ArticleNPJ digital medicine2021
Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The trial behind it
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Who cites it
19 citing papers in PubMed, 35 citations in OpenAlex.
- Hybrid phenotype-guided modeling across algorithm-feature regimes with application to ICU mortality prediction for Acinetobacter baumannii.Scientific reports · 2026Article
- Article
- Identifying and Validating Prognostic Hyper-Inflammatory and Hypo-Inflammatory COVID-19 Clinical Phenotypes Using Machine Learning Methods.Journal of inflammation research · 2025Article
- Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data.NPJ digital medicine · 2024Article
- Harnessing EHR data for health research.Nature medicine · 2024Review
- Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality.Pneumonia (Nathan Qld.) · 2024Article
- Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges.BMC medical informatics and decision making · 2024Article
- Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York.International journal of medical informatics · 2024Article
- Radiomics-based machine learning model to phenotype hip involvement in ankylosing spondylitis: a pilot study.Frontiers in immunology · 2024Article
- COVID-19 subphenotypes at hospital admission are associated with mortality: a cross-sectional study.Annals of medicine · 2023Article
- A Multi-Layered GRU Model for COVID-19 Patient Representation and Phenotyping from Large-Scale EHR Data.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2023Article
- Supervised Pretraining through Contrastive Categorical Positive Samplings to Improve COVID-19 Mortality Prediction.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2022Article
- Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes.Obesity (Silver Spring, Md.) · 2022Article
- Machine Learning-Based COVID-19 Patients Triage Algorithm Using Patient-Generated Health Data from Nationwide Multicenter Database.Infectious diseases and therapy · 2022Article
- Artificial intelligence for COVID-19: battling the pandemic with computational intelligence.Intelligent medicine · 2022Review
- Using sequence clustering to identify clinically relevant subphenotypes in patients with COVID-19 admitted to the intensive care unit.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data.Frontiers in public health · 2022Article
- Social Determinants of Health Correlating with Mechanical Ventilation of COVID-19 Patients: A Multi-Center Observational Study.International journal of general medicine · 2021Article
- Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.
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Registered trials
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 OpenQuestion graph.