(065) SURVIVAL AND GRAFT FAILURE FOLLOWING SOLID ORGAN TRANSPLANTATION IN HYPOGONADAL MEN: AN ANALYSIS OF THE INSTITUTE OF CLINICAL EVALUATIVE SCIENCES DATA REPOSITORY
Bibliographic record
Abstract
Abstract Introduction Organ-specific mechanisms for hypogonadism have been reported in end-stage renal disease and cirrhosis. The most commonly transplanted organs in the United States are kidneys, hearts, livers, and lungs. Amongst kidney transplant recipients, worse survival has been reported amongst hypogonadal men. Aberration within grafted organs, whether failure, impaired function, or rejection, has also been reported in hypogonadal men undergoing renal, hepatic, and cardiac transplantation. Objective To determine if survival and time to graft failure amongst men undergoing solid organ transplantation differ based on pre-transplant testosterone levels. Methods The Institute for Clinical Evaluative Sciences (ICES) data repository (Ontario, Canada) was utilized in this study. Males aged ≥18 who underwent a solid organ transplant (SOT - heart, lung, kidney, liver) between 2009 and 2021 with at least one laboratory measurement of total testosterone within the three years before transplant were included in our study. A low testosterone (<300 ng/dL) and normal testosterone (≥300 ng/dL) cohort were created based on the pre-transplant total testosterone level closest to the time of transplant. Propensity score matching based on covariates in Table 1 was performed. One-to-one greedy nearest-neighbor matching was used to match individuals in the low and normal testosterone cohorts. Kaplan Meier curves and Cox proportional hazard models were used to assess survival following SOT. Aalen-Johansen cumulative incidence functions and Fine-Gray proportional hazard models with death as a competing risk was used to assess time to graft failure. We also assessed survival and graft failure in a kidney transplant-only cohort. Data are reported as hazard ratios (HR) or subdistribution hazard ratios (sHR) and 95% confidence interval. Survival ((log) hazard of death) following transplant was also assessed when pre-transplant testosterone was treated as a continuous variable modeled via a p-spline with 5 degrees of freedom. Our reference value was 300 ng/dL. All analyses were conducted within the ICES secure system (IDAVE) with R (version 4.2.2) and made use of the MatchIt (v4.5.5) and survival (v3.5.5) packages. Results We identified 121 men and 220 men having undergone solid-organ transplantation with low and normal testosterone, respectively. Baseline characteristics did not differ between cohorts following propensity score matching (Table 1). Survival (overall SOT HR 0.809, 0.494, 1.325; renal-only HR 1.263, 0.563,2.830) and graft failure (overall SOT sHR 0.834, 0.255, 2.7225; renal-only sHR 4.349, 0.538, 25.150) did not differ based on pre-transplant testosterone level in the either cohort. No clear relationship between pre-transplant testosterone level treated as a continuous variable and post-transplant survival was observed (Fig. 1). Conclusions Using the ICES data repository, post-transplant survival and graft survival did not differ significantly between hypogonadal and eugonadal men within our entire SOT (heart, lung, kidney, liver) or renal-only cohorts. A clear relationship between pre-transplant testosterone levels and survival was not identified. Future research is needed to elucidate pre-transplant testosterone trends and outcome data to highlight potential avenues for intervention with testosterone therapy or alternative modalities. Disclosure No.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".