Imperial College London
Alternate

Emeritus ProfessorDerekBell

Faculty of Medicine, School of Public Health

Emeritus Professor in Acute Medicine
 
 
 
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Contact

 

+44 (0)7886 725 212d.bell

 
 
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Assistant

 

Miss Heather Barnes +44 (0)20 3315 8144

 
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Location

 

Chelsea and Westminster HospitalChelsea and Westminster Campus

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Summary

 

Publications

Citation

BibTex format

@article{Soong:2020:10.7861/clinmed.2019-0249,
author = {Soong, JT and Rolph, G and Poots, AJ and Bell, D},
doi = {10.7861/clinmed.2019-0249},
journal = {Clinical medicine (London, England)},
pages = {183--188},
title = {Validating a methodology to measure frailty syndromes at hospital level utilising administrative data.},
url = {http://dx.doi.org/10.7861/clinmed.2019-0249},
volume = {20},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BACKGROUND: Identifying older people with clinical frailty, reliably and at scale, is a research priority. We measured frailty in older people using a novel methodology coding frailty syndromes on routinely collected administrative data, developed on a national English secondary care population, and explored its performance of predicting inpatient mortality and long length of stay at a single acute hospital. METHODOLOGY: We included patient spells from Secondary User Service (SUS) data for those ≥65 years with attendance to the emergency department or admission to West Middlesex University Hospital between 01 July 2016 to 01 July 2017. We created eight groups of frailty syndromes using diagnostic coding groups. We used descriptive statistics and logistic regression to explore performance of diagnostic coding groups for the above outcomes. RESULTS: We included 17,199 patient episodes in the analysis. There was at least one frailty syndrome present in 7,004 (40.7%) patient episodes. The resultant model had moderate discrimination for inpatient mortality (area under the receiver operating characteristic curve (AUC) 0.74; 95% confidence interval (CI) 0.72-0.76) and upper quartile length of stay (AUC 0.731; 95% CI 0.722-0.741). There was good negative predictive value for inpatient mortality (98.1%). CONCLUSIONS: Coded frailty syndromes significantly predict outcomes. Model diagnostics suggest the model could be used for screening of elderly patients to optimise their care.
AU  - Soong,JT
AU  - Rolph,G
AU  - Poots,AJ
AU  - Bell,D
DO  - 10.7861/clinmed.2019-0249
EP  - 188
PY  - 2020///
SN  - 1470-2118
SP  - 183
TI  - Validating a methodology to measure frailty syndromes at hospital level utilising administrative data.
T2  - Clinical medicine (London, England)
UR  - http://dx.doi.org/10.7861/clinmed.2019-0249
UR  - https://www.ncbi.nlm.nih.gov/pubmed/32188656
UR  - https://www.rcpjournals.org/content/clinmedicine/20/2/183
UR  - http://hdl.handle.net/10044/1/77738
VL  - 20
ER  -
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