Association of the clinical frailty scale with hospital outcomes

QJM. 2015 Dec;108(12):943-9. doi: 10.1093/qjmed/hcv066. Epub 2015 Mar 15.


Background: The clinical frailty scale (CFS) was validated as a predictor of adverse outcomes in community-dwelling older people. In our hospital, the use of the CFS in emergency admissions of people aged ≥ 75 years was introduced under the Commissioning for Quality and Innovation payment framework.

Aim: We retrospectively studied the association of the CFS with patient characteristics and outcomes.

Design: Retrospective observational study in a large tertiary university National Health Service hospital in UK.

Methods: The CFS was correlated with transfer to specialist Geriatric ward, length of stay (LOS), in-patient mortality and 30-day readmission rate.

Results: Between 1st August 2013 and 31st July 2014, there were 11 271 emergency admission episodes of people aged ≥ 75 years (all specialties), corresponding to 7532 unique patients (first admissions); of those, 5764 had the CFS measured by the admitting team (81% of them within 72 hr of admission). After adjustment for age, gender, Charlson comorbidity index and history of dementia and/or current cognitive concern, the CFS was an independent predictor of in-patient mortality [odds ratio (OR) = 1.60, 95% confidence interval (CI): 1.48 to 1.74, P < 0.001], transfer to Geriatric ward (OR = 1.33, 95% CI: 1.24 to 1.42, P < 0.001) and LOS ≥ 10 days (OR = 1.19, 95% CI: 1.14 to 1.23, P < 0.001). The CFS was not a multivariate predictor of 30-day readmission.

Conclusions: The CFS may help predict in-patient mortality and target specialist geriatric resources within the hospital. Usual hospital metrics such as mortality and LOS should take into account measurable patient complexity.

Publication types

  • Observational Study

MeSH terms

  • Aged
  • Aged, 80 and over
  • Emergencies
  • England
  • Female
  • Frail Elderly*
  • Geriatric Assessment / methods*
  • Health Status Indicators*
  • Hospitalization / statistics & numerical data*
  • Humans
  • Length of Stay / statistics & numerical data
  • Male
  • Outcome Assessment, Health Care
  • Patient Readmission / statistics & numerical data
  • Prognosis
  • ROC Curve
  • Retrospective Studies