Development of a risk scoring system for predicting acute kidney injury after minimally invasive partial and radical nephrectomy: a retrospective study

Kim, Na Young; Chae, Dongwoo; Lee, Jongsoo; Kang, Byunghag; Park, Kyungsoo

doi:10.1007/s00464-020-07545-0

Cited by 8 publications

(10 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A meta-analysis of 71 studies suggested that machine learning algorithms did not improve discriminative power over traditional LR-based models in predicting various clinical outcomes such as diabetes mellitus, infection, heart failure, and cancer 34 . Nevertheless, one study reported the superiority of machine learning models to the LR model in predicting AKI after minimally invasive laparoscopic or robot-assisted laparoscopic nephrectomy for RCC 15 . The present study dealing with all operation types supports this result with better model performance.…”

Section: Discussionmentioning

confidence: 99%

“…Compensatory hypertrophy and hyperfiltration of the remaining kidney occurs within hours after nephrectomy, and a subsequent decrease in glomerular filtration rates is transient or subclinical 12 . However, 2-54% of patients experience postoperative acute kidney injury (AKI), which is attributable to several factors, such as elderly age, male sex, preoperative chronic kidney disease, diabetes mellitus, and RN [13][14][15][16][17][18][19][20] . AKI after nephrectomy for RCC leaves sequelae in the remaining kidneys, which is a strong risk factor…”

mentioning

confidence: 99%

“…Moreover, these studies included patients who underwent certain types of surgery (e.g. laparoscopic or robot-assisted laparoscopic) rather than all kinds of operations 15,20 . Preparing for AKI beforehand may not be easy because several conditions in addition to operative settings have interactive and complex effects on the risk.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma

Lee

Ryu

Kang

et al. 2021

Sci Rep

View full text Add to dashboard Cite

The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma

Lee

Ryu

Kang

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…We developed a model for predicting CKD after RN or PN, effectively extending the applicability of our prior model for predicting AKI after RN or PN [ 2 ]. The main strengths of our study were the active utilization of postoperative SCr and other laboratory values for CKD prediction and a clear demonstration of the importance of SCr measured within the first 5 days after surgery as a predictor of the 1-year SCr level.…”

Section: Discussionmentioning

confidence: 99%

“…Acute kidney injury (AKI) is a common complication after nephrectomy in renal cell cancer (RCC), with radical nephrectomy (RN) associated with a noticeably higher risk than partial nephrectomy (PN) [ 1 , 2 ]. AKI is defined as an abrupt decrease in kidney function occurring over 7 days or less, whereas chronic kidney disease (CKD) is defined by the persistence of kidney disease for a period of > 90 days [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels

Chae

Kim

et al. 2021

J Transl Med

Self Cite

View full text Add to dashboard Cite

Background Several 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. Methods Data 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. Results The 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. Conclusions Our 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.

show abstract

Influence of age on gender-related differences in acute kidney injury after minimally invasive radical or partial nephrectomy

et al. 2021

View full text Add to dashboard Cite

Development of a risk scoring system for predicting acute kidney injury after minimally invasive partial and radical nephrectomy: a retrospective study

Cited by 8 publications

References 37 publications

Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma

Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma

Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels

Influence of age on gender-related differences in acute kidney injury after minimally invasive radical or partial nephrectomy

Contact Info

Product

Resources

About