#4781 FRAILTY IN PERITONEAL DIALYSIS: PREVALENCE AND PREDICTION FACTORS
Bibliographic record
Abstract
Abstract Background and Aims Frailty is a clinical syndrome characterized by a state of increased vulnerability and risk of adverse outcomes following a stress, which exerts a heavy economic and social burden. The identification of this syndrome is done throught validated tools. Although frailty is associated with advanced age, certain conditions that produce age-like changes can lead to a state of frailty at younger ages. The presence of multiple comorbidities increases frailty risk. Also, low levels of albumin, even in the normal range, have been related to greater frailty and its levels have been used to assess frailty. Chronic kidney disease is associated with higher prevalence of frailty. However, little has been reported about frailty prevalence in peritoneal dialysis population. Our aim was to access the prevalence of frail and vulnerable PD patients using the Edmonton Frailty Scale (EFS) and to identify prediction factors. Method In a retrospective cohort study, we assessed frailty in PD patients from 2 center in Portugal by a validated frailty score (Edmonton Frailty Scale-EFS). We also collected information that could contribute to frailty in these patients. Linear and logistic regressions were used to access frailty predictors. Receiver operating characteristics (ROC) curve was used to access accuracy of those predictors. Results We included 74 PD patients, 51,5% male, mean age 53,9 ± 15,1 years, median body max index 25,2 ± 4,3 Kg/m2. Median CCI was 4 [interquartile range (IQR) of 3]. Fifty-three patients (71,6%) were classified as robust (non-frail), 11 as vulnerable, and 10 as frail (8 mildly and 2 moderate). Patients were divided into two groups: A group included robust (non-frail) patients; B group included vulnerable and frail ones. Age, sex distribution, IMC, diabetes mellitus prevalence, variables related to PD (efficacy, vintage, modality and complications), and levels of hemoglobin, phosphorus, potassium and C-reactive protein were similar between the groups. Non frail patients presented significantly higher albumin levels and lower CCI (p-value < 0,05). A linear regression was performed to ascertain the effects of CCI and albumin: CCI was an independent predictor for frailty/vulnerability, accessed by EFS (for each point in CCI, there was an increase of 0,5 points in EFS). Albumin did not show a significant effect defining frailty in our sample. ROC curve showed a high accuracy for CCI to identify frail/ vulnerable patients (AUC 0,817). Conclusion In our sample, CCI was an independent factor for frailty/vulnerability classified through EFS. In fact, it had a high accuracy to identify those patients, with a high sensibility. This means that CCI could be used as an acceptable screening test. However, the prevalence of frail/vulnerable patients was low in our sample. A larger one will allow to determine confirm if certain comorbidities, such as diabetes mellitus, or variables related to dialysis technique, are correlated with frailty. Furthermore, a larger sample would be essential to confirm whether albumin is, after all, a good predictor of frailty in the PD population, which was not confirmed in this work.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".