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Lancet. 2018 May 5;391(10132):1775-1782. doi: 10.1016/S0140-6736(18)30668-8. Epub 2018 Apr 26.

Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study.

Author information

1
Department of Geriatric Medicine, Lyon Teaching Hospital, Lyon, France.
2
The Nuffield Trust, London, UK.
3
Department of Business Intelligence, Manchester University NHS Foundation Trust, Manchester, UK.
4
Department of Public Health, Cardiff University, Cardiff, UK.
5
Department of Health Policy, London School of Economics, London, UK.
6
Institute for Ageing, Newcastle University, Newcastle, UK.
7
Academic Geriatric Medicine, University of Southampton, Southampton, UK.
8
Data Analytics Team, The Health Foundation, London, UK.
9
Department of Health Sciences, College of Life Sciences, University of Leicester, Leicester, UK. Electronic address: spc3@le.ac.uk.

Abstract

BACKGROUND:

Older people are increasing users of health care globally. We aimed to establish whether older people with characteristics of frailty and who are at risk of adverse health-care outcomes could be identified using routinely collected data.

METHODS:

A three-step approach was used to develop and validate a Hospital Frailty Risk Score from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes. First, we carried out a cluster analysis to identify a group of older people (≥75 years) admitted to hospital who had high resource use and diagnoses associated with frailty. Second, we created a Hospital Frailty Risk Score based on ICD-10 codes that characterised this group. Third, in separate cohorts, we tested how well the score predicted adverse outcomes and whether it identified similar groups as other frailty tools.

FINDINGS:

In the development cohort (n=22 139), older people with frailty diagnoses formed a distinct group and had higher non-elective hospital use (33·6 bed-days over 2 years compared with 23·0 bed-days for the group with the next highest number of bed-days). In the national validation cohort (n=1 013 590), compared with the 429 762 (42·4%) patients with the lowest risk scores, the 202 718 (20·0%) patients with the highest Hospital Frailty Risk Scores had increased odds of 30-day mortality (odds ratio 1·71, 95% CI 1·68-1·75), long hospital stay (6·03, 5·92-6·10), and 30-day readmission (1·48, 1·46-1·50). The c statistics (ie, model discrimination) between individuals for these three outcomes were 0·60, 0·68, and 0·56, respectively. The Hospital Frailty Risk Score showed fair overlap with dichotomised Fried and Rockwood scales (kappa scores 0·22, 95% CI 0·15-0·30 and 0·30, 0·22-0·38, respectively) and moderate agreement with the Rockwood Frailty Index (Pearson's correlation coefficient 0·41, 95% CI 0·38-0·47).

INTERPRETATION:

The Hospital Frailty Risk Score provides hospitals and health systems with a low-cost, systematic way to screen for frailty and identify a group of patients who are at greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful.

FUNDING:

National Institute for Health Research.

PMID:
29706364
PMCID:
PMC5946808
DOI:
10.1016/S0140-6736(18)30668-8
[Indexed for MEDLINE]
Free PMC Article

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