Evaluation of the FI-Lab, a laboratory-based, automated frailty index for acute care: A multicohort study
11 affiliations
- 1Department of Biostatistics and Health Informatics, King’s College London, London, United Kingdom
- 2Division of Geriatric Medicine, Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada
- 3Department of Endocrinology, St George's Healthcare NHS Trust, London, United Kingdom
- 4School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
- 5Department of Clinical Gerontology, King’s College Hospital, London, United Kingdom
- 6Department of Diabetes, Kings College Hospital, London, United Kingdom
- 7School of Biosciences, University of Surrey, Guildford, United Kingdom
- 8Institute of Health Informatics, University College London, London, United Kingdom
- 9Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom
- 10Oncology DepartmentWestern General HospitalEdinburgh, United Kingdom
- 11Department of Neurology, King’s College Hospital, London, United Kingdom
Abstract
Background: Laboratory-based frailty indices (FI-Lab) have shown promise in geriatric medicine research. We aimed, in diverse samples, to determine the optimal construction of an FI-Lab for acute care and evaluate its validity as a measure of latent health status across the adult life span. Methods and findings: Our retrospective multicohort study used emergency department encounters in Boston, USA (MIMIC-IV-ED; 2011–2019) and London, UK (King’s College Hospital (KCH); 2017–2020); and a community-based cohort in the UK (UK Biobank; 2006–2010). We evaluated FI-Lab configurations by varying test selection strategies, number of tests, and minimum test thresholds. Sensitivity analyses examined performance across age, ethnicities, sexes, and model types to identify potential blind spots. The primary outcome was 1-year all-cause mortality. Conclusions: The FI-Lab provides an automated, scalable measure of patient vulnerability that is robust across healthcare settings and populations. A core set of 20–40 commonly ordered tests is sufficient for signal capture. It offers a pragmatic complement to clinical judgement without requiring manual data entry or additional tests. Why was this study done?: What did the researchers do and find?: What do these findings mean?: In this multicohort study, Hugh Logan Ellis and colleagues optimize the laboratory-based frailty index for use as a general measure of illness severity across all adult ages and different healthcare settings.
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BibTeX
@article{ellis2026evaluation,
title = {{Evaluation of the FI-Lab, a laboratory-based, automated frailty index for acute care: A multicohort study}},
author = {Hugh Logan Ellis and Peter Hanlon and Liam Dunnell and Martin Whyte and Daniel H. J. Davis and Josephine Bates and Adeel Jafri and Diana Shamsutdinova and James T. Teo and Zina Ibrahim and Kenneth Rockwood},
journal = {PLOS Medicine},
year = {2026},
month = sep,
doi = {10.1371/journal.pmed.1005004},
url = {https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005004},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Evaluation of the FI-Lab, a laboratory-based, automated frailty index for acute care: A multicohort study
AU - Hugh Logan Ellis
AU - Peter Hanlon
AU - Liam Dunnell
AU - Martin Whyte
AU - Daniel H. J. Davis
AU - Josephine Bates
AU - Adeel Jafri
AU - Diana Shamsutdinova
AU - James T. Teo
AU - Zina Ibrahim
AU - Kenneth Rockwood
PY - 2026
DA - 2026/09/01
JO - PLOS Medicine
DO - 10.1371/journal.pmed.1005004
UR - https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005004
AB - Background: Laboratory-based frailty indices (FI-Lab) have shown promise in geriatric medicine research. We aimed, in diverse samples, to determine the optimal construction of an FI-Lab for acute care and evaluate its validity as a measure of latent health status across the adult life span. Methods and findings: Our retrospective multicohort study used emergency department encounters in Boston, USA (MIMIC-IV-ED; 2011–2019) and London, UK (King’s College Hospital (KCH); 2017–2020); and a community-based cohort in the UK (UK Biobank; 2006–2010). We evaluated FI-Lab configurations by varying test selection strategies, number of tests, and minimum test thresholds. Sensitivity analyses examined performance across age, ethnicities, sexes, and model types to identify potential blind spots. The primary outcome was 1-year all-cause mortality. Conclusions: The FI-Lab provides an automated, scalable measure of patient vulnerability that is robust across healthcare settings and populations. A core set of 20–40 commonly ordered tests is sufficient for signal capture. It offers a pragmatic complement to clinical judgement without requiring manual data entry or additional tests. Why was this study done?: What did the researchers do and find?: What do these findings mean?: In this multicohort study, Hugh Logan Ellis and colleagues optimize the laboratory-based frailty index for use as a general measure of illness severity across all adult ages and different healthcare settings.
SN - 1549-1676
ER -