Using Unsupervised Clustering to Characterize Phenotypes Among Older Kidney Transplant Recipients: A Cohort Study
Bibliographic record
Abstract
Background: Older kidney transplant recipients have inferior outcomes compared to younger recipients, and this risk may be compounded by donor characteristics. Objective: We applied an unsupervised machine learning clustering approach to group older recipients into similar phenotypes. We evaluated the association between each cluster and graft failure, and the impact of donor quality on outcomes. Design: This is a nationally representative retrospective cohort study. Setting and Patients: Kidney transplant recipients aged ≥65 years identified from the Scientific Registry of Transplant Recipients (2000-2017). Measurements and Methods: We used unsupervised clustering to generate phenotypes using 16 recipient factors. Donor quality was evaluated using 2 approaches, including the Kidney Donor Risk Index (KDRI). All-cause graft failure was analyzed using multivariable Cox regression. Results: Overall, 16 364 patients (mean age 69 years; 38% female) were separated into 3 clusters. Cluster 1 recipients were exclusively female; cluster 2 recipients were exclusively males without diabetes; and cluster 3 recipients were males with a higher burden of comorbidities. Compared to cluster 2, the risk of graft failure was higher for cluster 3 recipients (adjusted hazard ratio [aHR] = 1.25, 95% confidence interval [CI] = 1.19-1.32). Cluster 3 recipients of a lower quality (KDRI ≥1.45) kidney had the highest risk of graft failure (aHR = 1.74, 95% CI = 1.61-1.87) relative to cluster 2 recipients of a higher quality kidney. Limitations: This study did not include an external validation cohort. The findings should be interpreted as exploratory and should not be used to inform individual risk prediction nor be applied to recipients <65 years of age. Conclusions: In a national cohort of older kidney transplant recipients, unsupervised clustering generated 3 clinically distinct recipient phenotypes. These phenotypes may aid in complementing allocation decisions, providing prognostic information, and optimizing post-transplant care for older recipients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".