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Developing and Evaluating a Laboratory-Based Frailty Index for the Prediction of Long-Term Health Outcomes in Systemic Lupus Erythematosus

2025· article· en· W4411846607 on OpenAlexaffvenue
Grace L. Burns, Alexandra Legge

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineIndex (typography)Lupus erythematosusFrailty IndexSystemic lupusIntensive care medicineInternal medicineImmunologyDisease

Abstract

fetched live from OpenAlex

Objectives Frailty is a useful measure of health status in systemic lupus erythematosus (SLE), but it is not routinely captured in existing SLE datasets. In other populations, frailty indices constructed exclusively from laboratory data have been shown to predict adverse health outcomes. We aimed to construct and evaluate the first laboratory-based frailty index (FI-Lab) in SLE. Additionally, we compared the FI-Lab to an existing clinical frailty index (SLICC-FI) with respect to the prediction of future health outcomes. Methods This study used existing data from a single-center prospective cohort of adult SLE patients followed annually with standardized assessments. We included the first study visit for each patient occurring between 2010 and 2019, with follow-up data available up to June 2024. A 30-item FI-Lab was constructed by adapting the list of laboratory variables identified by Ellis et al.[1] Cox proportional hazards regression was used to examine the association between baseline FI-Lab scores and all-cause mortality risk, while negative binomial regression was used to evaluate the association of baseline FI-Lab scores with organ damage accrual during follow-up. To compare the performance of models containing the FI-Lab and/or SLICC-FI as predictor variables, we used Akaike information criterion (AIC), Harrell’s C-statistics, and pseudo-R2 values. Results The 283 included patients (89% female) had a mean (SD) age of 47.7 (15.1) years and a median (IQR) disease duration of 8.3 (2.6-19.8) years at baseline. The 97 patients classified as frail at baseline (based on FI-Lab scores > 0.21) had increased mortality risk [hazard ratio 3.71; 95% CI 1.82-7.54] and a higher rate of organ damage accrual during follow-up [incidence rate ratio 2.26; 95% CI 1.59-3.22] compared to non-frail patients. A weak correlation existed between baseline FI-Lab and SLICC-FI scores (rs=0.37, p < 0.0001). In unadjusted analysis, higher baseline FI-Lab and SLICC-FI scores were both associated with increased mortality risk during follow-up. However, after multivariable adjustment, only the FI-Lab maintained a significant association with mortality risk (Table 1). Both the FI-Lab and the SLICC-FI were significant baseline predictors of organ damage accrual during follow-up, and the multivariable model that included both frailty measures was superior to the models containing either the FI-Lab or the SLICC-FI alone. Table 1. Association of baseline FI-Lab and SLICC-FI scores with mortality risk and organ damage accrual during follow-up (n=274). Conclusion An FI constructed from routinely collected laboratory variables can measure frailty and predict future health outcomes in SLE. The FI-Lab may serve as a convenient screening tool to detect subclinical deficit accumulation and promote early risk mitigation among SLE patients. [1.] Ellis HL. CMAJ 2020;192(1):E3-E8.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.058
GPT teacher head0.383
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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