Association Between the Frailty Index Based on Laboratory Tests and All-Cause Mortality in Hospitalized Older Adults: Retrospective Cohort Study
Bibliographic record
Abstract
Background: Frailty is a common issue among hospitalized older adult patients and is associated with numerous adverse health outcomes. Assessing frailty facilitates better decision-making for treatment plans, patient placement, and discharge planning. Approximately a decade ago, the frailty index based on laboratory tests (FI-Lab) metric was introduced. Although this index has been shown in numerous studies to predict adverse medical outcomes, including mortality, it has not been extensively evaluated among patients hospitalized in internal medicine departments for diverse indications. Objective: The aim of the study was to investigate the relationship between FI-Lab at admission and all-cause mortality during hospitalization and after discharge in patients aged 65 years and older admitted for diverse clinical indications to internal medicine departments. Methods: This retrospective cohort study included patients aged 65 years and older hospitalized in the internal medicine departments of a large tertiary hospital. Data included demographic variables, comorbidity, and all-cause mortality. The FI-Lab was calculated based on 16 available blood tests, as well as blood pressure and heart rate measurements. We used Cox proportional hazards regression models to evaluate associations with mortality. Model performance was assessed using the C-index and time-dependent receiver operating characteristic (ROC) curves. Hospitalization data were collected from December 25, 2016, to January 7, 2023. Results: During the study period, 31,443 patients were hospitalized in internal medicine departments, and FI-Lab was calculable for 31,398 of them. The mean age of the patients was 77.6 (SD 8.2) years, and 52.1% (16,346/31,443) were women. The mean FI-Lab score was 0.38 (SD 0.15). Based on FI-Lab scores, patients were categorized into 4 groups: robust, mildly prefrail, moderately prefrail, and frail. After adjusting for age, sex, and comorbidities, frail and prefrail patients exhibited higher mortality rates than robust patients. For each 0.01 increase in the FI-Lab score (as a continuous variable), adjusted analyses revealed a 5.5% increase in in-hospital mortality, a 2.9% increase in mortality within the first year after hospitalization, and a 1.9% increase in mortality beyond the first year. Conclusions: The FI-Lab is a readily available and informative metric of frailty in older hospitalized patients. Calculating this index can assist physicians with identifying patients at high risk of mortality and provide meaningful information to support clinical decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".