Machine learning for the prediction of acute kidney injury in patients after cardiac surgery

Xue, Xin; Li, Yong; Xue, Tao; Chen, Wen; Chen, Xin

doi:10.3389/fsurg.2022.946610

Cited by 8 publications

(10 citation statements)

References 31 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to some other studies, Random Forest (RF), a widely used ensemble model, has been shown to perform well in certain contexts. For instance, in Xue et al (2022), RF achieved high accuracy, AUC, and Brier scores of 0.822, 85.8%, and 0.137, respectively, suggesting its efficacy for predicting length of stay in hospital patients. These findings highlight the importance of carefully selecting the appropriate machine learning algorithm based on the specific data and problem being addressed.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis

Zeleke

Palumbo

Malaguti

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

ObjectiveThis study aims to develop and compare different models to predict the Length of Stay (LoS) and the Prolonged Length of Stay (PLoS) of inpatients admitted through the emergency department (ED) in general patient settings. This aim is not only to promote any specific model but rather to suggest a decision-supporting tool (i.e., a prediction framework).MethodsWe analyzed a dataset of patients admitted through the ED to the “Sant”Orsola Malpighi University Hospital of Bologna, Italy, between January 1 and October 26, 2022. PLoS was defined as any hospitalization with LoS longer than 6 days. We deployed six classification algorithms for predicting PLoS: Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbors (KNN), and logistic regression (LoR). We evaluated the performance of these models with the Brier score, the area under the ROC curve (AUC), accuracy, sensitivity (recall), specificity, precision, and F1-score. We further developed eight regression models for LoS prediction: Linear Regression (LR), including the penalized linear models Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Elastic-net regression, Support vector regression, RF regression, KNN, and eXtreme Gradient Boosting (XGBoost) regression. The model performances were measured by their mean square error, mean absolute error, and mean relative error. The dataset was randomly split into a training set (70%) and a validation set (30%).ResultsA total of 12,858 eligible patients were included in our study, of whom 60.88% had a PloS. The GB classifier best predicted PloS (accuracy 75%, AUC 75.4%, Brier score 0.181), followed by LoR classifier (accuracy 75%, AUC 75.2%, Brier score 0.182). These models also showed to be adequately calibrated. Ridge and XGBoost regressions best predicted LoS, with the smallest total prediction error. The overall prediction error is between 6 and 7 days, meaning there is a 6–7 day mean difference between actual and predicted LoS.ConclusionOur results demonstrate the potential of machine learning-based methods to predict LoS and provide valuable insights into the risks behind prolonged hospitalizations. In addition to physicians' clinical expertise, the results of these models can be utilized as input to make informed decisions, such as predicting hospitalizations and enhancing the overall performance of a public healthcare system.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis

Zeleke

Palumbo

Malaguti

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…For this study, the RFC classification prediction model could achieve an AUC value of 0.778 in the test set, while the DCA curve showed a better net benefit than other models. Xue et al [25] have also constructed a CSA-AKI classification prediction model. They found that the random forest model had the best prediction performance and could achieve an AUC value of 0.858 (95% CI: 0.792-0.923).…”

Section: Discussionmentioning

confidence: 99%

Prediction of Acute Kidney Injury after Extracorporeal Cardiac Surgery (CSA-AKI) by Machine Learning Algorithms

Tong,

Niu,

Liu

2023

HSF

View full text Add to dashboard Cite

Background: Acute renal failure after extracorporeal cardiac surgery under general anesthesia is high and unpredictable, but machine learning algorithms could change this. A feasible approach is to use machine learning models to construct models to predict acute kidney injury after extracorporeal cardiac surgery (CSA-AKI) and screen for the best predictive model. Method: From January 2014 to December 2021, 2187 patients undergoing extracorporeal cardiac surgery at the third hospital of Hebei Medical University and the first medical centre of Chinese PLA General Hospital were collected in this study. After excluding 923 patients who did not meet the inclusion criteria, a dataset of 1264 patients with 125 clinical indexes was constructed. After screening the feature variables using Least absolute shrinkage (LASSO) regression, the dataset was randomly divided into a training set (70%), test set (30%), and six machine learning algorithms, including extreme gradient boosting (XGBoost), logistic regression (LRC), light gradient boosting machine (LGBM), random forest classifier (RFC), adaptive boosting (AdaBoost), and K-nearest neighbor (KNN), were used in training set for predicting the CSA-AKI. The machine learning model with the best predictive performance was selected to complete external validation of the test set. The SHapley Additive exPlanations (SHAP) algorithm was used to interpret the model. Results: Of all 1264 patients, 372 (29.43%) patients presented with CSA-AKI. The LASSO regression eliminated 22 feature variables out of 125 before model development. Among the six prediction models, the RFC prediction model has the best prediction performance, with an Area Under Curve (AUC) value of 0.778 (95% CI: 0.726–0.830) in the test set and the best net benefit compared to the other tools. SHAP explained the impact of different feature variables on the predicted outcome, where the three most influential feature variables were creatinine clearance (CRC), intraoperative urine output (mL/kg/h) and age. Conclusion: We developed an RFC prediction model to predict the CSA-AKI, which has good predictive performance and can explain the factors affecting the prediction results of cases by integrating the SHAP method.

show abstract

“…Artificial intelligence (AI) and machine learning (ML) are rapidly emerging as transformative tools for diagnosing and managing AKI patients [12][13][14][15][16][17][18][19][20][21]. Compared to traditional methods, ML algorithms can reveal patterns beyond human discernment and enhance SA-AKI prediction accuracy by analyzing vast datasets [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and machine learning’s role in sepsis-associated acute kidney injury

Cheungpasitporn,

Thongprayoon,

Kashani

2024

Kidney Res Clin Pract

View full text Add to dashboard Cite

Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.

show abstract

Machine learning for the prediction of acute kidney injury in patients after cardiac surgery

Cited by 8 publications

References 31 publications

Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis

Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis

Prediction of Acute Kidney Injury after Extracorporeal Cardiac Surgery (CSA-AKI) by Machine Learning Algorithms

Artificial intelligence and machine learning’s role in sepsis-associated acute kidney injury

Contact Info

Product

Resources

About