Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients - Université de Versailles Saint-Quentin-en-Yvelines Access content directly
Journal Articles Joint Bone Spine Year : 2023

Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients

Enrico de Lorenzis
  • Function : Author
Paolo Parente
  • Function : Author
Gerlando Natalello
  • Function : Author
Salvatore Soldati
  • Function : Author
Silvia Laura Bosello
  • Function : Author
Andrea Barbara
  • Function : Author
Chiara Sorge
  • Function : Author
Svetlana Axelrod
  • Function : Author
Lucrezia Verardi
  • Function : Author
Pier Giacomo Cerasuolo
  • Function : Author
Giusy Peluso
  • Function : Author
Antonella Gemma
  • Function : Author
Marina Davoli
  • Function : Author
Donatella Biliotti
  • Function : Author
Vincenzo Bruzzese
  • Function : Author
Mauro Goletti
  • Function : Author
Mirko Di Martino
  • Function : Author

Abstract

Since the COVID-19 outbreak, public health authorities have looked for the best evidence on infection risk and prognosis to guide their choices. The interpretation of the observational studies that variably reported increased infection rates and a poor prognosis in patients with systemic rheumatic diseases (SRD) has been limited by factors such as the selection of patients in the care of tertiary referral centres, the small available sample sizes for the less prevalent diseases, the description of SRDs as a single broad category, and the neglected influence of comorbidities [1]. The use of big healthcare data has become essential in gathering crucial information for a reliable identification of high-risk groups. The Chronic Related Group (CReG) system is an experimental approach of classification of chronic patients created to predict the medical resources needed to ensure their care. This system automatically assigns a diagnosis to a subject according to medical administrative records over a pre-set period. Specifically, CReG system relies on the registration and integration of disease-specific codes used to determine the share of healthcare costs, hospital discharge diagnoses codes and access to the prescription of drugs or therapeutic procedures uniquely associated with a specific condition [2], [3]. In this analysis, we compared incidences and 30-day outcomes of 40,490 SRD patients (Table 1) to 471,6119 subjects dwelling in the Lazio Region, the second most populated region of Italy that includes the Rome metropolitan area. SRDs and comorbidity diagnoses were derived from the CReG classification while data on COVID-19 infection from a regional digital network. The risk was expressed as incidence rate ratio adjusted for demographics and comorbidities. We focused on the period from the 20th of February 2020 to the 31st of December 2020 to selectively assess a cohort of unvaccinated patients.

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Health

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hal-04552303 , version 1 (19-04-2024)

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Enrico de Lorenzis, Paolo Parente, Gerlando Natalello, Salvatore Soldati, Silvia Laura Bosello, et al.. Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients. Joint Bone Spine, 2023, 90 (2), pp.105497. ⟨10.1016/j.jbspin.2022.105497⟩. ⟨hal-04552303⟩
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