Is Prekidney Transplantation Dialysis Modality Associated with Post-Transplant Physical and Mental Health? Cross-Sectional Study Using PROMISÒ Tools
Bibliographic record
Abstract
Background: Health-related quality of life (HRQoL) is impaired in patients on dialysis and may remain compromised after kidney transplantation (KT). Peritoneal dialysis (PD) has been linked to better HRQoL than hemodialysis (HD), but few studies have examined if pre-KT dialysis modality is associated with post-KT HRQoL. We wanted to answer this question using tools developed by the Patient Reported Outcome Measurement Information System (PROMIS). Methods: Secondary analysis of cross-sectional data from a convenience sample of adult KT recipients. Participants completed PROMIS questionaries on electronic data capture. The primary outcomes were PROMIS Physical Health Summary (PHS) and Mental Health Summary (MHS) scores (higher scores reflecting better health). The exposure was pre-KT dialysis modality: in-center HD vs. PD. The relationship between exposure and post-KT MHS and PHS was explored using multivariable linear regression models, adjusted for sociodemographic (self-reported) and clinical covariables. Results: Of 178 patients, 90 were on HD and 88 on PD pre-KT. Mean(SD) age was 54(13) years; patients on HD prior to transplant were slightly younger (52(14) vs. 56(13) years, p=0.063). The HD group had more males (69% vs. 49%, p=0.007) and higher diabetes prevalence (40% vs. 27%, p=0.07). Groups were similar in education, material deprivation, comorbidities, and transplant vintage. The mean(SD) post-KT eGFR was less in patients on HD (55(28) vs. 59(23), p=0.39). The PD group had higher mean(SD) MHS (50(9) vs 47(9), p= 0.03) scores but similar PHS (45(9) vs 44(11), p = 0.41) scores. In unadjusted analysis, PD was associated with higher MHS scores (coeff: 2.94, 95% CI: 0.35–5.54, p=0.026), which remained significant after adjustment (coeff: 3.22, 95% CI: 0.44–6.00, p=0.03). PHS was not different between patients who had been on PD vs HD prior to transplant. Conclusion: Participants who had been on PD vs HD pre-KT had better MH, even after adjusting for potentially important co-variables. Physical health was similar between groups. Although our cross-sectional analysis cannot establish causality and our result need to be confirmed in larger samples, our findings suggest that patients on PD compared to HD may expect a better HRQOL after KT. Funding: Private Foundation Support, Government Support – Non-U.S.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".