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

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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. We analysed 227,736 visits (113,032 patients) from MIMIC-IV-ED; 152,305 visits (96,843 patients) from KCH; and 492,703 UK Biobank. An FI-Lab calculated from 25 commonly ordered tests (minimum 15 required) demonstrated hazard ratios for 1-year mortality approaching those of chronological age and exceeding the National Early Warning Score 2 (NEWS2). In combined models, hazard ratios for FI-Lab per standard deviation increase were 2.00 (95 %CI [1.99, 2.09]; p < 0.001) in MIMIC-IV-ED, 2.55 (95 %CI [2.42, 2.69]; p < 0.001) in KCH, and 1.87 (95% CI [1.80, 1.94]; p < 0.001) in UK Biobank. Performance was consistent across subject groupings. Discrimination plateaued at 20-40 tests. This was a retrospective study; future work exploring the impact on everyday use requires prospective evaluation, potentially in a randomised controlled trial. 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.

Abstract as published, via PubMed.