FP661ASSOCIATION OF HOSPITALIZATION AND MORTALITY WITH FRAILTY AND COMORBIDITY INDEXES IN PATIENTS ON HEMODIALYSIS
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Frailty has been defined as a syndrome or a state of increased vulnerability resulting from a decline in biological functional reserves, such that the ability to cope with stressors is compromised, and leading to higher risk of poor health outcomes, disability, hospitalization and death. Our objective was to relate frailty - as measured by the Fried Phenotype Frail Index (FPFI) and the Edmonton Frail Scale (EFS) - and comorbidity - as measured by the Charlson comorbidity index (CCI) - with the annual hospitalization rate, number of days at hospital and mortality rate in a cohort of hemodialysis prevalent patients. METHODS: Frailty and comorbidity were measured by using the FPFI, EFS and CCI and admissions to hospital, number of days at hospital and final outcomes (death, kidney transplantation or loss to follow-up) were recorded for the hemodialysis prevalent patients managed in an sanitary area. The study included 275 hemodialysis patients: 61.5% men, 57.5% diabetics, median age 65 years, and the mean time in hemodialysis was 50.4 months. The mean follow-up time was 10.84 months; 215 patients completed the 12-month follow-up; 42 died before completing it, 16 received kidney transplantation and 2 were transfered. RESULTS: The proportion of frail patients was 41.1% as measured with the FPFI and 29.5% as measured with the EFS; 65% of patients presented CCI higher than 5, which is high or very high; 151 hospital admissions were recorded for 103 patients, with a total of 1403 and a mean of 9.29 days at hospital.Hospitalization and frailty according to the FPFI: 70 admissions in 47 patients out of 162 non-frail ones (FPFI) and 81 admissions in 56 patients out of 113 frail ones (FPFI). The annual hospitalization rate and number of days at hospital were significantly different between non-frail and frail patients: 0.55 vs. 0.99 p<0.001 and 4.38 vs. 10.50 p<0.001. Hospitalization and frailty according to the EFS: 91 admissions in 59 patients out of 194 non-frail ones (EFS) and 60 admissions in 44 patients out of 81 frail ones (EFS). The annual hospitalization rate and number of days at hospital were significantly different between non-frail and frail patients: 0.61 vs. 1.02 p<0.001 and 4.58 vs. 12.45 p<0.001. Hospitalization and CCI >5: 45 admissions in 31 patients out of 97 ones with CCI≤5 and 106 admissions in 72 patients out of 178 with CCI>5. The annual hospitalization rate and number of days at hospital were not significantly different between both groups 0.55 vs. 0.83 (n.s.) and 4.09 vs. 8.45 p=0.059. Regarding mortality, 11.1% non-frail patients and 21.2% frail ones died, according to the FPFI p<0.05; 10.3% non-frail patients and 27.2% frail ones according to the EFS p<0.005; and 10.4% patients with CCI ≤5 versus 18% patients with CCI>5 (n.s.). In a multivariate analysis, where other variables were included, such as sex, age, diabetes, time in dialysis, serum albumin and serum phosphorus, only frailty measured by the EFS was associated with mortality. CONCLUSIONS: In conclusion, higher hospitalization rate and number of days at hospital were demonstrated in patients classified as frail by the FPFI and EFS, with stronger association than that of the Charlson comorbidity index. Results also suggest that the frailty indexes could be even better predictors of mortality than the comorbidity index.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".