Methodology of a Large Multicenter Observational Study of Patients with COVID-19 in Spanish Intensive Care Units

Torres, Antoni; Motos, Anna; Ceccato, Adrián; Bermejo-Martín, Jesús F.; Gonzalo‐Calvo, David de; Pérez, Raquel Bosó; Barroso, Marta; Pascual, Ion Zubizarreta; González, Jessica; Fernández-Barat, Laia; Ferrer, Ricard; Riera, Jordi; Garcia-Gasulla, Darío; Peñuelas, Óscar; Lorente, José A.; Almansa, Raquel; Menéndez, Rosário; Kiarostami, Kasra; Canseco, Joan; Villar, Rosario Amaya; Añón, J.M.; Mariño, Ana Balan; Barberà, Carme; Barberán, José; Ortíz, Aaron Blandino; Boado, María Victoria; Bustamante-Munguira, Elena; Caballero, Jesús; Cantón-Bulnes, María Luisa; Pérez, Cristina Carbajales; Carbonell, Nieves; Catalán-González, Mercedes; Frutos, Raul de; Franco, Nieves; Galbán, Cristóbal; Gumucio-Sanguino, Víctor D.; Torre, María del Carmen de la; Dı́az, Emili; Estella, Ángel; Gallego, Elena; Garmendia, J.L. García; Gómez, José Manuel Naranjo; Huerta, Arturo; García, Ruth Noemí Jorge; Loza-Vázquez, Ana; Marín‐Corral, Judith; Delgado, M.C. Martín; Gándara, Amalia Martínez de la; Varela, Ignacio Martínez; Messa, Juan López; Albaiceta, Guillermo M.; Nieto, Maite; Novo, Mariana Andrea; Peñasco, Yhivian; Pérez-García, Felipe; Laderas, Juan Carlos Pozo; Ricart, Pilar; Sagredo, Víctor; Sánchez-Miralles, Ángel; Chinesta, Susana Sancho; Serra-Fortuny, Mireia; Socías, Lorenzo; Solé‐Violán, Jordi; Suárez-Sipmann, Fernando; Lomas, Luis Tamayo; Trenado, José; Úbeda, Alejandro; Valdivia, Luis Jorge; Vidal, Pablo; Barbé, Ferrán

doi:10.1016/j.arbres.2022.03.010

Cited by 11 publications

(7 citation statements)

References 27 publications

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“…The strengths of the study are the use of a multicenter design including patients from 19 hospitals from different geographical locations, the use of a priori sample size calculation, the high number of patients analyzed, the rigorous control of data quality and the evaluation of the miRNA classifier in conjunction with electronic health history and contemporaneous tests. The study population is a subcohort of critically ill COVID-19 patients collected in the same time frame as the nationwide cohort CIBERESUCICOVID [ 10 ]; and therefore, it has similar characteristics being females (29.6%), with a median age of 63 [54–71] and hypertension (49.7%), obesity (36.3%) and diabetes mellitus (24.6%) as the most common comorbidities (Supplemental Table S4 ; Additional File 2). In addition, the subcohort is similar to populations described in other studies, such as Dongelmans et al [ 28 ] and Carbonell et al [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

“…This is a substudy of the CIBERESUCICOVID study registered at www.clinicaltrials.gov with the identification NCT04457505. CIBERESUCICOVID is a multicenter, observational, prospective/retrospective cohort study that enrolled critically ill COVID-19 patients admitted to the ICUs of 55 Spanish hospitals [ 10 ]. CIBERESUCICOVID started in May 2020 by collecting the retrospective data of patients admitted to participating ICUs (from February 2020) and continued prospectively until February 2021.…”

Section: Methodsmentioning

confidence: 99%

“…CIBERESUCICOVID started in May 2020 by collecting the retrospective data of patients admitted to participating ICUs (from February 2020) and continued prospectively until February 2021. After enrollment, comprehensive demographic, clinical, pharmacological and laboratory data were exhaustively collected at hospital and ICU admission, as previously described in Torres et al [ 10 ]. The pharmacologic treatments administered and interventions performed during hospital admission until either discharge from hospital or death were also collected.…”

Section: Methodsmentioning

confidence: 99%

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A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study

de Gonzalo-Calvo,

Molinero,

Benítez

et al. 2023

Respir Res

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Background The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU. Methods This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group. Results Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways. Conclusions A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study

de Gonzalo-Calvo,

Molinero,

Benítez

et al. 2023

Respir Res

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“…A. Ceccato (Barcelona, Spain) showed from a multicentre national cohort [ 51 ] using a k-prototype algorithm that ARDS patients can be clustered into two groups (cluster 1 having less organ failure and higher P aO 2 / F IO 2 ratio than cluster 2) that have differing prognoses [ 52 ].…”

Section: Hot Topics In Intensive Care Medicinementioning

confidence: 99%

ERS International Congress 2023: highlights from the Respiratory Intensive Care Assembly

Bianquis,

Leiva Agüero,

Cantero

et al. 2023

ERJ Open Res

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Early Career Members of Assembly 2 (Respiratory Intensive Care) attended the 2023 European Respiratory Society Annual Congress in Milan. The conference covered acute and chronic respiratory failure. Sessions of interest to our Assembly members and to those interested in respiratory critical care are summarized in this article.Latest updates in respiratory intensive care, in particular ARDS and mechanical ventilation, presented at the 2023 European Respiratory Society Annual Congress in Milan and summarized by the Early Career Members of Assembly 2 (Respiratory Intensive Care).

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“…Critically ill COVID-19 patients present greater need for life-sustaining treatments, high mortality, and prolonged length of intensive care unit (ICU) stay [ 1 ]. In-hospital mortality can reach 31% and is expected that as much as 76% of patients will require invasive mechanical ventilation (IMV) [ 2 ]. Worst long-term sequelae are also expected for ICU COVID-19 survivors, although the one-year mortality following ICU discharge appears to be influenced more by patient’s comorbidities rather than the severity of COVID-19 pneumonia itself [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Predictors of early and long-term mortality after ICU discharge in critically ill COVID-19 patients: A prospective cohort study

Santos,

Pereira,

Cuboia

et al. 2023

PLoS ONE

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Background To mitigate mortality among critically ill COVID-19 patients, both during their Intensive Care Unit (ICU) stay and following ICU discharge, it is crucial to measure its frequency, identify predictors and to establish an appropriate post-ICU follow-up strategy. Methods In this multicentre, prospective cohort study, we included 586 critically ill COVID-19 patients. Results We observed an overall ICU mortality of 20.1% [95%CI: 17.1% to 23.6%] (118/586) and an overall hospital mortality of 25.4% [95%CI: 22.1% to 29.1%] (149/586). For ICU survivors, 30 days (early) post-ICU mortality was 5.3% [95%CI: 3.6% to 7.8%] (25/468) and one-year (late) post-ICU mortality was 7.9% [95%CI: 5.8% to 10.8%] (37/468). Pre-existing conditions/comorbidities were identified as the main independent predictors of mortality after ICU discharge: hypertension and heart failure were independent predictors of early mortality; and hypertension, chronic kidney disease, chronic obstructive pulmonary disease and cancer were independent predictors of late mortality. Conclusion Early and late post-ICU mortality exhibited an initial surge (in the first 30 days post-ICU) followed by a subsequent decline over time. Close monitoring of critically ill COVID-19 post-ICU survivors, especially those with pre-existing conditions, is crucial to prevent adverse outcomes, reduce mortality and to establish an appropriate follow-up strategy.

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Methodology of a Large Multicenter Observational Study of Patients with COVID-19 in Spanish Intensive Care Units

Cited by 11 publications

References 27 publications

A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study

A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study

ERS International Congress 2023: highlights from the Respiratory Intensive Care Assembly

Predictors of early and long-term mortality after ICU discharge in critically ill COVID-19 patients: A prospective cohort study

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