ArticleTranslational andrology and urology2020
Acute kidney injury in solitary kidney patients after partial nephrectomy: incidence, risk factors and prediction.
Article in Translational andrology and urology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Role of artificial intelligence in predicting the renal function after nephrectomy in renal cell carcinoma: a systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
- Minimally Invasive Surgery in Solitary Kidneys with Renal Tumors: A Surgical Evolution.Current urology reports · 2026Review
- The Effect of Dexmedetomidine on Postoperative Acute Kidney Injury in High-Risk Partial Nephrectomy Patients: A Retrospective Cohort Study.Drug design, development and therapy · 2026Article
- A novel risk-predicted nomogram for acute kidney injury progression in decompensated cirrhosis: a double-center study in Vietnam.International urology and nephrology · 2025Article
- Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Machine Learning Models for Predicting Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy.Medicina (Kaunas, Lithuania) · 2023Article
- Evaluation of Platelet Distribution Width as an Early Predictor of Acute Kidney Injury in Extensive Burn Patients.Emergency medicine international · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- Nomogram Prediction Model of Serum Chloride and Sodium Ions on the Risk of Acute Kidney Injury in Critically Ill Patients.Infection and drug resistance · 2022Article
- CYR61, regulated by miR-22-3p and MALAT1, promotes autophagy in HK-2 cell inflammatory model.Translational andrology and urology · 2021Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo analyze the incidence and risk factors of acute kidney injury (AKI) after partial nephrectomy (PN) in patients with solitary kidney, and to build AKI prediction models using logistic regression and machine learning (ML) approaches.
methodsClinical data of 87 solitary kidney patients with renal mass who received PN from January 2003 to March 2019 were collected. The diagnosis of AKI was based on KDIGO criteria. Logistic regression analysis and ML method were used to build prediction models.
resultsAKI developed in 52 (59.8%) patients. The logistic regression model had three variables: ischemia time (P=0.003), surgery time (P=0.001) and preoperative fasted blood glucose level (FBG) (P=0.049). The area under curve (AUC) was 0.826, with the specificity and sensitivity of optimal threshold value 82.9% and 69.2%. The ML model had the following variables: ischemia time, surgery time, age, FBG, mean arterial pressure (MAP), colloid, crystalloid, etc. XGBoost model has the best prediction performance. The AUC was 0.749, lower than that of the logistic regression model with no statistical difference (P=0.258), with the specificity and sensitivity 62.9% and 84.6%, respectively.
conclusionsThe incidence of AKI after PN in patients with a solitary kidney was relatively high, it was associated with longer ischemia time, surgery time and higher FBG level, etc. The performance of ML model had no significant difference with logistic regression model. Prospective studies with larger sample sizes are awaited to test and verify our research findings.
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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.