Predicting Long-term Outcomes in Deceased Donor Kidney Transplant Recipients Using Three Short-term Graft Characteristics
Bibliographic record
Abstract
Key Points Delayed graft function is not an ideal measure of graft function, yet is used to assess risk in kidney transplantation. We propose a model that combines it with two other measures of 90-day graft function to identify recipients at incremental risk of inferior long-term outcomes. Background Delayed graft function (DGF) in kidney transplant recipients is used to determine graft prognosis, make organ utilization decisions, and as an important end point in clinical trials. However, DGF is not an ideal measure of graft function. We aimed to develop and validate a model that provides incremental risk assessment for inferior patient and graft outcomes. Methods We included adult kidney-only deceased donor transplant recipients from 1996 to 2016. In addition to DGF, two short-term measures were used to assess risk: renal function recovery <100% (attaining half the donor's eGFR) and recipient's 90-day eGFR <30. Recipients were at no, low, moderate, or high risk if they met zero, one, two, or all criteria, respectively. Cox proportional hazard models were used to assess the independent relationship between exposure and death-censored graft failure (DCGF) and mortality. Results Of the 792 eligible recipients, 24.5% experienced DGF, 40.5% had renal function recovery <100%, and 6.9% had eGFR <30. Over a median follow-up of 7.3 years, the rate of DCGF was 18.7% and mortality was 25.1%. When compared with recipients at no risk, those at low, moderate, and high risk were noted to have an increase in risk of DCGF (adjusted hazard ratio [aHR], 1.53; 95% confidence interval [CI], 1.03 to 2.27; aHR, 2.84; 95% CI, 1.68 to 4.79; aHR, 15.46; 95% CI, 8.04 to 29.71) and mortality (aHR, 1.16; 95% CI, 0.84 to 1.58; aHR, 1.85; 95% CI, 1.13 to 3.07; aHR, 2.66; 95% CI, 1.19 to 5.97). When using a hierarchical approach, each additional exposure predicted the risk of DCGF better than DGF alone and 100 random bootstrap replications supported the internal validity of the risk model. In an external validation cohort deemed to be at lower risk of DCGF, similar nonsignificant trends were noted. Conclusion We propose a risk model that provides an incremental assessment of recipients at higher risk of adverse long-term outcomes than DGF alone. This can help advance the field of risk assessment in transplantation and inform therapeutic decision making in patients at the highest spectrum of inferior outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".