1.315, 95% CI1.116-1.548) and the delay in ICU admission (OR: 0.899, 95% CI 0.831-0.972) were related to the risk of coinfection. Bacterial coinfection increases the risk of death in hospital (OR 2.283; 95% CI 1.011.5.151; p=0.047). Conclusions. Bacterial coinfection is common in COVID patients admitted to the ICU and increases the risk of death. It is not possible to identify with certainty, at the time of admission, which patients do not benefit from antibiotic treatment.

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Section: Discussionunclassified

Bacterial coinfection in the critically-ill COVID-19 patient: incidence, impact and need for antimicrobial therapy

Vidal-Cortés,

Nieto del Olmo,

Tizón Varela

et al. 2023

Rev Esp Quimioter

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“…Although corticosteroid therapy did not improve survival overall, corticosteroid therapy was associated with reduced 28 th day mortality in patients with hyperinflammation. 7 In a secondary analysis of multicenter observational study, Moreno et al 50 investigated relationship between clinical phenotypes (A, B and C) of critically-ill COVID-19 patients and response to corticosteroid therapy. Authors found that only patients with phenotype C (with a higher inflammatory status) benefit from corticosteroid therapy consistent with the study by Chen et al 7…”

Section: Discussionmentioning

confidence: 99%

Assessment of Admission COVID-19 Associated Hyperinflammation Syndrome Score in Critically-Ill COVID-19 Patients

Yıldırım

Halaçlı

Yüce

et al. 2022

J Intensive Care Med

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Purpose We aimed to evaluate the relation between admission COVID-19 associated hyperinflammatory syndrome (cHIS) score and intensive care unit (ICU) outcomes. Materials and Methods Patients with laboratory confirmed COVID-19 admitted to our ICU between 20th March 2020-15th June 2021 were included. Patients who received immunomodulatory treatment except corticosteroids were excluded. Main outcomes were ICU mortality and invasive mechanical ventilation (IMV) requirement after ICU admission. Results Three hundred and seventy patients with a median (IQR) age of 66 (56-77) were analyzed. Median admission cHIS score was 3 (2-4). A cHIS score ≥3 was found to be associated with ICU mortality (sensitivity = 0.63, specificity = 0.50; p < 0.01) and IMV requirement after ICU admission (sensitivity = 0.61, specificity = 0.51; p < 0.01). Patients with an admission cHIS score ≥3 (n = 199) had worse median admission APACHEII, SOFA scores and PaO2/FiO2 ratio than others (n = 171) (p < 0.01). IMV requirement after ICU admission (38.5% vs 26.1%;p = 0.03), ICU (36.2% vs 25.1%;p = 0.02), hospital (39.1% vs 26.9%;p = 0.01) and 28th day (28.1% vs 19.1%;p = 0.04) mortality were higher in patients with admission cHIS score ≥3 than others (p < 0.01). Age <65 years, malignancy and higher admission SOFA score were independent variables associated with admission cHIS score ≥3. Conclusion Critically-ill COVID-19 patients with admission cHIS score ≥3 have worse disease severity and outcomes than other patients.

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Reliability and reproducibility of clinical phenotypes developed during the first wave of COVID-19: A validation study in critically ill patients from the second and third wave

Rodríguez¹,

Gómez

Franquet

et al. 2023

Preprint

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Background: During the first wave of the COVID-19 pandemic, different clinical phenotypes were published. However, none of them have been validated in subsequent waves, so their current validity is unknown. The aim of the study is to validate the unsupervised cluster model developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves. Methods: Retrospective, multicentre, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 74 Intensive Care Units (ICU) in Spain. To validate our original phenotypes model, we assigned a phenotype to each patient of the validation cohort using the same medoids, the same number of clusters (n= 3), the same number of variables (n= 25) and the same discretisation used in the development cohort. The performance of the classification was determined by Silhouette analysis and general linear modelling. The prognostic models were validated, and their performance was measured using accuracy test and area under curve (AUC)ROC. Results: The database included a total of 2,033 patients (mean age 63[53-92] years, 1643(70.5%) male, median APACHE II score (12[9-16]) and SOFA score (4[3-6]) points. The ICU mortality rate was 27.2%. Although the application of unsupervised cluster analysis classified patients in the validation population into 3 clinical phenotypes. Phenotype A (n=1,206 patients, 59.3%), phenotype B (n=618 patients, 30.4%) and phenotype C (n=506 patients, 24.3%), the characteristics of patients within each phenotype were significantly different from the original population. Furthermore, the silhouette coefficients were close to or below zero and the inclusion of phenotype classification in a regression model did not improve the model performance (accuracy =0.78, AUC=0.78) with respect to a standard model (accuracy = 0.79, AUC=0.79) or even worsened when the model was applied to patients within each phenotype (accuracy = 0.80, AUC 0.77 for Phenotype A, accuracy=0.73, AUC= 0.67 for phenotype B and accuracy= 0.66 , AUC= 0.76 for phenotype C ) 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. Trial Registration: The study was retrospectively registered (NCT 04948242) on June 30, 2021

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A differential therapeutic consideration for use of corticosteroids according to established COVID-19 clinical phenotypes in critically ill patients

Cited by 5 publications

References 44 publications

Bacterial coinfection in the critically-ill COVID-19 patient: incidence, impact and need for antimicrobial therapy

Bacterial coinfection in the critically-ill COVID-19 patient: incidence, impact and need for antimicrobial therapy

Assessment of Admission COVID-19 Associated Hyperinflammation Syndrome Score in Critically-Ill COVID-19 Patients

Reliability and reproducibility of clinical phenotypes developed during the first wave of COVID-19: A validation study in critically ill patients from the second and third wave

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