Impact of Biomarkers in the Eligibility Criteria of AKI Randomized Controlled Trials: A Systematic Review with Meta-Analysis
Bibliographic record
Abstract
Background: There is a growing interest around novel approaches for assessing acute kidney injury (AKI). Some clinical trials have used kidney biomarkers as part of their eligibility criteria to better select participants. This might cause a selection bias, as the expected event rate used to measure the sample size is based on prior studies without such criteria. Methods: We searched for studies published after 2010 in MEDLINE, EMBASE, GOOGLE Scholar, EBM Reviews, MedRxiv and PROSPERO. We included RCTs with biomarkers as eligibility criteria that assess kidney outcomes, defined as the incidence or composite of acute kidney injury, major adverse renal or cardiac events, initiation of dialysis or death. The main aim of this review was to quantify the discrepancy between the anticipated (used in sample size estimation) and the observed event rates for control and intervention groups. Results: A total of 14 RCTs involving 3817 patients were included. Biomarkers of interest were NGAL (4 studies), TIMP-2*IGFBP7 (3 studies), proteinuria (3 studies), albumin, NT-pro-BNP, homocysteine and uric acid. The mean risk difference between the anticipated and observed event rates was 0.098 (SD±0.115, p=0.58) for control groups and 0.116 (SD±0.106, p<0.01) for intervention groups. For RCTs with tubular damage biomarkers (NGAL, TIMP-2*IGFBP7 and proteinuria), the mean risk differences were 0.118 (SD±0.130, p=0.46) and 0.154 (SD±0.103, p=0.008) for the control and intervention groups respectively. Conclusion: The use of biomarkers as an enrichment method for selecting participants in AKI RCTs is associated with a difference between expected and observed incidence rates. Investigators must consider the implications of integrating such criteria on the event rate of RCTs. Funding: Clinical Revenue Support
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.104 | 0.088 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".