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Evaluation of the FI-Lab, a laboratory-based, automated frailty index for acute care: A multicohort study

by Hugh Logan Ellis, Peter Hanlon, Liam Dunnell, Martin Whyte, Daniel H. J. Davis, Josephine Bates, Adeel Jafri, Diana Shamsutdinova, James T. Teo, Zina Ibrahim, Kenneth Rockwood 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…

A recent study has investigated the effectiveness of a laboratory-based frailty index known as the FI-Lab in acute care settings. This multicohort study aimed to determine the optimal configuration of an FI-Lab and evaluate its ability to measure latent health status across the entire adult life span.

The researchers analyzed emergency department encounters in Boston, USA (MIMIC-IV-ED; 2011-2019) and London, UK (King's College Hospital; 2017-2020), as well as a community-based cohort in the UK (UK Biobank; 2006-2010). The primary outcome of interest was 1-year all-cause mortality.

The study found that a FI-Lab constructed from 25 commonly ordered tests, with a minimum threshold of 15 tests, demonstrated hazard ratios for 1-year mortality that were nearly identical to those of chronological age and surpassed the National Early Warning Score 2 (NEWS2). In combined models, each standard deviation increase in the FI-Lab score was associated with a 2.00-fold higher hazard ratio for 1-year mortality (95% CI [1.99, 2.09]; p < 0.001).

The findings suggest that the FI-Lab offers an automated and scalable measure of patient vulnerability that is robust across different healthcare settings and populations. The study concludes that a core set of 20-40 commonly ordered tests is sufficient for capturing meaningful signals. This automated approach provides a pragmatic complement to clinical judgment, eliminating the need for manual data entry or additional tests.

Written by urgent.news from PLOS Medicine's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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