ArticleTransplantation2017
Predictive Score for Posttransplantation Outcomes.
Article in Transplantation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Using Information Available at the Time of Donor Offer to Predict Kidney Transplant Survival Outcomes: A Systematic Review of Prediction Models.Transplant international : official journal of the European Society for Organ Transplantation · 2022Pooled it
- Predicting first-year kidney graft loss at the time of donor offer using machine learning and competing-risk analysis in a Latin American cohort.Clinical kidney journal · 2026Article
- A Novel Pre-Kidney Transplant Risk Score to Optimize Waiting List Management.Journal of clinical medicine · 2026Article
- A Prediction Model for Risk of Death in Kidney Transplant Recipients.JAMA network open · 2026Article
- Kidney Donor Risk Index and Cardiovascular Complications in a Long-Term Follow-Up Observation.Journal of clinical medicine · 2025Article
- A machine learning-based nomogram for predicting graft survival in allograft kidney transplant recipients: a 20-year follow-up study.Frontiers in medicine · 2025Article
- Article
- Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models.Scientific reports · 2024Article
- Predicting Kidney Transplantation Outcomes from Donor and Recipient Characteristics at Time Zero: Development of a Mobile Application for Nephrologists.Journal of clinical medicine · 2024Article
- Association of high-priority exceptions with waitlist mortality among heart transplant candidates.The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation · 2023Article
- Predicting Kidney Transplant Recipient Cohorts' 30-Day Rehospitalization Using Clinical Notes and Electronic Health Care Record Data.Kidney international reports · 2023Article
- Dominant predictors of early post-transplant outcomes based on the Korean Organ Transplantation Registry (KOTRY).Scientific reports · 2022Article
- Post-transplant outcomes in recipients of living donor kidneys and intended recipients of living donor kidneys.BMC nephrology · 2022Observational
- Organization of Post-Transplant Care and the 5-Year Outcomes of Kidney Transplantation.International journal of environmental research and public health · 2022Article
- Development of predictive score for post-transplant survival based on pre-transplant recipient characteristics.Korean journal of transplantation · 2021Article
- Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index.BMC medical research methodology · 2021Article
- Prediction scores for risk of allograft loss in patients receiving kidney transplants: nil satis nisi optimum.Clinical kidney journal · 2020Article
- Dynamic 2-deoxy-2[18F] fluoro-D-glucose PET/MRI in human renal allotransplant patients undergoing acute kidney injury.Scientific reports · 2020Observational
- Prediction of Three-Year Mortality After Deceased Donor Kidney Transplantation in Adults with Pre-Transplant Donor and Recipient Variables.Annals of transplantation · 2019Article
- Nutritional treatment of advanced CKD: twenty consensus statements.Journal of nephrology · 2018Article
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundMost current scoring tools to predict allograft and patient survival upon kidney transplantion are based on variables collected posttransplantation. We developed a novel score to predict posttransplant outcomes using pretransplant information including routine laboratory data available before or at the time of transplantation.
methodsLinking the 5-year patient data of a large dialysis organization to the Scientific Registry of Transplant Recipients, we identified 15 125 hemodialysis patients who underwent first deceased transplantion. Prediction models were developed using Cox models for (a) mortality, (b) allograft loss (death censored), and (c) combined death or transplant failure. The cohort was randomly divided into a two thirds set (Nd = 10 083) for model development and a one third set (Nv = 5042) for validation. Model predictive discrimination was assessed using the index of concordance, or C statistic, which accounts for censoring in time-to-event models (a-c). We used the bootstrap method to assess model overfitting and calibration using the development dataset.
resultsPatients were 50 ± 13 years of age and included 39% women, 15% African Americans, and 36% persons with diabetes. For prediction of posttransplant mortality and graft loss, 10 predictors were used (recipients' age, cause and length of end-stage renal disease, hemoglobin, albumin, selected comorbidities, race and type of insurance as well as donor age, diabetes status, extended criterion donor kidney, and number of HLA mismatches). The new model (www.TransplantScore.com) showed the overall best discrimination (C-statistics, 0.70; 95% confidence interval [95% CI], 0.67-0.73 for mortality; 0.63; 95% CI, 0.60-0.66 for graft failure; 0.63; 95% CI, 0.61-0.66 for combined outcome).
conclusionsThe new prediction tool, using data available before the time of transplantation, predicts relevant clinical outcomes and may perform better to predict patients' graft survival than currently used tools.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.