Comparison of different algorithms for the assessment of cardiovascular risk after kidney transplantation by the time of entering waiting list
Bibliographic record
Abstract
BACKGROUND: The prevalence of cardiovascular disease is high among patients with chronic kidney disease and cardiovascular events (CVE) remain the leading cause of death after kidney transplantation (KT). We performed a retrospective analysis of 389 KT recipients to assess if the European Society of Cardiology Score (ESC-Score), Framingham Heart Study Score (FRAMINGHAM), Prospective Cardiovascular Munster Study Score (PROCAM-Score) or Assessing cardiovascular risk using Scottish Intercollegiate Guidelines Network Score (ASSIGN-Score) algorithms can predict cardiovascular risk after KT at the time of entering the waiting list. METHODS: 389 KT candidates were scored by the time of entering the waiting list. Pearsons chi-square test, cox regression analysis and survival estimates were performed to evaluate the reliability of the cardiovascular scoring models after successful KT. RESULTS: During a follow-up of 8 ± 5.8 years, 96 patients (30%) died due to cardiovascular problems, whereas 13.9% suffered non-fatal CVE. Graft loss occurred in 84 patients (21.6%). Predictors of CVE, survival and graft loss were age and the length of end-stage kidney disease. All scores performed well in assessing the risk for CVE (P < 0.01). Receiver-operating characteristic analysis using the ESC-SCORE, as an example, suggested a cut-off for risk stratification and clinical decisions. CONCLUSIONS: We found all tested scores were reliable for cardiovascular assessment. We suggest using cardiac scores for risk assessment before KT and then taking further steps according to current guidelines.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".