Cystatin C as Predictor of Long-Term Mortality in Elderly: a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Prediction of mortality in growing aged population will offer several benefits for health sector. Cystatin C, which has long been known as biomarker to more accurately evaluate glomerular filtration rate in elderly, has also been shown to predict mortality from several studies. Studies regarding its predictive ability were vastly varied, and there has not been systematic review to examine its ability in predicting long-term mortality in elderly population. This study aimed to evaluate cystatin C performance as predictor for all-cause and cardiovascular mortality among elderly population. A systematic review of prospective cohort studies was performed. Literature searching was done in major databases such as PubMed, Cochrane, Scopus, EBSCOhost, and ProQuest. Manual searching was also performed. Inclusion criteria were studies involving elderly age 65 or older, cystatin C serum levels available, all-cause mortality as outcome, and 5-year minimum of follow-up. Study selection was performed according to PRISMA algorithm. Newcastle–Ottawa scale for cohort study was used to assess primary studies’ quality and risk of bias. Study results were presented in descriptive tables and forest plot. Initial searching revealed 609 hits with 12 studies eligible for the review: five studies evaluated all-cause mortality, three studies evaluated cardiovascular mortality, and four studies evaluated both. Meta-analysis showed that high cystatin C levels are increasing risk of long-term all-cause mortality [(HR: 1.74 (95% CI: 1.48–2.04); p < 0.0001)] and cardiovascular mortality [HR: 2.01 (95% CI: 1.63–2.47); p < 0.0001)]. The prognostic ability of cystatin C was considerably moderate [AUC 0.70 (95% CI: 0.68–0.72); p = 0.02]. Cystatin C was able to predict long-term mortality in elderly population.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".