Portal de investigación
K-Means Clustering Identifies Diverse Clinical Phenotypes in COVID-19 Patients: Implications for Mortality Risks and Remdesivir Impact.
Fecha de publicación:
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Autores de IIS La Fe
Participantes ajenos a IIS La Fe
- Teijón-Lumbreras C
- Aiello TF
- Chumbita M
- Mateu-Subirà A
- Peyrony O
- Monzó P
- Lopera C
- Gallardo-Pizarro A
- Calbo E
- Xercavins M
- Cuesta-Chasco G
- Mensa J
- Soriano A
Grupos
Abstract
INTRODUCTION: The impact of remdesivir on mortality in patients with COVID-19 is still controversial. We aimed to identify clinical phenotype clusters of COVID-19 hospitalized patients with highest benefit from remdesivir use and validate these findings in an external cohort. METHODS: We included consecutive patients hospitalized between February 2020 and February 2021 for COVID-19. The derivation cohort comprised subjects admitted to Hospital Clinic of Barcelona. The validation cohort included patients from Hospital Universitari Mutua de Terrassa (Terrassa) and Hospital Universitari La Fe (Valencia), all tertiary centers in Spain. We employed K-means clustering to group patients according to reverse transcription polymerase chain reaction (rRT-PCR) cycle threshold (Ct) values and lymphocyte counts at diagnosis, and pre-test symptom duration. The impact of remdesivir on 60-day mortality in each cluster was assessed. RESULTS: A total of 1160 patients (median age 66, interquartile range (IQR) 55-78) were included. We identified five clusters, with mortality rates ranging from 0 to 36.7%. Highest mortality rate was observed in the cluster including patients with shorter pre-test symptom duration, lower lymphocyte counts, and lower Ct values at diagnosis. The absence of remdesivir administration was associated with worse outcome in the high-mortality cluster (10.5% vs. 36.7%; p < 0.001), comprising subjects with higher viral loads. These results were validated in an external multicenter cohort of 981 patients. CONCLUSIONS: Patients with COVID-19 exhibit varying mortality rates across different clinical phenotypes. K-means clustering aids in identifying patients who derive the greatest mortality benefit from remdesivir use.
© 2024. The Author(s).
Datos de la publicación
- ISSN/ISSNe:
- 2193-8229, 2193-6382
- Tipo:
- Article
- Páginas:
- 715-726
- PubMed:
- 38489118
- Factor de Impacto:
- 1,141 SCImago ℠
- Cuartil:
- Q2 SCImago ℠
Infectious Diseases and Therapy SPRINGER LONDON LTD
Citas Recibidas en Web of Science: 11
Documentos
- No hay documentos
Filiaciones
Keywords
- Antiviral agents; Artificial intelligence; COVID-19; Clustering
Cita
GARCIA C,Teijón C,Aiello TF,Chumbita M,MENENDEZ R,Mateu A,Peyrony O,Monzó P,Lopera C,Gallardo A,MÉNDEZ R,Calbo E,Xercavins M,Cuesta G,MARTÍNEZ JA,MARCOS MA,Mensa J,Soriano A. K-Means Clustering Identifies Diverse Clinical Phenotypes in COVID-19 Patients: Implications for Mortality Risks and Remdesivir Impact. Infect. Dis. Ther. 2024. 13. (4):p. 715-726. IF:5,300. (1).