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Background: The COVID-19 pandemic has led highly developed healthcare systems to the brink of collapse due to the large numbers of patients being admitted into hospitals. One of the potential prognostic indicators in patients with COVID-19 is frailty. The degree of frailty could be used to assist both the triage into intensive care, and decisions regarding treatment limitations. Our study sought to determine the interaction of frailty and age in elderly COVID-19 ICU patients. Methods: A prospective multicentre study of COVID-19 patients ≥ 70 years admitted to intensive care in 138 ICUs from 28 countries was conducted. The primary endpoint was 30-day mortality. Frailty was assessed using the clinical frailty scale. Additionally, comorbidities, management strategies and treatment limitations were recorded. Results: The study included 1346 patients (28% female) with a median age of 75 years (IQR 72-78, range 70-96), 16.3% were older than 80 years, and 21% of the patients were frail. The overall survival at 30 days was 59% (95% CI 56-62), with 66% (63-69) in fit, 53% (47-61) in vulnerable and 41% (35-47) in frail patients (p < 0.001). In frail patients, there was no difference in 30-day survival between different age categories. Frailty was linked to an increased use of treatment limitations and less use of mechanical ventilation. In a model controlling for age, disease severity, sex, treatment limitations and comorbidities, frailty was independently associated with lower survival. Conclusion: Frailty provides relevant prognostic information in elderly COVID-19 patients in addition to age and comorbidities. Trial registration Clinicaltrials.gov: NCT04321265 , registered 19 March 2020.
The impact of frailty on survival in elderly intensive care patients with COVID-19: the COVIP study
Jung C.;Flaatten H.;Fjolner J.;Bruno R. R.;Wernly B.;Artigas A.;Bollen Pinto B.;Schefold J. C.;Wolff G.;Kelm M.;Beil M.;Sviri S.;van Heerden P. V.;Szczeklik W.;Czuczwar M.;Elhadi M.;Joannidis M.;Oeyen S.;Zafeiridis T.;Marsh B.;Andersen F. H.;Moreno R.;Cecconi M.;Leaver S.;Boumendil A.;De Lange D. W.;Guidet B.;Flaatten H.;Wernly B.;Beil M.;Sviri S.;van Heerden P. V.;Elhadi M.;Zafeiridis T.;Moreno R.;Boumendil A.;Abosheaishaa H. M.;Abualqumboz E. M. Y.;Ahmed A. K.;Ahmed H.;Aidoni Z.;Aldecoa C.;Alexandru N.;Ali Y. K. N. E. M.;Al-Sadawi M.;Andersen K.;Andersen F. H.;Assis R.;Azab M. A.;Azzam A. Y.;Badawy M. R.;Balleby I. R.;Barth E.;Barth E.;Ben-HAmouda N.;Besch G.;Besset S.;Bjerregaard A. T.;Brix H.;Bruno R. R.;Brushoej J.;Bundgaard H.;Burtin P.;Caillard A.;Canas-Perez I.;Charron C.;Chrisanthopoulou E.;Comellini V.;Cornet A.;Cubero P. J.;Dauger S.;Diaz-Rodriguez C.;Dieperink W.;Dindane Z.;Djibre M.;Dormans T.;Dullenkopf A.;Dumas G.;Elgazzar Y. A.;Eller P.;Elsaka A.;Evers M.;Faltlhauser A.;Ferreira A. F.;Fjolner J.;Fleury Y.;Galbois A.;Garcon P.;Garnier M.;Gawda R.;Ghannam A.;Goebel U.;Goma G.;Goncalves B.;Gordinho A.;Groenendijk M.;Guerot E.;Guidet B.;Gurjar M.;Haake H.;Haas L.;Habib A. A.;Hahn M.;Hansen M. A.;Hilles M. M. Y.;Hussein A. A. R. M.;Iglesias D.;Joannidis M.;Jung C.;Jurcisin I.;Kabitz H. -J.;Kelm M.;Kindgen-Milles D.;Klimkiewicz J.;Kuhn K. F.;Kunstein A.;Kurt M.;De Lange D. W.;Leaver S.;Lutz M.;Mahmoodpoor A.;Maizel J.;Marin N.;Megarbane B.;Mesotten D.;Meybohm P.;Meyer C.;Mira A. P.;Namendys-Silva S.;Nedergaard H. K.;Nseir S.;Oeyen S.;Olasveengen T.;Oliveira A. I. P.;Oziel J.;Papadogoulas A.;Perez-Torres D.;Bollen Pinto B.;Piton G.;Plantefeve G.;Poerner T.;Priego J.;Rabha A.;Randerath W.;Raphaelen J. -H.;Reper P.;Rigaud J. -P.;Rivera S. A.;Roberti A.;Romundstad L.;Rovina N.;Salah R.;Saleh M.;Sancho S.;de Lurdes Campos Santos M.;Santos H.;Schaller S.;Schuster M.;Shala G.;Sjobo B.;Steiner S.;Strietzel H. F.;Sviri S.;Swinnen W.;Tamayo-Lomas L.;Tharwat S.;Tomasa T.;Uhrenholt S.;Vaissiere M.;Valent A.;Valette X.;Vanderlinden T.;Vazquez E. M.;Villamayor M. I.;Villefrance M.;Voigt I.;Wassim K.;Welte M.;Wolff G.;Wollborn J.;Zalba-Etayo B.;Zegers M.
2021-01-01
Abstract
Background: The COVID-19 pandemic has led highly developed healthcare systems to the brink of collapse due to the large numbers of patients being admitted into hospitals. One of the potential prognostic indicators in patients with COVID-19 is frailty. The degree of frailty could be used to assist both the triage into intensive care, and decisions regarding treatment limitations. Our study sought to determine the interaction of frailty and age in elderly COVID-19 ICU patients. Methods: A prospective multicentre study of COVID-19 patients ≥ 70 years admitted to intensive care in 138 ICUs from 28 countries was conducted. The primary endpoint was 30-day mortality. Frailty was assessed using the clinical frailty scale. Additionally, comorbidities, management strategies and treatment limitations were recorded. Results: The study included 1346 patients (28% female) with a median age of 75 years (IQR 72-78, range 70-96), 16.3% were older than 80 years, and 21% of the patients were frail. The overall survival at 30 days was 59% (95% CI 56-62), with 66% (63-69) in fit, 53% (47-61) in vulnerable and 41% (35-47) in frail patients (p < 0.001). In frail patients, there was no difference in 30-day survival between different age categories. Frailty was linked to an increased use of treatment limitations and less use of mechanical ventilation. In a model controlling for age, disease severity, sex, treatment limitations and comorbidities, frailty was independently associated with lower survival. Conclusion: Frailty provides relevant prognostic information in elderly COVID-19 patients in addition to age and comorbidities. Trial registration Clinicaltrials.gov: NCT04321265 , registered 19 March 2020.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11699/87193
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.
