Frailty risk prediction models in maintenance hemodialysis patients: a systematic review and meta-analysis of model performance and methodological quality
Bibliographic record
Abstract
BACKGROUND: Frailty affects outcomes in maintenance hemodialysis (MHD) patients, highlighting the need for reliable predictive tools. Despite the rise of predictive models, the clinical validity and scientific quality of these models remain unknown. OBJECTIVE: The purpose of this systematic review is to assess the clinical usefulness, predictive accuracy, and methodological quality of the current frailty risk prediction models in patients with MHD. METHODS: Databases including PubMed, Embase, Cochrane Library, CNKI, and others were comprehensively searched until August 2024. Studies that created or validated frailty risk prediction models for adult MHD patients were considered. The Newcastle-Ottawa Scale (NOS) and PROBAST were used to measure quality. The meta-analysis examined common predictive factors. RESULTS: Twelve of the 824 papers that reported 14 prediction models satisfied the inclusion criteria. The most common method was logistic regression. Frailty prevalence ranged from 17.2% to 79.2%. Age, albumin, depression, and dietary condition were among the variables that were most often found. Model performance varied considerably, with area under the curve (AUC) ranging from 0.72 to 0.998. All studies had significant methodological deficiencies. CONCLUSIONS: Existing frailty risk prediction models demonstrate potential utility but currently suffer from significant methodological flaws and limited external validation, impairing their clinical applicability. Future models should emphasize rigorous study design, standardized statistical methods, and robust external validation. Clinicians should cautiously interpret existing models while focusing on critical predictors such as age, albumin, depression, and nutrition for frailty management in MHD patients.
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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 | 0.066 | 0.148 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.046 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".