Observational studyCritical care (London, England)2024
Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort.
Observational study in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Discovery of postoperative clinical states linked to cardiac surgery-associated acute kidney injury.BioData mining · 2026Article
- Biomarker-guided use of corticosteroids in pneumonia.Pneumonia (Nathan Qld.) · 2026Review
- Radiographic phenotype-driven clustering in lumbar decompression: comparative study of outcome and reoperation risk.The spine journal : official journal of the North American Spine Society · 2026Article
- Immune-endothelial-coagulation crosstalk as a driver of multi-organ dysfunction in severe viral pneumonia.Frontiers in immunology · 2026Review
- Machine-learning approach uncovers hemodynamic-driven phenotypes in cardiac surgery by clustering multimodal, high-dimensional perioperative data.International journal of surgery (London, England) · 2025Article
- Prognostic Value of the Charlson Comorbidity Index for Mortality and Machine Learning-Based Prediction in Critically Ill Patients with Paralytic Ileus: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Clinical Phenotyping in Acute Respiratory Distress Syndrome: Steps Towards Personalized Medicine.Journal of clinical medicine · 2025Article
- Dynamics of Subphenotypes in Critical Illness: When the Tick-Tock of the Clock Counts.American journal of respiratory and critical care medicine · 2025Article
- Identifying and Validating Prognostic Hyper-Inflammatory and Hypo-Inflammatory COVID-19 Clinical Phenotypes Using Machine Learning Methods.Journal of inflammation research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
73 authors.
Funding
Abstract
backgroundAcute 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.
methodsMulticenter, 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.
resultsOf 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.
conclusionsDuring 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.