Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/31090
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dc.contributor.authorSubramaniam, Ashwin-
dc.contributor.authorUeno, Ryo-
dc.contributor.authorTiruvoipati, Ravindranath-
dc.contributor.authorDarvall, Jai-
dc.contributor.authorSrikanth, Velandai-
dc.contributor.authorBailey, Michael-
dc.contributor.authorPilcher, David-
dc.contributor.authorBellomo, Rinaldo-
dc.date2022-
dc.date.accessioned2022-11-04T05:05:28Z-
dc.date.available2022-11-04T05:05:28Z-
dc.date.issued2022-10-
dc.identifier.citationCritical Care Explorations 2022; 4(10): e0777en
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/31090-
dc.description.abstractThe Clinical Frailty Scale (CFS) is the most used frailty measure in intensive care unit (ICU) patients. Recently, the modified frailty index (mFI), derived from 11 comorbidities has also been used. It is unclear to what degree the mFI is a true measure of frailty rather than comorbidity. Furthermore, the mFI cannot be freely obtained outside of specific proprietary databases. To compare the performance of CFS and a recently developed International Classification of Diseases-10 (ICD-10) mFI (ICD-10mFI) as frailty-based predictors of long-term survival for up to 1 year. A retrospective multicentric observational study. All adult (≥16 yr) critically ill patients with documented CFS scores admitted to sixteen Australian ICUs in the state of Victoria between April 1, 2017 to June 30, 2018 were included. We used probabilistic methods to match de-identified ICU admission episodes listed in the Australia and New Zealand Intensive Care Society Adult Patient Database with the Victorian Admission Episode Dataset and the Victorian Death Index via the Victorian Data Linkage Centre. The primary outcome was the longest available survival following ICU admission. We compared CFS and ICD-10mFI as primary outcome predictors, after adjusting for key confounders. The CFS and ICD-10mFI were compared in 7,001 ICU patients. The proportion of patients categorized as frail was greater with the CFS than with the ICD-10mFI (18.9% [n = 1,323] vs. 8.8% [n = 616]; p < 0.001). The median (IQR) follow-up time was 165 (82-276) days. The CFS predicted long-term survival up to 6 months after adjusting for confounders (hazard ratio [HR] = 1.26, 95% CI, 1.21-1.31), whereas ICD-10mFI did not (HR = 1.04, 95% CI, 0.98-1.10). The ICD-10mFI weakly correlated with the CFS (Spearman's rho = 0.22) but had a poor agreement (kappa = 0.06). The ICD-10mFI more strongly correlated with the Charlson comorbidity index (Spearman's rho 0.30) than CFS (Spearman's rho = 0.25) (p < 0.001). CFS, but not ICD-10mFI, predicted long-term survival in ICU patients. ICD-10mFI correlated with co-morbidities more than CFS. These findings suggest that CFS and ICD-10mFI are not equivalent. CFS and ICD-10mFI are not equivalent in screening for frailty in critically ill patients and therefore ICD-10mFI in its current form should not be used.en
dc.language.isoeng
dc.subjectClinical Frailty Scaleen
dc.subjectDelphi consensusen
dc.subjectICD-10mFIen
dc.subjectInternational Classification of Diseases 10th Revisionen
dc.subjectLong-term survivalen
dc.subjectModified frailty indexen
dc.titleComparing the Clinical Frailty Scale and an International Classification of Diseases-10 Modified Frailty Index in Predicting Long-Term Survival in Critically Ill Patients.en
dc.typeJournal Articleen
dc.identifier.journaltitleCritical Care Explorationsen
dc.identifier.affiliationIntensive Careen
dc.identifier.affiliationDepartment of Intensive Care, Alfred Hospital, Melbourne, VIC, Australiaen
dc.identifier.affiliationCentre for Outcome and Resource Evaluation, Australian and New Zealand Intensive Care Society, Melbourne, VIC, Australiaen
dc.identifier.affiliationDepartment of Intensive Care, Peninsula Health, Frankston, VIC, Australiaen
dc.identifier.affiliationDivision of Medicine, Peninsula Clinical School, Monash University, Frankston, VIC, Australiaen
dc.identifier.affiliationAustralian and New Zealand Intensive Care Research Centre (ANZIC RC), Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australiaen
dc.identifier.affiliationDepartment of Intensive Care, Eastern Health, Box Hill, VIC, Australiaen
dc.identifier.affiliationDepartment of Intensive Care, Royal Melbourne Hospital, Melbourne, VIC, Australiaen
dc.identifier.affiliationCentre for Integrated Critical Care, The University of Melbourne, Melbourne, VIC, Australiaen
dc.identifier.affiliationDepartment of Geriatric Medicine, Peninsula Health, Frankston, VIC, Australiaen
dc.identifier.affiliationNational Centre for Healthy Ageing, Melbourne, VIC, Australiaen
dc.identifier.doi10.1097/CCE.0000000000000777en
dc.type.contentTexten
dc.identifier.pubmedid36259062
local.name.researcherBellomo, Rinaldo
item.grantfulltextnone-
item.openairetypeJournal Article-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.deptIntensive Care-
crisitem.author.deptData Analytics Research and Evaluation (DARE) Centre-
Appears in Collections:Journal articles
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