Derivation and external validation of a simple prediction rule for the development of respiratory failure in hospitalized patients with influenza.
Clinical prediction rules
Human
Influenza
Mechanical ventilation
Pneumonia
Respiratory failure
Viral
Journal
Respiratory research
ISSN: 1465-993X
Titre abrégé: Respir Res
Pays: England
ID NLM: 101090633
Informations de publication
Date de publication:
24 Nov 2022
24 Nov 2022
Historique:
received:
10
01
2022
accepted:
09
11
2022
entrez:
24
11
2022
pubmed:
25
11
2022
medline:
26
11
2022
Statut:
epublish
Résumé
Influenza viruses cause seasonal epidemics worldwide with a significant morbimortality burden. Clinical spectrum of Influenza is wide, being respiratory failure (RF) one of its most severe complications. This study aims to elaborate a clinical prediction rule of RF in hospitalized Influenza patients. A prospective cohort study was conducted during two consecutive Influenza seasons (December 2016-March 2017 and December 2017-April 2018) including hospitalized adults with confirmed A or B Influenza infection. A prediction rule was derived using logistic regression and recursive partitioning, followed by internal cross-validation. External validation was performed on a retrospective cohort in a different hospital between December 2018 and May 2019. Overall, 707 patients were included in the derivation cohort and 285 in the validation cohort. RF rate was 6.8% and 11.6%, respectively. Chronic obstructive pulmonary disease, immunosuppression, radiological abnormalities, respiratory rate, lymphopenia, lactate dehydrogenase and C-reactive protein at admission were associated with RF. A four category-grouped seven point-score was derived including radiological abnormalities, lymphopenia, respiratory rate and lactate dehydrogenase. Final model area under the curve was 0.796 (0.714-0.877) in the derivation cohort and 0.773 (0.687-0.859) in the validation cohort (p < 0.001 in both cases). The predicted model showed an adequate fit with the observed results (Fisher's test p > 0.43). we present a simple, discriminating, well-calibrated rule for an early prediction of the development of RF in hospitalized Influenza patients, with proper performance in an external validation cohort. This tool can be helpful in patient's stratification during seasonal Influenza epidemics.
Sections du résumé
BACKGROUND
BACKGROUND
Influenza viruses cause seasonal epidemics worldwide with a significant morbimortality burden. Clinical spectrum of Influenza is wide, being respiratory failure (RF) one of its most severe complications. This study aims to elaborate a clinical prediction rule of RF in hospitalized Influenza patients.
METHODS
METHODS
A prospective cohort study was conducted during two consecutive Influenza seasons (December 2016-March 2017 and December 2017-April 2018) including hospitalized adults with confirmed A or B Influenza infection. A prediction rule was derived using logistic regression and recursive partitioning, followed by internal cross-validation. External validation was performed on a retrospective cohort in a different hospital between December 2018 and May 2019.
RESULTS
RESULTS
Overall, 707 patients were included in the derivation cohort and 285 in the validation cohort. RF rate was 6.8% and 11.6%, respectively. Chronic obstructive pulmonary disease, immunosuppression, radiological abnormalities, respiratory rate, lymphopenia, lactate dehydrogenase and C-reactive protein at admission were associated with RF. A four category-grouped seven point-score was derived including radiological abnormalities, lymphopenia, respiratory rate and lactate dehydrogenase. Final model area under the curve was 0.796 (0.714-0.877) in the derivation cohort and 0.773 (0.687-0.859) in the validation cohort (p < 0.001 in both cases). The predicted model showed an adequate fit with the observed results (Fisher's test p > 0.43).
CONCLUSION
CONCLUSIONS
we present a simple, discriminating, well-calibrated rule for an early prediction of the development of RF in hospitalized Influenza patients, with proper performance in an external validation cohort. This tool can be helpful in patient's stratification during seasonal Influenza epidemics.
Identifiants
pubmed: 36419130
doi: 10.1186/s12931-022-02245-w
pii: 10.1186/s12931-022-02245-w
pmc: PMC9684757
doi:
Substances chimiques
Lactate Dehydrogenases
EC 1.1.-
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
323Subventions
Organisme : Instituto de Salud Carlos III
ID : 17/01129
Informations de copyright
© 2022. The Author(s).
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