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COVID-19 Patients Benefitting From Remdesivir for Improved Survival: A Neural Network-Based Approach

Fecha de publicación:

Autores de IIS La Fe

Participantes ajenos a IIS La Fe

  • Teijón-Lumbreras, C
  • Aiello, TF
  • Chumbita, M
  • Subirà, AM
  • Peyrony, O
  • Monzo-Gallo, P
  • Gallardo-Pizarro, A
  • Sebastian, EC
  • Ballestero-Tellez, M
  • Cuesta-Chasco, G
  • Martínez, JA
  • Marcos, MA
  • Mensa, J
  • Soriano, A

Grupos

Abstract

Conflicting results from randomized trials regarding the efficacy of remdesivir for COVID-19 have been reported. We aimed to develop a neural network (NN) to identify COVID-19 patients who would derive the greatest survival benefit from remdesivir. This multicenter observational study included adults hospitalized for COVID-19 between February 2020 and February 2021. A derivation cohort from Hospital Cl & iacute;nic (Barcelona) was used to create the NN, which was split into 928/1160 (80%) for training and 232/1160 (20%) for internal validation. The model used three normalized input variables: Ct values from rRT-PCR, lymphocyte count at diagnosis, and the duration of symptoms before testing. Effectiveness was assessed in an external validation cohort of 898 patients from Hospital M & uacute;tua Terrassa and Hospital Universitari La Fe, Valencia. In the derivation cohort (median age 66 years; IQR 55-78), symptom duration, Ct values, and lymphocyte count showed considerable variation. Overall, 60-day mortality was 165/1160 (14.2%). In the training set, 385/928 (41.5%) patients were identified as benefiting from remdesivir, characterized by lower Ct values, reduced lymphocyte counts, and shorter symptom duration. Mortality in this subgroup was 93/385 (24.2%): 6/385 (7.2%) in patients receiving remdesivir versus 87/385 (28.8%) in those who did not (p < 0.001). In the test set, 296/898 (33%) patients were identified as high-benefit, with 60-day mortality rates of 8/296 (11%) for those patients treated with remdesivir compared to 49/296 (22%) for those not treated (p < 0.04). In conclusion, we successfully developed and validated a NN capable of identifying patients with distinct clinical phenotypes who are at higher risk of mortality without remdesivir.

© 2025 Wiley Periodicals LLC.

Datos de la publicación

ISSN/ISSNe:
0146-6615, 1096-9071

JOURNAL OF MEDICAL VIROLOGY  WILEY

Tipo:
Article
Páginas:
-
PubMed:
40059457
Enlace a otro recurso:
www.scopus.com
Factor de Impacto:
2,656 SCImago ℠
Cuartil:
Q1 SCImago ℠

Citas Recibidas en Web of Science: 1

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Keywords

  • COVID-19; neural networks, remdesivir, artificial intelligence; phenotypes

Proyectos y Estudios Clínicos

Contratos Juan Rodés 2021. Raúl Méndez.

Investigador Principal: RAÚL MÉNDEZ OCAÑA

JR21/00051 . INSTITUTO DE SALUD CARLOS III . 2022

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