ArticleCritical care (London, England)2021
Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain.
Article in Critical care (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04948242 (Epidemiologic, Clinical Characteristics and Risk Factors of Critical Ill Patients Admitted to the ICU for Severe Acute Respiratory Syndrome Coronavirus 2 Infection), which is not on this map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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Epidemiologic, Clinical Characteristics and Risk Factors of Critical Ill Patients Admitted to the ICU for Severe Acute Respiratory Syndrome Coronavirus 2 Infection (COVID-19)
Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mortality and Clinical Interventions in Critically ill Patient With Coronavirus Disease 2019: A Systematic Review and Meta-Analysis.Frontiers in medicine · 2021Pooled it
- Impact of a participatory research intervention based on a citizen science approach on snacking consumption in European adolescents from deprived areas: a cluster randomised control trial of the SEEDS project.BMC public health · 2026Trial
- Prediction of Hyperinflammatory Phenotypes in Critically Ill Patients via Routine Clinical Data and IL-6: Towards Personalized Anti-Inflammatory Therapy.International journal of molecular sciences · 2025Article
- Identifying clinically useful COVID-19 population and emergency department phenotypes across the pre-Omicron and Omicron periods.Archives of public health = Archives belges de sante publique · 2025Article
- Machine Learning-Based Identification of Risk Factors for ICU Mortality in 8902 Critically Ill Patients with Pandemic Viral Infection.Journal of clinical medicine · 2025Article
- Does Empirical Antibiotic Use Improve Outcomes in Ventilated Patients with Pandemic Viral Infection? A Multicentre Retrospective Study.Antibiotics (Basel, Switzerland) · 2025Article
- Identifying and Validating Prognostic Hyper-Inflammatory and Hypo-Inflammatory COVID-19 Clinical Phenotypes Using Machine Learning Methods.Journal of inflammation research · 2025Article
- Six clinical phenotypes with prognostic implications were identified by unsupervised machine learning in children and adolescents with SARS-CoV-2 infection: results from a German nationwide registry.Respiratory research · 2024Article
- A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection: A Spanish Perspective.Antibiotics (Basel, Switzerland) · 2024Article
- Article
- A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis.Journal of translational medicine · 2024Article
- Major adverse cardiovascular events (MACE) in patients with severe COVID-19 registered in the ISARIC WHO clinical characterization protocol: A prospective, multinational, observational study.Journal of critical care · 2023Observational
- Immunomodulatory approaches in managing lung inflammation in COVID-19: A double-edge sword.Immunity, inflammation and disease · 2023Review
- A scoping review of the clinical application of machine learning in data-driven population segmentation analysis.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Article
- Risk factors for developing ventilator-associated lower respiratory tract infection in patients with severe COVID-19: a multinational, multicentre study, prospective, observational study.Scientific reports · 2023Observational
- Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients: Results from a Cohort Observational Study.Journal of clinical medicine · 2023Article
- Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups.Scientific reports · 2023Article
- Intensive Care and Organ Support Related Mortality in Patients With COVID-19: A Systematic Review and Meta-Analysis.Critical care explorations · 2023Review
- Comparison of systemic inflammatory profiles in COVID-19 and community-acquired pneumonia patients: a prospective cohort study.Respiratory research · 2023Observational
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Authors and funding
32 authors.
Funding
Abstract
backgroundThe identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes.
methodsProspective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient's factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves.
resultsThe database included a total of 2022 patients (mean age 64 [IQR 5-71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10-17]) and SOFA score (5 [IQR 3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.
conclusionThe presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a "one-size-fits-all" model in practice.
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