Incidence, Clinical Correlates, and Outcomes of Pulmonary Hypertension After Kidney Transplantation: Analysis of Linked US Registry and Medicare Billing Claims
Bibliographic record
Abstract
Background. The incidence, risks, and outcomes associated with pulmonary hypertension (P-HTN) in the kidney transplant (KTx) population are not well described. Methods. We linked US transplant registry data with Medicare claims (2006–2016) to investigate P-HTN diagnoses among Medicare-insured KTx recipients (N = 35 512) using billing claims. Cox regression was applied to identify independent correlates and outcomes of P-HTN (adjusted hazard ratio [aHR] 95%LCL aHR 95%UCL ) and to examine P-HTN diagnoses as time-dependent mortality predictors. Results. Overall, 8.2% of recipients had a diagnostic code for P-HTN within 2 y preceding transplant. By 3 y posttransplant, P-HTN was diagnosed in 10.3 10.6% 11.0 of the study cohort. After adjustment, posttransplant P-HTN was more likely in KTx recipients who were older (age ≥60 versus 18–30 y a HR, 1.91 2.40 3.01 ) or female (aHR, 1.15 1.24 1.34 ), who had pretransplant P-HTN (aHR, 4.38 4.79 5.24 ), coronary artery disease (aHR, 1.05 1.15 1.27 ), valvular heart disease (aHR, 1.22 1.32 1.43 ), peripheral vascular disease (aHR, 1.05 1.18 1.33 ), chronic pulmonary disease (aHR, 1.20 1.31 1.43 ), obstructive sleep apnea (aHR, 1.15 1.28 1.43 ), longer dialysis duration, pretransplant hemodialysis (aHR, 1.17 1.37 1.59 ), or who underwent transplant in the more recent era (2012–2016 versus 2006–2011: aHR, 1.29 1.39 1.51 ). Posttransplant P-HTN was associated with >2.5-fold increased risk of mortality (aHR, 2.57 2.84 3.14 ) and all-cause graft failure (aHR, 2.42 2.64 2.88 ) within 3 y posttransplant. Outcome associations of newly diagnosed posttransplant P-HTN were similar. Conclusions. Posttransplant P-HTN is diagnosed in 1 in 10 KTx recipients and is associated with an increased risk of death and graft failure. Future research is needed to refine diagnostic, classification, and management strategies to improve outcomes in KTx recipients who develop P-HTN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".