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

Lee, Yeonhee; Ryu, Jiwon; Kang, Min Woo; Seo, Kyung; Kim, Jayoun; Suh, Jungyo; Kim, Yong Chul; Kim, Yon Su; Oh, Kook Hwan; Joo, Kwon Wook; Jeong, Chang Wook; Lee, Sang Chul; Kwak, Cheol; Kim, Sejoong

doi:10.1038/s41598-021-95019-1

Cited by 16 publications

(8 citation statements)

References 35 publications

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“…Traditional statistical models such as logistic regression analysis have been previously utilized to construct such prognostication tools [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. In recent years, ML predictive algorithms have emerged as a method to handle high-dimensional, unstructured, and complex structured data including hospitalized patient with AKI [ 27 , 28 , 29 , 30 , 31 ]. While autoML has been shown to be very effective, with high predictive performance comparable to human hyperparameter optimization and with higher time-efficient workflow when compared to non-automated ML [ 41 , 43 ], autoML has never been utilized in the development of AKI prediction models.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence (AI) and machine learning (ML) have been increasingly applied to individualized medicine [ 21 , 22 , 23 , 24 , 25 , 26 ], including the prediction of AKI in various settings [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. ML algorithms can handle nonlinear, complex, and multidimensional data [ 36 , 37 ], and recent studies have shown high predictive performance from ML algorithms that outperform traditional statistical analyses [ 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

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Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury

Pattharanitima

Kattah

Mao

et al. 2022

JCM

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Background: We aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI). Methods: Using 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model. We then compared model performance using area under the receiver operating characteristic curve (AUROC) and assessed model calibration using Brier score on the independent testing dataset. Results: The incidence of CSA-AKI was 36%. Stacked ensemble autoML had the highest predictive performance among autoML models, and was chosen for comparison with other non-autoML and multivariable logistic regression models. The autoML had the highest AUROC (0.79), followed by RF (0.78), XGBoost (0.77), multivariable logistic regression (0.77), ANN (0.75), and DT (0.64). The autoML had comparable AUROC with RF and outperformed the other models. The autoML was well-calibrated. The Brier score for autoML, RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.18, 0.18, 0.21, 0.19, 0.19, and 0.18, respectively. We applied SHAP and LIME algorithms to our autoML prediction model to extract an explanation of the variables that drive patient-specific predictions of CSA-AKI. Conclusion: We were able to present a preoperative autoML prediction model for CSA-AKI that provided high predictive performance that was comparable to RF and superior to other ML and multivariable logistic regression models. The novel approaches of the proposed explainable preoperative autoML prediction model for CSA-AKI may guide clinicians in advancing individualized medicine plans for patients under cardiac surgery.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury

Pattharanitima

Kattah

Mao

et al. 2022

JCM

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“… 26 Our results are consistent with previous studies in more selected populations. 27 , 28 For example, a retrospective cohort study of 4104 renal cell carcinoma (RCC) patients in South Korea found that the application of ML algorithms improves the predictability of acute kidney injury after nephrectomy for RCC, and these models perform better than conventional logistic regression-based models. 27 …”

Section: Discussionmentioning

confidence: 99%

Comparison Between Statistical Model and Machine Learning Methods for Predicting the Risk of Renal Function Decline Using Routine Clinical Data in Health Screening

Cao¹,

Lin²,

Yang³

et al. 2022

RMHP

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Purpose Using machine learning method to predict and judge unknown data offers opportunity to improve accuracy by exploring complex interactions between risk factors. Therefore, we evaluate the performance of machine learning (ML) algorithms and to compare them with logistic regression for predicting the risk of renal function decline (RFD) using routine clinical data. Patients and Methods This retrospective cohort study includes datasets from 2166 subjects, aged 35–74 years old, provided by an adult health screening follow-up program between 2010 and 2020. Seven different ML models were considered – random forest, gradient boosting, multilayer perceptron, support vector machine, K-nearest neighbors, adaptive boosting, and decision tree - and were compared with standard logistic regression. There were 24 independent variables, and the baseline estimate glomerular filtration rate (eGFR) was used as the predictive variable. Results A total of 2166 participants (mean age 49.2±11.2 years old, 63.3% males) were enrolled and randomly divided into a training set (n=1732) and a test set (n=434). The area under receiver operating characteristic curve (AUROC) for detecting RFD corresponding to the different models were above 0.85 during the training phase. The gradient boosting algorithms exhibited the best average prediction accuracy (AUROC: 0.914) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the RFD prediction performance, compared to logistic regression model (AUROC:0.882), except the K-nearest neighbors and decision tree algorithms (AUROC:0.854 and 0.824, respectively). However, the improvement differences with logistic regression were small (less than 4%) and nonsignificant. Conclusion Our results indicate that the proposed health screening dataset-based RFD prediction model using ML algorithms is readily applicable, produces validated results. But logistic regression yields as good performance as ML models to predict the risk of RFD with simple clinical predictors.

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“…However, in another study performed in 2020 by Zhu et al (47), by incorporating preoperative, intraoperative and post-operative variables and using multiple methods to learn and train information on 87 patients, the results showed that the best performing XGBoost model had an AUC value of 0.749, which was lower than that of the classical logistic regression model of 0.826. It may be difficult to conduct parallel comparisons among the only three studies at present because of the degree of variation in the between-group design; for example, there was a certain proportion of RN patients among the 4,104 patients included in the first study (45), whereas the third study included all patients with isolated kidney (47). In addition, in a single study, differences in operator and surgical approaches (manual or robotic, laparoscopic or open) may also lead to confounding bias, making the accurate prediction of outcomes complex and difficult.…”

Section: Special Surgery-related Aki Prediction Modelmentioning

confidence: 99%

Machine learning for acute kidney injury: Changing the traditional disease prediction mode

Xia

Huang

et al. 2023

Front. Med.

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Acute kidney injury (AKI) is a serious clinical comorbidity with clear short-term and long-term prognostic implications for inpatients. The diversity of risk factors for AKI has been recognized in previous studies, and a series of predictive models have been developed using traditional statistical methods in conjunction with its preventability, but they have failed to meet the expectations in limited clinical applications, the rapid spread of electronic health records and artificial intelligence machine learning technology has brought new hope for the construction of AKI prediction models. In this article, we systematically review the definition and classification of machine learning methods, modeling ideas and evaluation methods, and the characteristics and current status of modeling studies. According to the modeling objectives, we subdivided them into critical care medical setting models, all medical environment models, special surgery models, special disease models, and special nephrotoxin exposure models. As the first review article to comprehensively summarize and analyze machine learning prediction models for AKI, we aim to objectively describe the advantages and disadvantages of machine learning approaches to modeling, and help other researchers more quickly and intuitively understand the current status of modeling research, inspire ideas and learn from experience, so as to guide and stimulate more research and more in-depth exploration in the future, which will ultimately provide greater help to improve the overall status of AKI diagnosis and treatment.

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

Cited by 16 publications

References 35 publications

Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury

Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury

Comparison Between Statistical Model and Machine Learning Methods for Predicting the Risk of Renal Function Decline Using Routine Clinical Data in Health Screening

Machine learning for acute kidney injury: Changing the traditional disease prediction mode

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