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LONGITUDINAL CLUSTER ANALYSIS OF HEMODIALYSIS PATIENTS WITH COVID-19 IN THE PRE-VACCINATION ERA
Pasquale Esposito; Sara Garbarino; Daniela Fenoglio; Isabella Cama; Leda Cipriani; Cristina Campi; Alessia Parodi; Tiziana Vigo; Diego Franciotta; Tiziana Altosole; Fabrizio Grosjean; Francesca Viazzi; Gilberto Filaci; Michele Piana.
Afiliação
  • Pasquale Esposito; University of Genoa, Italy
  • Sara Garbarino; University of Genova, Italy
  • Daniela Fenoglio; University of Genova, Italy
  • Isabella Cama; University of Genova, Italy
  • Leda Cipriani; University of Genova, Italy
  • Cristina Campi; University of Genova, Italy
  • Alessia Parodi; Ospedale Policlinico San Martino, Genova, Italy
  • Tiziana Vigo; Ospedale Policlinico San Martino, Genova, Italy
  • Diego Franciotta; Ospedale Policlinico San Martino, Genova, Italy
  • Tiziana Altosole; University of Genova, Italy
  • Fabrizio Grosjean; Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
  • Francesca Viazzi; University of Genova, Italy
  • Gilberto Filaci; University of Genova, Italy
  • Michele Piana; University of Genova, Italy
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22279014
ABSTRACT
Coronavirus disease 2019 (COVID-19) is characterized by a high heterogeneity of clinical presentation and outcomes. This is also true for patients undergoing maintenance hemodialysis (HD), who, due to specific clinical factors and immune status, represent a distinct subgroup of COVID-19 patients. Starting from this observation in this research letter we tested and validated in two cohorts of HD patients with COVID-19 (derivation and validation cohort, respectively) an innovative model which combines linear mixed effect modeling and cluster analysis on longitudinal. This study aimed to describe a methodology allowing patient stratification from simple and widely available data. Our results could be interesting not only to improve COVID-19 management but also to support the application of longitudinal cluster analysis strategy in other clinical settings.
Licença
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Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Cohort_studies / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Cohort_studies / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
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