Identifying what Aspects of the Post-Kidney Transplant Experience Affect Quality of Life
Bibliographic record
Abstract
Introduction Although transplantation improves quality of life (QOL) of patients with end stage renal disease, post-transplant QOL scores remain below age- and sex-matched norms. To optimize post-transplant QOL, it is important to identify what matters to kidney transplant recipients as they recover. This will inform future personalized and patient-centered interventions. The majority of the tools available to measure QOL and health-related quality of life (HRQL) in kidney transplant recipients include standardized domains, which may not reflect what matters to patients. In contrast, the Patient Generated Index (PGI) is an individualized measure of QOL where people nominate areas of life affected by their health condition or living situation. The objectives of this study were to 1) identify what aspects of the post-kidney transplant experience are affecting QOL and 2) estimate the strength of the relationship between the PGI and standardized generic measures of health aspects of QOL. Materials and Methods We conducted a cross-sectional study at McGill University Health Centre. QOL was evaluated using three questionnaires (the EuroQol-5D (EQ-5D)TM, Visual Analogue Health States (VAHS) and PGI) and analyzed by descriptive statistics. Areas nominated on the PGI were categorized into a standard nomenclature. The relation between PGI, EQ-5D and VAHS was evaluated using Pearson correlations. Results and Discussion Fifty-one kidney transplant recipients (time post-transplant: < 1 year (n=25; 49%); 1-3 years (n=16; 31%) and >3 years (n=10; 20%)) participated in the study. The total EQ-5D score was 70 ± 17 (Mean ± SD). Participants reported having problems in all five domains and most commonly with mobility (47%) and the least common problem being self-care (6%). On the VAHS, the most common domains achieving scores of ≤ 6 (reflecting a need for intervention) were fatigue and sleep. The overall PGI score was 45.2 ± 27.6 (Mean ± SD). Seventy-one percent of the study participants reported ≥1 area of concern as impacting their QOL, with 30% reporting ≥3 areas. The most commonly nominated areas were physiological complaints (e.g. urine infection, creatinine levels), nutrition, mobility, pain, mood/emotions and fatigue. Pain and mood/emotions were the only dimensions assessed across all three questionnaires. The PGI identified six unique affected areas that were not identified on the EQ-5D or VAHS (i.e., nutrition, physiological, libido, restricted socialization, consequences of surgery and restriction on work). A weak relationship was found between the PGI and VAHS (r=0.16; CI: -0.17-0.46) and PGI and EQ-5D (r=0.34; CI: -0.01-0.6). Conclusions Of the QOL measures, the PGI appears to be the most sensitive for identifying what matters to each individual kidney transplant recipient. Given the breadth of QOL areas affected by kidney transplantation, comprehensive interventions such as self-management may be considered to target these areas of concerns. Fonds de Recherche Santé Québec (FRQS).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".