Prevalence and Postdischarge Outcomes Associated with Frailty in Medical Inpatients: Impact of Different Frailty Definitions
We compared prevalence estimates and prognostication if frailty were defined using the face-to-face Clinical Frailty Scale (CFS) or the administrative-data-derived Hospital Frailty Risk Score (HFRS). We evaluated 489 adults from a prospective cohort study of medical patients being discharged back to the community; 276 (56%) were deemed frail (214 [44%] on the HFRS and 161 [33%] on the CFS), but only 99 (20%) met both frailty definitions (kappa 0.24, 95% CI 0.16-0.33). Patients classified as frail on the CFS exhibited significantly higher 30-day readmission/death rates, 19% versus 10% for those not frail (aOR [adjusted odds ratio] 2.53, 95% CI 1.40-4.57) and 21% versus 6% for those aged >65 years (aOR 4.31, 95% CI 1.80-10.31). Patients with HFRS-defined frailty exhibited higher 30-day readmission/death rates that were not statistically significant (16% vs 11%, aOR 1.62 [95% CI 0.95-2.75] in all adults and 14% vs 11%, aOR 1.24 [95% CI 0.58-2.83] in those aged >65 years).
© 2019 Society of Hospital Medicine
Previous studies have reported marked heterogeneity in prevalence estimates between different frailty instruments.2,9 For example, Aguayo et al. found that the prevalence of frailty in the English Longitudinal Study of Aging varied between 0.9% and 68% depending on which of the 35 frailty scales they tested were used, although the prevalence with comprehensive geriatric assessments (the gold standard) was 14.9% (and 15.3% on the CFS).9 Although frail patients are at higher risk for death and/or readmission after discharge, other investigators have also reported similar findings to ours that frailty-based risk models are surprisingly modest at predicting postdischarge readmission or death, with the C statistics ranging between 0.52 and 0.57, although the CFS appears to correlate best with the gold standard of comprehensive geriatric assessment.10-14 This is not surprising since the CFS is multidimensional and as a cumulative deficit model, it incorporates assessment of the patient’s underlying diseases, cognition, function, mobility, and mood in the assignment of their CFS level. Regardless, others15 have pointed out the need for studies such as ours to compare the validity of published frailty scales.
Despite our prospective cohort design and blinded endpoint ascertainment, there are some potential limitations to our study. First, we excluded long-term care residents and patients with foreshortened life expectancy – the frailest of the frail – from our analysis of 30-day outcomes, thereby potentially reducing the magnitude of the association between frailty and adverse outcomes. However, we were interested only in situations where clinicians were faced with equipoise about patient prognosis. Second, we assessed only 30-day readmissions or deaths and cannot comment on the impact of frailty definitions on other postdischarge outcomes (such as discharge locale or need for home care services) or other timeframes. Finally, although the association between the HFRS definition of frailty and the 30-day mortality/readmission was not statistically significant, the 95% confidence intervals were wide and thus we cannot definitively rule out a positive association.
In conclusion, considering that it had the strongest association with postdischarge outcomes and is the fastest and easiest to perform, the most useful of the frailty assessment tools for clinicians at the bedside still appears to be the CFS (both overall and in those patients who are elderly). However, for researchers who are analyzing data retrospectively or policy planners looking at health services data where the CFS was not collected, the HFRS holds promise for risk adjustment in population-level studies comparing processes and outcomes between hospitals.
Acknowledgments
The authors would like to acknowledge Miriam Fradette, Debbie Boyko, Sara Belga, Darren Lau, Jenelle Pederson, and Sharry Kahlon for their important contributions in data acquisition in our original cohort study, as well as all the physicians rotating through the general internal medicine wards at the University of Alberta Hospital for their help in identifying the patients. We also thank Dr. Simon Conroy, MB ChB PhD, University of Leicester, UK, for his helpful comments on an earlier draft of this manuscript.
Disclosures
The authors declare no conflicts of interest. All authors had access to the data and played a role in writing and revising this manuscript.
Funding
Funding for this study was provided by an operating grant from Alberta Innovates - Health Solutions. F.A.M. holds the Chair in Cardiovascular Outcomes Research at the Mazankowski Heart Institute, University of Alberta. The authors have no affiliations or financial interests with any organization or entity with a financial interest in the contents of this manuscript.
