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

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.post-print3224 K

Identification of circulating microRNA profiles associated with pulmonary function and radiologic features in survivors of SARS-CoV-2-induced ARDS

ABSTRACT There is a limited understanding of the pathophysiology of postacute pulmonary sequelae in severe COVID-19. The aim of current study was to define the circulating microRNA (miRNA) profiles associated with pulmonary function and radiologic features in survivors of SARS-CoV-2-induced ARDS. The study included patients who developed ARDS secondary to SARS-CoV-2 infection (n=167) and a group of infected patients who did not develop ARDS (n=33). Patients were evaluated 3 months after hospital discharge. The follow-up included a complete pulmonary evaluation and chest computed tomography. Plasma miRNA profiling was performed using RT-qPCR. Random forest was used to construct miRNA signatures associated with lung diffusing capacity for carbon monoxide (DLCO) and total severity score (TSS). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted. DLCO<80% predicted was observed in 81.8% of the patients. TSS showed a median [P25;P75] of 5 [2;8]. The miRNA model associated with DLCO comprised miR-17-5p, miR-27a-3p, miR-126-3p, miR-146a-5p and miR-495-3p. Concerning radiologic features, a miRNA signature composed by miR-9-5p, miR-21-5p, miR-24-3p and miR-221-3p correlated with TSS values. These associations were not observed in the non-ARDS group. KEGG pathway and GO enrichment analyses provided evidence of molecular mechanisms related not only to profibrotic or anti-inflammatory states but also to cell death, immune response, hypoxia, vascularization, coagulation and viral infection. In conclusion, diffusing capacity and radiological features in survivors from SARS-CoV-2-induced ARDS are associated with specific miRNA profiles. These findings provide novel insights into the possible molecular pathways underlying the pathogenesis of pulmonary sequelae. Trial registration: ClinicalTrials.gov identifier: NCT04457505.. Trial registration: ISRCTN.org identifier: ISRCTN16865246..This work is supported by Instituto de Salud Carlos III (COV20/00110), co-funded by European Regional Development Fund (ERDF)/“A way to make Europe”. CIBERES is an initiative of the Instituto de Salud Carlos III. CIBERES is an initiative of the Instituto de Salud Carlos III. Suported by: Programa de donaciones "estar preparados" UNESPA (Madrid, Spain) and Fundación Francisco Soria Melguizo (Madrid, Spain).. Finançat per La Fundació La Marató de TV3, projecte amb codi 202108-30/-31. COVIDPONENT is funded by Institut Català de la Salut and Gestió de Serveis Sanitaris. MM is the recipient of a predoctoral fellowship (PFIS: FI21/00187) from Instituto de Salud Carlos III. MCGH is the recipient of a predoctoral fellowship from “University of Lleida”. DdGC has received financial support from Instituto de Salud Carlos III (Miguel Servet 2020: CP20/00041), co-funded by the European Social Fund (ESF)/“Investing in your future”. ENL and GL were funded by COVID1005 and ACT210085 from National Agency of Investigation & Development (ANID), Chile."Article signat per 27 autors/es: María C. García-Hidalgo, Jessica González, Iván D. Benítez, Paola Carmona, Sally Santisteve, Manel Pérez-Pons, Anna Moncusí-Moix, Clara Gort-Paniello, Fátima Rodríguez-Jara, Marta Molinero, Thalia Belmonte, Gerard Torres, Gonzalo Labarca, Estefania Nova-Lamperti, Jesús Caballero, Jesús F. Bermejo-Martin, Adrián Ceccato, Laia Fernández-Barat, Ricard Ferrer, Dario Garcia-Gasulla, Rosario Menéndez, Ana Motos ,Oscar Peñuelas, Jordi Riera, Antoni Torres, Ferran Barbé, David de Gonzalo-Calvo & on behalf of the CIBERESUCICOVID Project (COV20/00110 ISCIII)"Postprint (published version

Bronchial Aspirate-Based Profiling Identifies MicroRNA Signatures Associated With COVID-19 and Fatal Disease in Critically Ill Patients

Background: The pathophysiology of COVID-19-related critical illness is not completely understood. Here, we analyzed the microRNA (miRNA) profile of bronchial aspirate (BAS) samples from COVID-19 and non-COVID-19 patients admitted to the ICU to identify prognostic biomarkers of fatal outcomes and to define molecular pathways involved in the disease and adverse events. Methods: Two patient populations were included (n = 89): (i) a study population composed of critically ill COVID-19 and non-COVID-19 patients; (ii) a prospective study cohort composed of COVID-19 survivors and non-survivors among patients assisted by invasive mechanical ventilation (IMV). BAS samples were obtained by bronchoaspiration during the ICU stay. The miRNA profile was analyzed using RT-qPCR. Detailed biomarker and bioinformatics analyses were performed. Results: The deregulation in five miRNA ratios (miR-122-5p/miR-199a-5p, miR-125a-5p/miR-133a-3p, miR-155-5p/miR-486-5p, miR-214-3p/miR-222-3p, and miR-221-3p/miR-27a-3p) was observed when COVID-19 and non-COVID-19 patients were compared. In addition, five miRNA ratios segregated between ICU survivors and nonsurvivors (miR-1-3p/miR-124-3p, miR-125b-5p/miR-34a-5p, miR-126-3p/miR-16-5p, miR-199a-5p/miR-9-5p, and miR-221-3p/miR-491-5p). Through multivariable analysis, we constructed a miRNA ratio-based prediction model for ICU mortality that optimized the best combination of miRNA ratios (miR-125b-5p/miR-34a-5p, miR-199a-5p/miR-9-5p, and miR-221-3p/miR-491-5p). The model (AUC 0.85) and the miR-199a-5p/miR-9-5p ratio (AUC 0.80) showed an optimal discrimination value and outperformed the best clinical predictor for ICU mortality (days from first symptoms to IMV initiation, AUC 0.73). The survival analysis confirmed the usefulness of the miRNA ratio model and the individual ratio to identify patients at high risk of fatal outcomes following IMV initiation. Functional enrichment analyses identified pathological mechanisms implicated in fibrosis, coagulation, viral infections, immune responses and inflammation. Conclusions: COVID-19 induces a specific miRNA signature in BAS from critically ill patients. In addition, specific miRNA ratios in BAS samples hold individual and collective potential to improve risk-based patient stratification following IMV initiation in COVID-19-related critical illness. The biological role of the host miRNA profiles may allow a better understanding of the different pathological axes of the disease.We want particularly to acknowledge the patients, Biobank IdISBa and CIBERES Pulmonary Biobank Consortium (PT17/0015/0001), a member of the Spanish National Biobanks Network financed by the Carlos III Health Institute, with the participation of the Units of Intensive Care, Clinical Analysis and Pulmonology of Hospital Universitario Son Espases and Hospital Son Llatzer for their collaboration. This work was also supported by IRBLleida Biobank (B.0000682) and Plataforma Biobancos PT17/0015/0027/.Peer Reviewed"Article signat per 25 autors/es: Marta Molinero, Iván D. Benítez, Jessica González, Clara Gort-Paniello, Anna Moncusí-Moix, Fátima Rodríguez-Jara, María C. García-Hidalgo, Gerard Torres, J. J. Vengoechea, Silvia Gómez, Ramón Cabo, Jesús Caballero, Jesús F. Bermejo-Martin, Adrián Ceccato, Laia Fernández-Barat, Ricard Ferrer, Dario Garcia-Gasulla, Rosario Menéndez, Ana Motos, Oscar Peñuelas, Jordi Riera, Antoni Torres, Ferran Barbé and David de Gonzalo-Calvo* on behalf of the CIBERESUCICOVID Project (COV20/00110 ISCIII)"Postprint (published version

Circulating microRNA profiles predict the severity of COVID-19 in hospitalized patients

We aimed to examine the circulating microRNA (miRNA) profile of hospitalized COVID-19 patients and evaluate its potential as a source of biomarkers for the management of the disease. This was an observational and multicenter study that included 84 patients with a positive nasopharyngeal swab Polymerase chain reaction (PCR) test for SARS-CoV-2 recruited during the first pandemic wave in Spain (March-June 2020). Patients were stratified according to disease severity: hospitalized patients admitted to the clinical wards without requiring critical care and patients admitted to the intensive care unit (ICU). An additional study was completed including ICU nonsurvivors and survivors. Plasma miRNA profiling was performed using reverse transcription polymerase quantitative chain reaction (RT-qPCR). Predictive models were constructed using least absolute shrinkage and selection operator (LASSO) regression. Ten circulating miRNAs were dysregulated in ICU patients compared to ward patients. LASSO analysis identified a signature of three miRNAs (miR-148a-3p, miR-451a and miR-486-5p) that distinguishes between ICU and ward patients [AUC (95% CI) = 0.89 (0.81-0.97)]. Among critically ill patients, six miRNAs were downregulated between nonsurvivors and survivors. A signature based on two miRNAs (miR-192-5p and miR-323a-3p) differentiated ICU nonsurvivors from survivors [AUC (95% CI) = 0.80 (0.64–0.96)]. The discriminatory potential of the signature was higher than that observed for laboratory parameters such as leukocyte counts, C-reactive protein (CRP) or D-dimer [maximum AUC (95% CI) for these variables = 0.73 (0.55–0.92)]. miRNA levels were correlated with the duration of ICU stay. Specific circulating miRNA profiles are associated with the severity of COVID-19. Plasma miRNA signatures emerge as a novel tool to assist in the early prediction of vital status deterioration among ICU patients.Supported by ISCIII (CIBERESUCICOVID, COV20/00110), co-funded by ERDF, “Una manera de hacer Europa”. DdGC acknowledges receiving financial support from Instituto de Salud Carlos III (ISCIII), Miguel Servet 2020 (CP20/00041), co-funded by the European Social Fund (ESF), “Investing in your future”. LP is the recipient of a predoctoral fellowship from the Ministry of Universities of Spain (FPU19/03526). This work partially supported by IRBLleida Biobank (B.0000682) and “Plataforma Biobancos PT17/0015/0027/”.Peer Reviewed"Article signat per 33 autors/es: David de Gonzalo-Calvo, Iván D. Benítez, Lucía Pinilla, Amara Carratalá, Anna Moncusí-Moix, Clara Gort-Paniello, Marta Molinero, Jessica González, Gerard Torres, María Bernal, Silvia Pico, Raquel Almansa, Noelia Jorge, Alicia Ortega, Elena Bustamante-Munguira, José Manuel Gómez, Milagros González-Rivera, Dariela Micheloud, Pablo Ryan, Amalia Martinez, Luis Tamayo, César Aldecoa, Ricard Ferrer, Adrián Ceccato, Laia Fernández-Barat, Ana Motos, Jordi Riera, Rosario Menéndez, Dario Garcia-Gasulla, Oscar Peñuelas, Antoni Torres, Jesús F. Bermejo-Martin, Ferran Barbé on behalf of the Ciberesucicovid Project (Cov20/00110, Isciii)"Postprint (author's final draft

One year overview and follow-up in a post-COVID consultation of critically ill patients

The long-term clinical management and evolution of a cohort of critical COVID-19 survivors have not been described in detail. We report a prospective observational study of COVID-19 patients admitted to the ICU between March and August 2020. The follow-up in a post-COVID consultation comprised symptoms, pulmonary function tests, the 6-minute walking test (6MWT), and chest computed tomography (CT). Additionally, questionnaires to evaluate the prevalence of post-COVID-19 syndrome were administered at 1 year. A total of 181 patients were admitted to the ICU during the study period. They were middle-aged (median [IQR] of 61 [52;67]) and male (66.9%), with a median ICU stay of 9 (5–24.2) days. 20% died in the hospital, and 39 were not able to be included. A cohort of 105 patients initiated the follow-up. At 1 year, 32.2% persisted with respiratory alterations and needed to continue the follow-up. Ten percent still had moderate/severe lung diffusion (DLCO) involvement (<60%), and 53.7% had a fibrotic pattern on CT. Moreover, patients had a mean (SD) number of symptoms of 5.7 ± 4.6, and 61.3% met the criteria for post-COVID syndrome at 1 year. During the follow-up, 46 patients were discharged, and 16 were transferred to other consultations. Other conditions, such as emphysema (21.6%), COPD (8.2%), severe neurocognitive disorders (4.1%), and lung cancer (1%) were identified. A high use of health care resources is observed in the first year. In conclusion, one-third of critically ill COVID-19 patients need to continue follow-up beyond 1 year, due to abnormalities on DLCO, chest CT, or persistent symptoms.This study was supported in part by ISCIII (CIBERESUCICOVID, COV20/00110), co-funded by ERDF, “Una manera de hacer Europa,” donation program “Estar Preparados,” UNESPA, Madrid, Spain and Fundación Soria Melguizo (Madrid, Spain). DG-C had received financial support from Instituto de Salud Carlos III (Miguel Servet 2020: CP20/00041), co-funded by the European Social Fund (ESF)/“Investing in your future.” JB acknowledged receiving financial support from Instituto de Salud Carlos III (ISCIII; Miguel Servet 2019: CP19/00108), co-funded by the European Social Fund (ESF), “Investing in your future.”Peer ReviewedArticle signat per 29 autors/es: Jessica González (1,2,3,4), María Zuil (1,2,3,4), Iván D. Benítez (2,3,4), David de Gonzalo-Calvo (2,3,4), María Aguilar (1,2), Sally Santisteve (1,2,3,4), Rafaela Vaca (1,2), Olga Minguez (1,2), Faty Seck (1,2), Gerard Torres (1,2,3,4), Jordi de Batlle (2,3,4), Silvia Gómez (1,2,3,4), Silvia Barril (1,2,3,4), Anna Moncusí-Moix (2,3,4), Aida Monge (1,2,3,4), Clara Gort-Paniello (2,3,4), Ricard Ferrer (4,5), Adrián Ceccato (4), Laia Fernández (4,6), Ana Motos (4,6), Jordi Riera (4,5), Rosario Menéndez (4,7), Darío Garcia-Gasulla (8), Oscar Peñuelas (4,9), Gonzalo Labarca (10,11), Jesús Caballero (12), Carme Barberà (13), Antoni Torres (4,6) and Ferran Barbé (1,2,3,4) * on behalf of the CIBERESUCICOVID Project (COV20/00110, ISCIII) // (1) Department of Pulmonary, Hospital Universitari Arnau de Vilanova and Santa Maria, Lleida, Spain, (2) Translational Research in Respiratory Medicine Group, Lleida, Spain, (3) Lleida Biomedical Research Institute, Lleida, Spain, (4) Centro de Investigación Biomédica en Red (CIBER) of Respiratory Diseases, Institute of Health Carlos III, Madrid, Spain, (5) Intensive Care Department, Vall d’Hebron Hospital Universitari, Shock, Organ Dysfunction and Resuscitation (SODIR) Research Group, Vall d’Hebron Institut de Recerca, Barcelona, Spain, (6) Department of Pulmonary, Hospital Clinic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain, (7) Department of Pulmonary, University and Polytechnic Hospital La Fe, Valencia, Spain, (8) Barcelona Supercomputing Center, Barcelona, Spain, (9) Hospital Universitario de Getafe, Madrid, Spain, (10) Faculty of Medicine, University of Concepción, Concepción, Chile, (11) Department of Clinical Biochemistry and Immunology, Faculty of Pharmacy, Concepción, Chile, (12) Intensive Care Department, Hospital Universitari Arnau de Vilanova de Lleida, Lleida, Spain, (13) Intensive Care Department, Hospital Universitari Santa Maria de Lleida, Lleida, SpainPostprint (published version

Blood-based lipidomic signature of severe obstructive sleep apnoea in Alzheimer's disease

Background: Obstructive sleep apnoea (OSA) is the most frequent form of sleep-disordered breathing in patients with Alzheimer’s disease (AD). Available evidence demonstrates that both conditions are independently associated with alterations in lipid metabolism. However, it is unknown whether the expression of lipids is diferent between AD patients with and without severe OSA. In this context, we examined the plasma lipidome of patients with suspected OSA, aiming to identify potential diagnostic biomarkers and to provide insights into the pathophysiological mechanisms underlying the disease. Methods: The study included 103 consecutive patients from the memory unit of our institution with a diagnosis of AD. The individuals were subjected to overnight polysomnography (PSG) to diagnose severe OSA (apnoea-hypopnea index ≥30/h), and blood was collected the following morning. Untargeted plasma lipidomic profling was performed using liquid chromatography coupled with mass spectrometry. Results: We identifed a subset of 44 lipids (mainly phospholipids and glycerolipids) that were expressed diferently between patients with AD and severe and nonsevere OSA. Among the lipids in this profle, 30 were signifcantly correlated with specifc PSG measures of OSA severity related to sleep fragmentation and hypoxemia. Machine learning analyses revealed a 4-lipid signature (phosphatidylcholine PC(35:4), cis-8,11,14,17-eicosatetraenoic acid and two oxidized triglycerides (OxTG(58:5) and OxTG(62:12)) that provided an accuracy (95% CI) of 0.78 (0.69–0.86) in the detection of OSA. These same lipids improved the predictive power of the STOP-Bang questionnaire in terms of the area under the curve (AUC) from 0.61 (0.50–0.74) to 0.80 (0.70–0.90). Conclusion: Our results show a plasma lipidomic fngerprint that allows the identifcation of patients with AD and severe OSA, allowing the personalized management of these individuals. The fndings suggest that oxidative stress and infammation are potential prominent mechanisms underlying the association between OSA and AD.Government of Catalonia, Department of Health (PERIS 2019 SLT008/18/00050) and “Fundació La Marató TV3” (464/C/2014) to GPR. The Spanish Ministry of Science, Innovation, and Universities (Ministerio de Ciencia, Innovación y Universidades), co-fnanced by FEDER funds from the European Union “A way to build Europe” (grant RTI2018-099200-B-I00), the IRBLleida-Diputació de Lleida (PIRS2021), and the Government of Catalonia: Agency for Management of University and Research Grants (2017SGR696) to RP. FD was supported by the Agency for Management of University and Research Grants and the European Social Fund (FI_B100153). IRBLleida is a CERCA Program of the Government of Catalonia

Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study

Background: The clinical heterogeneity of COVID-19 suggests the existence of different phenotypes with prognostic implications. We aimed to analyze comorbidity patterns in critically ill COVID-19 patients and assess their impact on in-hospital outcomes, response to treatment and sequelae. Methods: Multicenter prospective/retrospective observational study in intensive care units of 55 Spanish hospitals. 5866 PCR-confirmed COVID-19 patients had comorbidities recorded at hospital admission; clinical and biological parameters, in-hospital procedures and complications throughout the stay; and, clinical complications, persistent symptoms and sequelae at 3 and 6 months. Findings: Latent class analysis identified 3 phenotypes using training and test subcohorts: low-morbidity (n=3385; 58%), younger and with few comorbidities; high-morbidity (n=2074; 35%), with high comorbid burden; and renal-morbidity (n=407; 7%), with chronic kidney disease (CKD), high comorbidity burden and the worst oxygenation profile. Renal-morbidity and high-morbidity had more in-hospital complications and higher mortality risk than low-morbidity (adjusted HR (95% CI): 1.57 (1.34-1.84) and 1.16 (1.05-1.28), respectively). Corticosteroids, but not tocilizumab, were associated with lower mortality risk (HR (95% CI) 0.76 (0.63-0.93)), especially in renal-morbidity and high-morbidity. Renal-morbidity and high-morbidity showed the worst lung function throughout the follow-up, with renal-morbidity having the highest risk of infectious complications (6%), emergency visits (29%) or hospital readmissions (14%) at 6 months (p<0.01). Interpretation: Comorbidity-based phenotypes were identified and associated with different expression of in-hospital complications, mortality, treatment response, and sequelae, with CKD playing a major role. This could help clinicians in day-to-day decision making including the management of post-discharge COVID-19 sequelae.Financial support was provided by Instituto de Salud Carlos III (CIBERESUCICOVID, COV20/00110), co-funded by Fondo Europeo de Desarrollo Regional (FEDER), “Una manera de hacer Europa”, Centro de Investigación Biomédica en Red − Enfermedades Respiratorias (CIBERES) and Donation Program “estar preparados”, UNESPA, Madrid, Spain. JdB acknowledges receiving financial support from Instituto de Salud Carlos III (ISCIII; Miguel Servet 2019: CP19/00108), cofunded by the European Social Fund (ESF), “Investing in your future”. DdGC acknowledges receiving financial support from Instituto de Salud Carlos III (ISCIII; Miguel Servet 2019: CP20/00041), co-funded by the European Social Fund (ESF), “Investing in your future”. AC acknowledges receiving financial support from Instituto de Salud Carlos III (ISCIII; Sara Borrell 2021: CD21/00087).Peer ReviewedArticle signat per 71 autors/es: Iván D. Benítez (a,b,1), Jordi de Batlle (a,b,1), Gerard Torres (a,b), Jessica Gonzáalez (a,b), David de Gonzalo-Calvo (a,b), Adriano D.S. Targa (a,b), Clara Gort-Paniello (a,b), Anna Moncusí-Moix (a,b), Adrián Ceccato (b,c), Laia Fernández-Barat (b,d), Ricard Ferrer (b,e), Dario Garcia-Gasulla (f), Rosario Menéndez (b,g), Anna Motos (b,d), Oscar Peñuelas (b,h), Jordi Riera (b,e), Jesús F. Bermejo-Martin (b,i), Yhivian Peñasco (j), Pilar Ricart (k), María Cruz Martin Delgado(l), Luciano Aguilera(m), Alejandro Rodríguez(n), Maria Victoria Boado Varela (o), Fernando Suarez-Sipmann (p), Juan Carlos Pozo-Laderas (q), Jordi Solé-Violan (r), Maite Nieto (s), Mariana Andrea Novo (t), José Barberán (u), Rosario Amaya Villar (v), José Garnacho-Montero (w), Jose Luis García-Garmendia (x), José M. Gómez (y), José Ángel Lorente (b,h), Aaron Blandino Ortiz (z), Luis Tamayo Lomas (aa), Esther López-Ramos (ab), Alejandro Úbeda (ac), Mercedes Catalán-González (ad), Angel Sánchez-Miralles (ae), Ignacio Martínez Varela (af), Ruth Noemí Jorge García (ag), Nieves Franco (ah), Víctor D. Gumucio-Sanguino (ai), Arturo Huerta Garcia (aj), Elena Bustamante-Munguira (ak), Luis Jorge Valdivia (al), Jesús Caballero (am), Elena Gallego (an), Amalia Martínez de la Gándara (ao), Álvaro Castellanos-Ortega (ap), Josep Trenado (aq), Judith Marin-Corral (ar), Guillermo M Albaiceta (b,as), Maria del Carmen de la Torre (at), Ana Loza-Vázquez (au), Pablo Vidal (av), Juan Lopez Messa (aw), Jose M. Añon (b,ax), Cristina Carbajales Pérez (ay), Victor Sagredo (az), Neus Bofill (ba), Nieves Carbonell (bb), Lorenzo Socias(bc), Carme Barberá (bd), Angel Estella (be), Manuel Valledor Mendez (bf), Emili Diaz (bg), Ana López Lago (bh), Antoni Torres (b,d) and Ferran Barbé (a,b*), on behalf of the CIBERESUCICOVID Project (COV20/00110, ISCIII)2 // (a) Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; (b) CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; (c) Critical Care Center, ParcTaulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Sabadell, Spain; (d) Department of Pneumology, Hospital Clinic of Barcelona; August Pi i Sunyer Biomedical Research Institute−IDIBAPS, University of Barcelona, Barcelona, Spain; (e) Intensive Care Department, Vall d’Hebron Hospital Universitari. SODIR Research Group, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain; (f) Barcelona Supercomputing Center (BSC), Barcelona, Spain; (g) Pulmonology Service, University and Polytechnic Hospital La Fe, Valencia, Spain; (h) Hospital Universitario de Getafe, Madrid, Spain; Universidad Europea, Madrid, Spain; (i) Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain; Group for Biomedical Research in Sepsis (BioSepsis), Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, Spain; (j) Servicio de Medicina Intensiva, Hospital Universitario Marqués de Valdecilla, Santander, Spain; (k) Servei de Medicina Intensiva, Hospital Universitari Germans Trias, Badalona, Spain; (l) Hospital Universitario Torrejón-Universidad Francisco de Vitoria, Madrid, Spain; (m) Servicio de Anestesiología y Reanimación, Hospital Universitario Basurto, Bilbao, Spain; (n) Critical Care Department, Hospital Joan XXIII, Tarragona, Spain; (o) Servicio de Medicina Intensiva, Hospital de Cruces, Baracaldo, Vizcaya, Spain; (p) Intensive Care Unit, Hospital Universitario La Princesa, Madrid, Spain; (q) UGC-Medicina Intensiva, Hospital Universitario Reina Sofia, Instituto Maimonides IMIBIC, Córdoba, Spain; (r) Critical Care Department, Hospital Dr. Negrín Gran Canaria, Las Palmas, Gran Canaria, Spain. Universidad Fernando Pessoa, Canarias, Spain; (s) Hospital Universitario de Segovia, Segovia, Spain; (t) Servei de Medicina Intensiva, Hospital Universitari Son Espases, Palma de Mallorca, Illes Balears, Spain; (u) Hospital Universitario HM Montepríncipe, Universidad San Pablo-CEU, Madrid, Spain; vIntensive Care Clinical Unit, Hospital Universitario Virgen de Rocío, Sevilla, Spain; (w) Intensive Care Clinical Unit, Hospital Universitario Virgen Macarena, Seville, Spain; (x) Intensive Care Unit, Hospital San Juan de Dios del Aljarafe, Bormujos, Sevilla, Spain; (y) Hospital General Universitario Gregorio Marañon, Madrid, Spain; (z) Servicio de Medicina Intensiva, Hospital Universitario Ramón y Cajal, Madrid, Spain; (aa) Critical Care Department, Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain; (ab) Servicio de Medicina Intensiva, Hospital Universitario Príncipe de Asturias, Madrid, Spain; (ac) Servicio de Medicina Intensiva, Hospital Punta de Europa, Algeciras, Spain; (ad) Department of Intensive Care Medicine, Hospital Universitario 12 de Octubre, Madrid, Spain; (ae) Hospital de Sant Joan d’Alacant, Alacant, Spain; (af) Critical Care Department, Hospital Universitario Lucus Augusti, Lugo, Spain; (ag) Intensive Care Department, Hospital Nuestra Señora de Gracia, Zaragoza, Spain; (ah) Hospital Universitario de Móstoles, Madrid, Spain; (ai) Department of Intensive Care. Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain. Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; (aj) Pulmonary and Critical Care Division; Emergency Department, Clínica Sagrada Família, Barcelona, Spain; (ak) Department of Intensive Care Medicine, Hospital Clínico Universitario Valladolid, Valladolid, Spain; (al) Hospital Universitario de León, León, Spain; (am) Critical Care Department, Hospital Universitari Arnau de Vilanova; IRBLleida, Lleida, Spain; (an) Unidad de Cuidados Intensivos, Hospital Universitario San Pedro de Alcántara, Cáceres, Spain; (ao) Department of Intensive Medicine, Hospital Universitario Infanta Leonor, Madrid, Spain; (ap) Servicio de medicina intensiva. Hospital Universitario y Politécnico La Fe, Valencia, Spain; (aq) Servicio de Medicina Intensiva, Hospital Universitario Mútua de Terrassa, Terrassa, Barcelona, Spain; (ar) Critical Care Department, Hospital del Mar-IMIM, Barcelona, Spain; (as) Departamento de Biología Funcional. Instituto Universitario de Oncología del Principado de Asturias, Universidad de Oviedo; Instituto de Investigación Sanitaria del Principado de Asturias, Hospital Central de Asturias, Oviedo, Spain; (at) Hospital de Mataró de Barcelona, Spain; (au) Unidad de Medicina Intensiva, Hospital Universitario Virgen de Valme, Sevilla, Spain; (av) Complexo Hospitalario Universitario de Ourense, Ourense, Spain; (aw) Complejo Asistencial Universitario de Palencia, Palencia, Spain; (ax) Servicio de Medicina Intensiva. Hospital Universitario La Paz, IdiPAZ, Madrid, Spain; (ay) Intensive Care Unit, Hospital Álvaro Cunqueiro, Vigo, Spain; (az) Hospital Universitario de Salamanca, Salamanca, Spain; (ba) Department of Physical Medicine and Rehabilitation, Hospital Verge de la Cinta, Tortosa, Tarragona, Spain; (bb) Intensive Care Unit, Hospital Clínico y Universitario de Valencia, Valencia, Spain; (bc) Intensive Care Unit, Hospital Son Llàtzer, Palma de Mallorca, Illes Balears, Spain; (bd) Hospital Santa Maria; IRBLleida, Lleida, Spain; (be) Intensive Care Unit, University Hospital of Jerez. Medicine Department University of Cadiz. INiBICA, Spain; (bf) Hospital Universitario San Agustín, Asturias, Spain; (bg) Department of Medicine, Universitat Autónoma de Barcelona (UAB); Critical Care Department, Corporació Sanitària Parc Taulí, Sabadell, Barcelona, Spain; (bh) Department of Intensive care Medicine, Complejo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, SpainPostprint (published version

Impact of time to intubation on mortality and pulmonary sequelae in critically ill patients with COVID-19: a prospective cohort study

We evaluated whether the time between first respiratory support and intubation of patients receiving invasive mechanical ventilation (IMV) due to COVID-19 was associated with mortality or pulmonary sequelae. Materials and methods: Prospective cohort of critical COVID-19 patients on IMV. Patients were classified as early intubation if they were intubated within the first 48 h from the first respiratory support or delayed intubation if they were intubated later. Surviving patients were evaluated after hospital discharge. Results: We included 205 patients (140 with early IMV and 65 with delayed IMV). The median [p25;p75] age was 63 [56.0; 70.0] years, and 74.1% were male. The survival analysis showed a significant increase in the risk of mortality in the delayed group with an adjusted hazard ratio (HR) of 2.45 (95% CI 1.29–4.65). The continuous predictor time to IMV showed a nonlinear association with the risk of in-hospital mortality. A multivariate mortality model showed that delay of IMV was a factor associated with mortality (HR of 2.40; 95% CI 1.42–4.1). During follow-up, patients in the delayed group showed a worse DLCO (mean difference of -¿10.77 (95% CI -¿18.40 to -¿3.15), with a greater number of affected lobes (+¿1.51 [95% CI 0.89–2.13]) and a greater TSS (+¿4.35 [95% CI 2.41–6.27]) in the chest CT scan. Conclusions: Among critically ill patients with COVID-19 who required IMV, the delay in intubation from the first respiratory support was associated with an increase in hospital mortality and worse pulmonary sequelae during follow-up.Postprint (published version

Pulmonary function and radiologic features in survivors of critical COVID-19: a 3-month prospective cohort

© 2021 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/BACKGROUND: More than 20% of hospitalized patients with COVID-19 demonstrate ARDS requiring ICU admission. The long-term respiratory sequelae in such patients remain unclear. RESEARCH QUESTION: What are the major long-term pulmonary sequelae in critical patients who survive COVID-19? STUDY DESIGN AND METHODS: Consecutive patients with COVID-19 requiring ICU admission were recruited and evaluated 3 months after hospitalization discharge. The follow-up comprised symptom and quality of life, anxiety and depression questionnaires, pulmonary function tests, exercise test (6-min walking test [6MWT]), and chest CT imaging. RESULTS: One hundred twenty-five patients admitted to the ICU with ARDS secondary to COVID- 19 were recruited between March and June 2020. At the 3-month follow-up, 62 patients were available for pulmonary evaluation. The most frequent symptoms were dyspnea (46.7%) and cough (34.4%). Eighty-two percent of patients showed a lung diffusing capacity of less than 80%. The median distance in the 6MWT was 400 m (interquartile range, 362-440 m). CT scans showed abnormal results in 70.2% of patients, demonstrating reticular lesions in 49.1% and fibrotic patterns in 21.1%. Patients with more severe alterations on chest CT scan showed worse pulmonary function and presented more degrees of desaturation in the 6MWT. Factors associated with the severity of lung damage on chest CT scan were age and length of invasive mechanical ventilation during the ICU stay. INTERPRETATION: Three months after hospital discharge, pulmonary structural abnormalities and functional impairment are highly prevalent in patients with ARDS secondary to COVID- 19 who required an ICU stay. Pulmonary evaluation should be considered for all critical COVID-19 survivors 3 months after discharge.This study was supported in part by the Instituto de Salud Carlos III [Grant CIBERESUCICOVID, COV20/00110] and was cofunded by European Regional Development Funds, “Una manera de hacer Europa.” D. d. G.-C. has received financial support from the Instituto de Salud Carlos III [Grant Miguel Servet 2020: CP20/00041], co-funded by the European Social Fund “Investing in Your Future.” L. P. acknowledges receiving financial support from the Ministry of Science, Innovation and Universities for the Training of University Lecturers (FPU19 / 03526).Peer ReviewedPostprint (author's final draft

Impact of time to intubation on mortality and pulmonary sequelae in critically ill patients with COVID-19: a prospective cohort study

Question: We evaluated whether the time between first respiratory support and intubation of patients receiving invasive mechanical ventilation (IMV) due to COVID-19 was associated with mortality or pulmonary sequelae. Materials and methods: Prospective cohort of critical COVID-19 patients on IMV. Patients were classified as early intubation if they were intubated within the first 48 h from the first respiratory support or delayed intubation if they were intubated later. Surviving patients were evaluated after hospital discharge. Results: We included 205 patients (140 with early IMV and 65 with delayed IMV). The median [p25;p75] age was 63 [56.0; 70.0] years, and 74.1% were male. The survival analysis showed a significant increase in the risk of mortality in the delayed group with an adjusted hazard ratio (HR) of 2.45 (95% CI 1.29-4.65). The continuous predictor time to IMV showed a nonlinear association with the risk of in-hospital mortality. A multivariate mortality model showed that delay of IMV was a factor associated with mortality (HR of 2.40; 95% CI 1.42-4.1). During follow-up, patients in the delayed group showed a worse DLCO (mean difference of - 10.77 (95% CI - 18.40 to - 3.15), with a greater number of affected lobes (+ 1.51 [95% CI 0.89-2.13]) and a greater TSS (+ 4.35 [95% CI 2.41-6.27]) in the chest CT scan. Conclusions: Among critically ill patients with COVID-19 who required IMV, the delay in intubation from the first respiratory support was associated with an increase in hospital mortality and worse pulmonary sequelae during follow-up.The study was supported in part by ISCIII (CIBERESUCICOVID, COV20/00110), co‑funded by ERDF, “Una manera de hacer Europa” and Donation pro‑gram "estar preparados". UNESPA. Madrid. Spain David de Gonzalo Calvo acknowledges receiving financial support from Instituto de Salud Carlos III (ISCIII); Miguel Servet 2020: CP20/00041), co‑funded by the European Social Fund (ESF), “Investing in your future”. JdB acknowledges receiving financial support from Instituto de Salud Carlos III (Miguel Servet 2019: CP19/00108), co‑funded by European Regional European Social Fund (ESF), “Investing in your future