#2711 Geriatric assessment use to frailty prediction in older CKD patients
Bibliographic record
Abstract
Abstract Background and Aims Frailty is common among chronic kidney disease (CKD) patients. The 2024 KDIGO guidelines recommend identifying frailty as a key factor in CKD management. The Comprehensive Geriatric Assessment (CGA) is the gold-standard methodology used by geriatricians to define frailty. Our aim was to identify predictors of frailty in CKD patients, using CGA methodology. Method We evaluated 204 patients aged ≥65 years in 2024: 111 with CKD stages 3 to 5, 71 on dialysis, and 22 receiving conservative care in a single nephrology unit. A multidisciplinary team, including a nephrologist specialized in geriatrics, conducted the CGA. Data collection included demographics and clinical parameters, as well as assessments of activities of daily living (ADL), cognitive function, nutritional status, mobility, and social support. Statistical analysis was performed using SPSS v.29. Results Among the 204 patients, the mean age was 79.1 (±7.2) years, 58.2% were male, 32.7% were widowed, and 28% were institutionalized, 52.5% had diabetes, and 27.7% had heart failure. Overall, 25% of patients were classified as frail based on the CGA. Frailty prevalence varied depending on the assessment tool used: 26.5% according to the Fried Index (FI >3), 23.5% according to the Edmonton Frailty Scale (EFS >8), and 58.3% according to the Clinical Frailty Scale (CFS >5). Frailty prevalence also differed across CKD stages (Table 1). Logistic regression identified four significant predictors of frailty: activities of daily livings [(Barthel Index (Exp(B) = 0.191, P < 0.001), Lawton and Brody Scale (Exp(B) = 0.145, P = 0.001)], mobility [Short Physical Performance Battery (SPPB) (Exp(B) = 0.575, P = 0.001)] and depression (Exp(B) = 21.057, P = 0.013). The model showed an excellent fit (Nagelkerke's R² = 0.835, 92.3% accuracy). Conclusion Frailty identification has a significant impact on CKD management. Simple, time-efficient tools should be integrated into routine care to facilitate early detection and intervention. The EFS may be particularly useful due to its shorter administration time while maintaining alignment with CGA results. Identifying frailty syndromes such as impaired mobility (SPPB) and depression could help target modifiable risk factors and improve patient outcomes. Conversely, declines in ADL function, while strong frailty predictors, may indicate a stage too advanced for effective intervention. To our knowledge, this is the first study to evaluate frailty across the full CKD continuum, from early to advanced stages, and to identify predictors that may be relevant across all CKD stages.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".