Derivation and validation of the clinical prediction model for COVID-19.
Covid-19
Critical illness
Derivation score
Predictive-markers
Sars-CoV2
Score
Validation score
Journal
Internal and emergency medicine
ISSN: 1970-9366
Titre abrégé: Intern Emerg Med
Pays: Italy
ID NLM: 101263418
Informations de publication
Date de publication:
Nov 2020
Nov 2020
Historique:
received:
16
05
2020
accepted:
07
07
2020
pubmed:
16
9
2020
medline:
25
11
2020
entrez:
15
9
2020
Statut:
ppublish
Résumé
The epidemic phase of Coronavirus disease 2019 (COVID-19) made the Worldwide health system struggle against a severe interstitial pneumonia requiring high-intensity care settings for respiratory failure. A rationalisation of resources and a specific treatment path were necessary. The study suggests a predictive model drawing on clinical data gathered by 119 consecutive patients with laboratory-confirmed COVID-19 admitted in Busto Arsizio hospital. We derived a score that identifies the risk of clinical evolution and in-hospital mortality clustering patients into four groups. The study outcomes have been compared across the derivation and validation samples. The prediction rule is based on eight simple patient characteristics that were independently associated with study outcomes. It is able to stratify COVID-19 patients into four severity classes, with in-hospital mortality rates of 0% in group 1, 6-12.5% in group 2, 7-20% in group 3 and 60-86% in group 4 across the derivation and validation sample. The prediction model derived in this study identifies COVID-19 patients with low risk of in-hospital mortality and ICU admission. The prediction model that the study presents identifies COVID-19 patients with low risk of in-hospital mortality and admission to ICU. Moreover, it establishes an intermediate portion of patients that should be treated accurately in order to avoid an unfavourable clinical evolution. A further validation of the model is important before its implementation as a decision-making tool to guide the initial management of patients.
Identifiants
pubmed: 32930963
doi: 10.1007/s11739-020-02480-3
pii: 10.1007/s11739-020-02480-3
pmc: PMC7490315
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
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