Dataset Information

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort.

ABSTRACT:

Background

Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.

Methods

Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.

Results

Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.

Conclusions

During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis.

SUBMITTER: Ceccato A

PROVIDER: S-EPMC10958830 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort.

Ceccato Adrian A Forne Carles C Bos Lieuwe D LD Camprubí-Rimblas Marta M Areny-Balagueró Aina A Campaña-Duel Elena E Quero Sara S Diaz Emili E Roca Oriol O De Gonzalo-Calvo David D Fernández-Barat Laia L Motos Anna A Ferrer Ricard R Riera Jordi J Lorente Jose A JA Peñuelas Oscar O Menendez Rosario R Amaya-Villar Rosario R Añón José M JM Balan-Mariño Ana A Barberà Carme C Barberán José J Blandino-Ortiz Aaron A Boado Maria Victoria MV Bustamante-Munguira Elena E Caballero Jesús J Carbajales Cristina C Carbonell Nieves N Catalán-González Mercedes M Franco Nieves N Galbán Cristóbal C Gumucio-Sanguino Víctor D VD de la Torre Maria Del Carmen MDC Estella Ángel Á Gallego Elena E García-Garmendia José Luis JL Garnacho-Montero José J Gómez José M JM Huerta Arturo A Jorge-García Ruth Noemí RN Loza-Vázquez Ana A Marin-Corral Judith J Martínez de la Gándara Amalia A Martin-Delgado María Cruz MC Martínez-Varela Ignacio I Messa Juan Lopez JL Muñiz-Albaiceta Guillermo G Nieto María Teresa MT Novo Mariana Andrea MA Peñasco Yhivian Y Pozo-Laderas Juan Carlos JC Pérez-García Felipe F Ricart Pilar P Roche-Campo Ferran F Rodríguez Alejandro A Sagredo Victor V Sánchez-Miralles Angel A Sancho-Chinesta Susana S Socias Lorenzo L Solé-Violan Jordi J Suarez-Sipmann Fernando F Tamayo-Lomas Luis L Trenado José J Úbeda Alejandro A Valdivia Luis Jorge LJ Vidal Pablo P Bermejo Jesus J Gonzalez Jesica J Barbe Ferran F Calfee Carolyn S CS Artigas Antonio A Torres Antoni A

Critical care (London, England) 20240321 1

<h4>Background</h4>Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of ...[more]

PMID: 38515193

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Project description:BackgroundAs lung ultrasound (LUS) has emerged as a diagnostic tool in patients with COVID-19, we sought to investigate the association between LUS findings and the composite in-hospital outcome of ARDS incidence, ICU admission, and all-cause mortality.MethodsIn this prospective, multi-center, observational study, adults with laboratory-confirmed SARS-CoV-2 infection were enrolled from non-ICU in-patient units. Subjects underwent an LUS evaluating a total of 8 zones. Images were analyzed off-line, blinded to clinical variables and outcomes. A LUS score was developed to integrate LUS findings: ≥ 3 B-lines corresponded to a score of 1, confluent B-lines to a score of 2, and subpleural or lobar consolidation to a score of 3. The total LUS score ranged from 0-24 per subject.ResultsAmong 215 enrolled subjects, 168 with LUS data and no current signs of ARDS or ICU admission (mean age 59 y, 56% male) were included. One hundred thirty-six (81%) subjects had pathologic LUS findings in ≥ 1 zone (≥ 3 B-lines, confluent B-lines, or consolidations). Markers of disease severity at baseline were higher in subjects with the composite outcome (n = 31, 18%), including higher median C-reactive protein (90 mg/L vs 55, P < .001) and procalcitonin levels (0.35 μg/L vs 0.13, P = .033) and higher supplemental oxygen requirements (median 4 L/min vs 2, P = .001). However, LUS findings and score did not differ significantly between subjects with the composite outcome and those without, and were not associated with outcomes in unadjusted and adjusted logistic regression analyses.ConclusionsPathologic findings on LUS were common a median of 3 d after admission in this cohort of non-ICU hospitalized subjects with COVID-19 and did not differ among subjects who experienced the composite outcome of incident ARDS, ICU admission, and all-cause mortality compared to subjects who did not. These findings should be confirmed in future investigations. The study is registered at Clinicaltrials.gov (NCT04377035).

Dataset Information

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort.

Background

Methods

Results

Conclusions

Publications

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort.

Similar Datasets

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