ArticleJournal of translational medicine2021
Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels.
Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 9 citations in OpenAlex.
- Finerenone in People with CKD, Type 2 Diabetes, and History of Nephrectomy.Clinical journal of the American Society of Nephrology : CJASN · 2026Trial
- Glucagon like peptide-1 receptor agonist therapy and long-term renal outcomes after radical nephrectomy: a propensity-matched cohort analysis.International urology and nephrology · 2026Article
- Bibliometric analysis of renal function preservation in robot-assisted partial nephrectomy: research trends, functional outcomes, and CKD-related perspectives.Journal of robotic surgery · 2026Review
- Unveiling the silent burden: high prevalence of occult nephropathies in radical nephrectomy specimens.Clinical kidney journal · 2026Article
- Multiparametric MRI for Predicting Renal Function Deterioration and Chronic Kidney Disease Development in Patients Undergoing Nephrectomy for Renal Masses: A Pilot Study.Journal of magnetic resonance imaging : JMRI · 2026Article
- Development and Validation of a Nomogram for Predicting Chronic Kidney Disease in Older Patients with Type 2 Diabetes Mellitus and Cardiovascular Disease.Journal of multidisciplinary healthcare · 2026Article
- Accuracy of warm ischemia time measurement using a surgical intelligence software in partial nephrectomies: A validation study.BJUI compass · 2024Article
- Impact of Tumor Volume and Other Factors on Renal Function After Partial Nephrectomy.Journal of clinical medicine · 2024Article
- Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine Learning Algorithms: Case-Control Study.JMIR medical informatics · 2024Article
- Article
- New-onset renal impairment following radical nephrectomy for renal cell carcinoma: a retrospective cohort study of clinical, intraoperative, and postoperative risk factors.Therapeutic advances in urologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral predictive factors for chronic kidney disease (CKD) following radical nephrectomy (RN) or partial nephrectomy (PN) have been identified. However, early postoperative laboratory values were infrequently considered as potential predictors. Therefore, this study aimed to develop predictive models for CKD 1 year after RN or PN using early postoperative laboratory values, including serum creatinine (SCr) levels, in addition to preoperative and intraoperative factors. Moreover, the optimal SCr sampling time point for the best prediction of CKD was determined.
methodsData were retrospectively collected from patients with renal cell cancer who underwent laparoscopic or robotic RN (n = 557) or PN (n = 999). Preoperative, intraoperative, and postoperative factors, including laboratory values, were incorporated during model development. We developed 8 final models using information collected at different time points (preoperative, postoperative day [POD] 0 to 5, and postoperative 1 month). Lastly, we combined all possible subsets of the developed models to generate 120 meta-models. Furthermore, we built a web application to facilitate the implementation of the model.
resultsThe magnitude of postoperative elevation of SCr and history of CKD were the most important predictors for CKD at 1 year, followed by RN (compared to PN) and older age. Among the final models, the model using features of POD 4 showed the best performance for correctly predicting the stages of CKD at 1 year compared to other models (accuracy: 79% of POD 4 model versus 75% of POD 0 model, 76% of POD 1 model, 77% of POD 2 model, 78% of POD 3 model, 76% of POD 5 model, and 73% in postoperative 1 month model). Therefore, POD 4 may be the optimal sampling time point for postoperative SCr. A web application is hosted at https://dongy.shinyapps.io/aki_ckd .
conclusionsOur predictive model, which incorporated postoperative laboratory values, especially SCr levels, in addition to preoperative and intraoperative factors, effectively predicted the occurrence of CKD 1 year after RN or PN and may be helpful for comprehensive management planning.
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.