Frailty as a Predictor of Negative Health Outcomes in Chronic Kidney Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: To perform a comprehensive evidence synthesis to summarize the impact of frailty on health outcomes in patients with chronic kidney disease (CKD). Design: Systematic reviews and meta-analysis. Setting: Electronic searches in PubMed, Embase, Web of Science, CNKI, VIP, CBM, and Wanfang Database were performed. The methodological quality was evaluated using the Newcastle Ottawa Scale (NOS). Participants: Patients with chronic kidney disease (CKD). Measurements: Potential clinical outcomes due to frailty. Results: Eighteen cohort studies incorporating a total of 22,788 participants were included. The overall risk of bias was low. The median reported prevalence of frail and prefrail individuals with CKD was 41.8% (range 2.8-81.5%) and 43.9% (range 19.1-62.7%), respectively. Prefrailty and frailty related to mortality indicated an increased hazard ratio (HR), with a pooled HR of 1.68 [95% confidence interval (CI) 1.46-1.94P<01] and 1.48 (95% CI 1.21-1.81P<01), respectively. Prefrailty and frailty related to hospitalization with the pooled HR/risk ratio (RR) of 1.56 (95% CI 1.37-1.76P<01) and 1.21 (95% CI 0.79-1.85P = .38), respectively. Similarly, the pooled HR demonstrated a strong correlation between frailty and falls in patients with CKD with HR 1.83 (95% CI 1.40-2.37P<01) and no statistical correlation between prefrailty and falls in these patients with pooled HR 1.19 (95% CI 0.44-3.22P = .73), respectively. Conclusions and Implications: Frailty is predictive of negative outcomes in patients with CKD, including all-cause mortality, all-cause hospitalization, and falls. Therefore, frailty should be routinely assessed among patients with CKD to prevent poor prognosis, reduce fatality rate, and provide evidence to support future targeted interventions. However, because of the limited amount of information currently in the literature, additional prospective studies are needed to explore the role of prefrailty in predicting adverse outcomes for patients with CKD. (C) 2020 AMDA - The Society for Post-Acute and Long-Term Care Medicine.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".