ArticleJournal of the American Medical Informatics Association : JAMIA2022
Using sequence clustering to identify clinically relevant subphenotypes in patients with COVID-19 admitted to the intensive care unit.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Prescribing Trajectories in Type 2 Diabetes in the United States, 2019-2024.Diabetes, obesity & metabolism · 2026Article
- A clustering-based trajectory analytics of functional loss and recovery among older adults.PloS one · 2026Article
- Identifying Stigma Phenotypes in Social Media Narratives of Substance Use: Observational Study.Journal of medical Internet research · 2025Observational
- Soft phenotyping for sepsis via EHR time-aware soft clustering.Journal of biomedical informatics · 2024Article
- A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.Artificial intelligence in medicine · 2024Article
- Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis.JMIR aging · 2023Article
- Clinical Sepsis Phenotypes in Critically Ill Patients.Microorganisms · 2023Review
- Clinical and temporal characterization of COVID-19 subgroups using patient vector embeddings of electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
objectiveThe novel coronavirus disease 2019 (COVID-19) has heterogenous clinical courses, indicating that there might be distinct subphenotypes in critically ill patients. Although prior research has identified these subphenotypes, the temporal pattern of multiple clinical features has not been considered in cluster models. We aimed to identify temporal subphenotypes in critically ill patients with COVID-19 using a novel sequence cluster analysis and associate them with clinically relevant outcomes. MATERIALS AND
methodsWe analyzed 1036 confirmed critically ill patients with laboratory-confirmed SARS-COV-2 infection admitted to the Mount Sinai Health System in New York city. The agglomerative hierarchical clustering method was used with Levenshtein distance and Ward's minimum variance linkage.
resultsWe identified four subphenotypes. Subphenotype I (N = 233 [22.5%]) included patients with rapid respirations and a rapid heartbeat but less need for invasive interventions within the first 24 hours, along with a relatively good prognosis. Subphenotype II (N = 418 [40.3%]) represented patients with the least degree of ailments, relatively low mortality, and the highest probability of discharge from the hospital. Subphenotype III (N = 259 [25.0%]) represented patients who experienced clinical deterioration during the first 24 hours of intensive care unit admission, leading to poor outcomes. Subphenotype IV (N = 126 [12.2%]) represented an acute respiratory distress syndrome trajectory with an almost universal need for mechanical ventilation.
conclusionWe utilized the sequence cluster analysis to identify clinical subphenotypes in critically ill COVID-19 patients who had distinct temporal patterns and different clinical outcomes. This study points toward the utility of including temporal information in subphenotyping approaches.
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