Retrace-Clustering Creatinine Trajectory and Baseline Features for Predicting ESKD
Bibliographic record
Abstract
Background: Patients with ANCA-associated vasculitis (AAV) may experience end-stage kidney disease (ESKD) and mortality. We aim to investigate the connection between the longitudinal trajectory of creatinine and the occurrence of ESKD and mortality. Methods: We included patients with a minimum of two creatinine measurements, encompassing the baseline period and the following six months (Figure). Creatinine trajectories were formulated using six months of creatinine readings from AAV patients with kidney involvement. We excluded patients who presented with end-stage kidney disease (ESKD). In instances where a patient had multiple creatinine values within a given month, the average of those values was employed. Percentage delta creatinine values, representing the percentage difference between a creatinine value and its baseline, were then calculated. The K-means algorithm for longitudinal data was employed to cluster the creatinine trajectories of AAV patients. The quality of clustering was evaluated using the Calinski-Harabasz Index. We conducted a time-to-event analysis for ESKD and death, assessing the survival rates of the clusters over a five-year follow-up period through Kaplan-Meier Survival analysis. Results: The study incorporates 273 patients with >1 creatinine values, amounting to a total of 2022 creatinine readings. We identified three renal trajectory groups: A-Stable (140), B-Recovered (N=100), and C- Declining (N=33). The baseline features varied across clusters, specifically in terms of baseline creatinine (284uM, 390uM and 151uM respectively, p<0.001) and ENT involvement (p=0.001). When considering the composite outcome of ESKD and death, Cluster A exhibited a 3-year incidence rate of 23%, Cluster B at 8%, and Cluster C at 28% (p<0.0069). Conclusion: Trajectory clustering allows for the identification of patients who may require closer monitoring, or targeted interventions based on their cluster assignment and associated risk profile.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".