Postdonation eGFR and New-Onset Antihypertensive Medication Use After Living Kidney Donation
Bibliographic record
Abstract
Background. Limited data are available regarding clinical implications of lower renal function after living kidney donation. We examined a novel integrated database to study associations between postdonation estimated glomerular filtration rate (eGFR) and use of antihypertensive medication (AHM) treatment after living kidney donation. Methods. Study data were assembled by linking national U.S. transplant registry identifiers, serum creatinine (SCr) values from electronic medical records, and pharmacy fill records for 3222 living donors (1989–2016) without predonation hypertension. Estimated GFR (mL/min per 1.73 m 2 ) was computed from SCr values by the CKD-EPI equation. Repeated measures multivariable mixed effects modeling examined the associations (adjusted odds ratio, 95%LCL aOR 95% UCL ) between AHM use and postdonation eGFR levels (random effect) with fixed effects for baseline donor factors. Results. The linked database identified an average of 3 postdonation SCr values per donor (range: 1–38). Lower postdonation eGFR (vs ≥75) bore graded associations with higher odds of AHM use (eGFR 30–44: aOR 0.95 1.47 2.26 ; <30: aOR 1.08 2.52 5.90 ). Other independent correlates of postdonation AHM use included older age at donation (aOR per decade: 1.08 1.23 1.40 ), black race (aOR 1.03 1.51 2.21 ), body mass index > 30 kg/m 2 (aOR 1.01 1.45 2.09 ), first-degree donor–recipient relationship (aOR 1.07 1.38 1.79 ), “prehypertension” at donation (systolic blood pressure 120–139: aOR 1.10 1.46 1.94 ; diastolic blood pressure 80–89: aOR 1.06 1.45 1.99 ). Conclusions. This novel linkage illustrates the ability to identify postdonation kidney function and associate it with clinically meaningful outcomes; lower eGFR after living kidney donation is a correlate of AHM treatment requirements. Further work should define relationships of postdonation renal function, hypertension, and other morbidity measures.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".