ArticleNephrology (Carlton, Vic.)2019
Development and future deployment of a 5 years allograft survival model for kidney transplantation.
Article in Nephrology (Carlton, Vic.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Predictive utility of nomogram based on serum glucose-regulated protein 78 and kidney function for long-term kidney graft survival.Scientific reports · 2024Article
- Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions.Journal of clinical and translational science · 2024Article
- Predicting Kidney Transplant Recipient Cohorts' 30-Day Rehospitalization Using Clinical Notes and Electronic Health Care Record Data.Kidney international reports · 2023Article
- Clinical Events and Renal Function in the First Year Predict Long-Term Kidney Transplant Survival.Kidney360 · 2022Article
- The path from big data analytics capabilities to value in hospitals: a scoping review.BMC health services research · 2022Article
- Surrogate Endpoints for Late Kidney Transplantation Failure.Transplant international : official journal of the European Society for Organ Transplantation · 2022Article
- Costs in the Year Following Deceased Donor Kidney Transplantation: Relationships With Renal Function and Graft Failure.Transplant international : official journal of the European Society for Organ Transplantation · 2022Article
- Article
- Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.Health data science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
aimIdentifying kidney transplant patients at highest risk for graft loss prior to loss may allow for effective interventions to improve 5 years survival.
methodsWe performed a 10 years retrospective cohort study of adult kidney transplant recipients (n = 1747). We acquired data from electronic health records, United Network of Organ Sharing, social determinants of health, natural language processing data extraction, and real-time capture of dynamically evolving clinical data obtained within 1 year of transplant; from which we developed a 5 years graft survival model.
resultsTotal of 1439 met eligibility; 265 (18.4%) of them experienced graft loss by 5 years. Graft loss patients were characterized by: older age, being African-American, diabetic, unemployed, smokers, having marginal donor kidneys and cardiovascular comorbidities. Predictive dynamic variables included: low mean blood pressure, higher pulse pressures, higher heart rate, anaemia, lower estimated glomerular filtration rate peak, increased tacrolimus variability, rejection and readmissions. This Big Data analysis generated a 5 years graft loss model with an 82% predictive capacity, versus 66% using baseline United Network of Organ Sharing data alone.
conclusionOur analysis yielded a 5 years graft loss model demonstrating superior predictive capacity compared with United Network of Organ Sharing data alone, allowing post-transplant individualized risk-assessed care prior to transitioning back to community care.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.