scholarly journals Machine Learning for the Prediction of Progression in Patients with Acute Kidney Injury in Critical Care

2021 ◽  
Author(s):  
Lifan Zhang ◽  
Canzheng Wei ◽  
Yunxia Feng ◽  
Aijia Ma ◽  
Yan Kang
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Critical Care ◽  
Intensive Care ◽  
Logistic Regression Model ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Stage 1

Abstract Background: Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3. Methods: Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care (MIMIC-III), were included. We excluded patients who had underwent RRT or progressed to AKI stage 3 within 72 hours of the first AKI diagnosis. We also excluded patients with chronic kidney disease (CKD). We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve (ROC), and precision-recall curves (PRC). Results: We included 25711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes (MODS), blood urea nitrogen (BUN), sepsis, and respiratory failure were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression (AU-ROC, 0.926; 95%CI, 0.917 to 0.931 vs. 0.784; 95%CI, 0.771 to 0.796, respectively). Conclusions: The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research. Keywords: Acute kidney injury; Critical care; Logistic Models; Extreme gradient boosting

scholarly journals Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Cheng Qu ◽  
Lin Gao ◽  
Xian-qiang Yu ◽  
Mei Wei ◽  
Guo-quan Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Acute Pancreatitis ◽  
Logistic Regression ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

Background. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.

scholarly journals Machine Learning for the Prediction of Progression in Patients With Acute Kidney Injury in Critical Care

2020 ◽  
Author(s):  
Lifan Zhang ◽  
Canzheng Wei ◽  
Xuepeng Zhang ◽  
Aijia Ma ◽  
Jiangli Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
Regression Model ◽  
Cox Regression ◽  
Kidney Injury ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Cox Regression Model ◽  
Extreme Gradient Boosting

Abstract Background Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. Our previous study has shown that patients who will progress to AKI 3 stage are considered to receive RRT. This study aimed to develop a prediction model that can predict whether progression to AKI stage 3. Methods Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care (MIMIC-III), were included. Patients who receive RRT or progress to AKI 3 stage within 72 hours of first AKI diagnosis were excluded. We build two predictive models, respectively using machine learning extreme gradient boosting (XGBoost) and logistic regression, to predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation and area under receiver operating characteristic curve (AU-ROC). Results Of the 29238 patients included in the analysis, 3237 (11.1%) patients progressed to AKI stage 3. Creatinine, blood urea nitrogen (BUN), sepsis and respiratory failure were the important predictors of AKI progression. The machine learning XGBoost model has a better performance than the Cox regression model on predicting AKI stage 3 progression (AU-ROC, 0.860 vs. 0.728, respectively). Conclusions The XGBoost model was able to identify patients with AKI progression better than the Cox regression model. Machine learning techniques may improve predictive modeling in medical research.

scholarly journals Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

2018 ◽  
Vol 7 (11) ◽  
pp. 428 ◽  
Author(s):  
Hyung-Chul Lee ◽  
Soo Yoon ◽  
Seong-Mi Yang ◽  
Won Kim ◽  
Ho-Geol Ryu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Gradient Boosting Machine

Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86–0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56–0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.

scholarly journals Risk Factors and Outcomes of Acute Kidney Injury in Critically Ill Patients with Coronavirus Disease 2019

2020 ◽  
pp. 1-9
Author(s):  
Yichun Cheng ◽  
Nanhui Zhang ◽  
Ran Luo ◽  
Meng Zhang ◽  
Zhixiang Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Kidney Injury ◽  
Stage 1

<b><i>Background:</i></b> Coronavirus disease 2019 (COVID-19) has emerged as a major global health threat with a great number of deaths worldwide. Acute kidney injury (AKI) is a common complication in patients admitted to the intensive care unit. We aimed to assess the incidence, risk factors and in-hospital outcomes of AKI in COVID-19 patients admitted to the intensive care unit. <b><i>Methods:</i></b> We conducted a retrospective observational study in the intensive care unit of Tongji Hospital, which was assigned responsibility for the treatments of severe COVID-19 patients by the Wuhan government. AKI was defined and staged based on Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Mild AKI was defined as stage 1, and severe AKI was defined as stage 2 or stage 3. Logistic regression analysis was used to evaluate AKI risk factors, and Cox proportional hazards model was used to assess the association between AKI and in-hospital mortality. <b><i>Results:</i></b> A total of 119 patients with COVID-19 were included in our study. The median patient age was 70 years (interquartile range, 59–77) and 61.3% were male. Fifty-one (42.8%) patients developed AKI during hospitalization, corresponding to 14.3% in stage 1, 28.6% in stage 2 and 18.5% in stage 3, respectively. Compared to patients without AKI, patients with AKI had a higher proportion of mechanical ventilation mortality and higher in-hospital mortality. A total of 97.1% of patients with severe AKI received mechanical ventilation and in-hospital mortality was up to 79.4%. Severe AKI was independently associated with high in-hospital mortality (OR: 1.82; 95% CI: 1.06–3.13). Logistic regression analysis demonstrated that high serum interleukin-8 (OR: 4.21; 95% CI: 1.23–14.38), interleukin-10 (OR: 3.32; 95% CI: 1.04–10.59) and interleukin-2 receptor (OR: 4.50; 95% CI: 0.73–6.78) were risk factors for severe AKI development. <b><i>Conclusions:</i></b> Severe AKI was associated with high in-hospital mortality, and inflammatory response may play a role in AKI development in critically ill patients with COVID-19.

scholarly journals A machine learning-based prediction of hospital mortality in patients with postoperative sepsis

2020 ◽  
Author(s):  
Ren-qi Yao ◽  
Xin Jin ◽  
Guo-wei Wang ◽  
Yue Yu ◽  
Guo-sheng Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Gradient Boosting ◽  
Stepwise Logistic Regression ◽  
Postoperative Sepsis ◽  
Extreme Gradient Boosting ◽  
Stepwise Logistic Regression Model

Abstract Background: The incidence of postoperative sepsis is continually increased, while few studies have specifically focused on the risk factors and clinical outcomes associated with the development of sepsis after surgical procedures. The present study aimed to develop a mathematical model for predicting the in-hospital mortality among patients with postoperative sepsis.Methods: Surgical patients in Medical Information Mart for Intensive Care (MIMIC-III) database who simultaneously fulfilled Sepsis 3.0 as well as Agency for Healthcare Research and Quality (AHRQ) criteria during ICU admission were incorporated. We employed both extreme gradient boosting (XGBoost) and stepwise logistic regression model to predict in-hospital mortality among included patients with postoperative sepsis. Consequently, model performance was assessed from the angles of discrimination and calibration.Results: We included 3713 patients who fulfilled our inclusion criteria, in which 397 (10.7%) patients died during hospitalization, while 3316 (89.3%) of them survived through discharge. Fluid-electrolyte disturbance, coagulopathy, renal replacement therapy (RRT), urine output, and cardiovascular surgery were important features related to the in-hospital mortality. The XGBoost model had a better performance in both discriminatory ability (c-statistics, 0.835 [95% CI, 0.786 to 0.877] vs. c-statistics, 0.737 [95% CI, 0.688 to 0.786]) and goodness of fit (visualized by calibration curve) compared to the stepwise logistic regression model. Conclusion: XGBoost model appears to be a better performance in predicting hospital mortality among postoperative septic patients compared to the conventional stepwise logistic regression model. Machine learning-based algorithm might have significant application in the development of early warning system for septic patients following major operations.

Kidney and Mortality Outcomes Associated with Ondansetron in Critically Ill Patients

2022 ◽  
pp. 088506662110735
Author(s):  
Matthew Gray ◽  
Priyanka Priyanka ◽  
Sandra Kane-Gill ◽  
Lirong Wang ◽  
John A. Kellum
Keyword(s):  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Critical Care ◽  
Intensive Care ◽  
Kidney Disease ◽  
Primary Outcome ◽  
Kidney Injury ◽  
Multivariate Logistic Regression ◽  
Increased Risk ◽  
Secondary Outcomes

Background: Ondansetron is a preferred anti-emetic in critical care to treat nausea and vomiting, and has historically been considered a largely safe option. A recent pharmacoepidemiology study reported that ondansetron may be associated with an increased risk for acute kidney injury (AKI). Methods: We interrogated the High-Density Intensive Care (HiDenIC-15) database containing intensive care data for 13 hospitals across Western Pennsylvania between Oct 2008-Dec 2014. AKI was defined using the Kidney Disease, Improving Global Outcomes 2012 guidelines. Ondansetron use was considered as receiving any form of ondansetron within 24 h of admission. The subsequent 48 h (hours 25-72 after admission) were analyzed for outcomes. Primary outcome was development of AKI; secondary outcomes included 90-day mortality and time to AKI. Propensity-matched, multivariate logistic regression was applied for both outcomes. Comparator groups were metoclopramide and prochlorperazine using the same exposure criteria. Results:AKI occurred in 965 (5.6%), 12 (3.0%), and 61 (6.5%) patients receiving ondansetron, prochlorperazine, and metoclopramide, respectively. In the adjusted analysis, no anti-emetic was associated with a significant change in the odds of developing AKI. Ondansetron was associated with a 5.48% decrease (CI −6.17–−4.79) in death within 90 days of ICU-admission, which was independent of AKI status; an effect not seen with other anti-emetics. Anti-emetic usage was not associated with a change in the time to first AKI. Conclusion:Anti-emetic usage did not alter AKI risk. Ondansetron was associated with a significant decrease in 90-day mortality that was not seen by other anti-emetics, which requires further exploration.

scholarly journals Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiawei He ◽  
Jin Lin ◽  
Meili Duan
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Intensive Care ◽  
Kidney Injury ◽  
Learning Models ◽  
Short Term ◽  
Acute Kidney Disease ◽  
Mimic Iii ◽  
Machine Learning Models

Background: Sepsis-associated acute kidney injury (AKI) is frequent in patients admitted to intensive care units (ICU) and may contribute to adverse short-term and long-term outcomes. Acute kidney disease (AKD) reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models to predict the occurrence of AKD in patients with sepsis-associated AKI.Methods: Using clinical data from patients with sepsis in the ICU at Beijing Friendship Hospital (BFH), we studied whether the following three machine learning models could predict the occurrence of AKD using demographic, laboratory, and other related variables: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), decision trees, and logistic regression. In addition, we externally validated the results in the Medical Information Mart for Intensive Care III (MIMIC III) database. The outcome was the diagnosis of AKD when defined as AKI prolonged for 7–90 days according to Acute Disease Quality Initiative-16.Results: In this study, 209 patients from BFH were included, with 55.5% of them diagnosed as having AKD. Furthermore, 509 patients were included from the MIMIC III database, of which 46.4% were diagnosed as having AKD. Applying machine learning could successfully achieve very high accuracy (RNN-LSTM AUROC = 1; decision trees AUROC = 0.954; logistic regression AUROC = 0.728), with RNN-LSTM showing the best results. Further analyses revealed that the change of non-renal Sequential Organ Failure Assessment (SOFA) score between the 1st day and 3rd day (Δnon-renal SOFA) is instrumental in predicting the occurrence of AKD.Conclusion: Our results showed that machine learning, particularly RNN-LSTM, can accurately predict AKD occurrence. In addition, Δ SOFAnon−renal plays an important role in predicting the occurrence of AKD.

scholarly journals Artificial Intelligence for Risk Prediction of Rehospitalization with Acute Kidney Injury in Sepsis Survivors

2022 ◽  
Vol 12 (1) ◽  
pp. 43
Author(s):  
Shuo-Ming Ou ◽  
Kuo-Hua Lee ◽  
Ming-Tsun Tsai ◽  
Wei-Cheng Tseng ◽  
Yuan-Chia Chu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Acute Kidney Injury ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Cell Counts ◽  
Tree Classifier ◽  
Extreme Gradient Boosting ◽  
Sepsis Survivors

Sepsis survivors have a higher risk of long-term complications. Acute kidney injury (AKI) may still be common among sepsis survivors after discharge from sepsis. Therefore, our study utilized an artificial-intelligence-based machine learning approach to predict future risks of rehospitalization with AKI between 1 January 2008 and 31 December 2018. We included a total of 23,761 patients aged ≥ 20 years who were admitted due to sepsis and survived to discharge. We adopted a machine learning method by using models based on logistic regression, random forest, extra tree classifier, gradient boosting decision tree (GBDT), extreme gradient boosting, and light gradient boosting machine (LGBM). The LGBM model exhibited the highest area under the receiver operating characteristic curves (AUCs) of 0.816 to predict rehospitalization with AKI in sepsis survivors and followed by the GBDT model with AUCs of 0.813. The top five most important features in the LGBM model were C-reactive protein, white blood cell counts, use of inotropes, blood urea nitrogen and use of diuretics. We established machine learning models for the prediction of the risk of rehospitalization with AKI in sepsis survivors, and the machine learning model may set the stage for the broader use of clinical features in healthcare.

scholarly journals Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning

2020 ◽  
Author(s):  
Khaled Shawwa ◽  
Erina Ghosh ◽  
Stephanie Lanius ◽  
Emma Schwager ◽  
Larry Eshelman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
Mayo Clinic ◽  
Medical Information ◽  
Kidney Injury ◽  
Feature Model ◽  
Receiver Operating Curve ◽  
Gradient Boosting ◽  
Mimic Iii

Abstract Background Acute kidney injury (AKI) carries a poor prognosis. Its incidence is increasing in the intensive care unit (ICU). Our purpose in this study is to develop and externally validate a model for predicting AKI in the ICU using patient data present prior to ICU admission. Methods We used data of 98 472 adult ICU admissions at Mayo Clinic between 1 January 2005 and 31 December 2017 and 51 801 encounters from Medical Information Mart for Intensive Care III (MIMIC-III) cohort. A gradient-boosting model was trained on 80% of the Mayo Clinic cohort using a set of features to predict AKI acquired in the ICU. Results AKI was identified in 39 307 (39.9%) encounters in the Mayo Clinic cohort. Patients who developed AKI in the ICU were older and had higher ICU and in-hospital mortality compared to patients without AKI. A 30-feature model yielded an area under the receiver operating curve of 0.690 [95% confidence interval (CI) 0.682–0.697] in the Mayo Clinic cohort set and 0.656 (95% CI 0.648–0.664) in the MIMIC-III cohort. Conclusions Using machine learning, AKI among ICU patients can be predicted using information available prior to admission. This model is independent of ICU information, making it valuable for stratifying patients at admission.

scholarly journals Risk Factors and Outcomes of Acute Kidney Injury in Critically Ill Patients With Coronavirus Disease 2019

2020 ◽  
Author(s):  
Yichun Cheng ◽  
Nanhui Zhang ◽  
Ran Luo ◽  
Meng Zhang ◽  
Zhixiang Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Kidney Injury ◽  
Stage 1

Abstract Background: Coronavirus disease 2019 (COVID-19) has emerged as a major global health threat with a great number of deaths worldwide. Acute kidney injury (AKI) is a common complication in patients admitted to the intensive care unit. We aimed toassess the incidence, risk factors and in-hospital outcomes of AKI in COVID-19 patients admitted to intensive care unitMethods: we conducted a retrospective observational study in intensive care unit of Tongji hospital, which was assigned responsibility for the treatments of severe COVID-19 patients by Wuhan government. The AKI was defined and staged based onKidney Disease: Improving Global Outcomes (KDIGO) criteria. Mild AKI was defined as stage 1, and severe AKI was defined as stage 2 or stage 3. We used logistic regression analysis to evaluate AKI risk factors and the association between AKI and in-hospital mortality.Results: A total of 150 patients with COVID-19 were included in our study. The median age of patients was 70 (interquartile range, 60-80) years and 62.7% were male. 70 (46.7%) patients developed AKI during hospitalization, corresponding to the 17.3% in stage 1 and 9.3% in stage 2 and 20.0% in stage 3, respectively. Compared to patients without AKI, patients with AKI had higher proportion of mechanical ventilation mortality and higher in-hospital mortality. 95.5% patients with severe AKI received mechanical ventilation and in-hospital mortality was up to 79.5%. Severe AKI was independently associated with high in-hospital mortality (OR: 4.30; 95% CI: 1.83-10.10). Logistic regression analysis demonstrated that high serum interleukin-6 (OR: 2.54; 95%CI: 1.00-6.42) and interleukin-10 (OR: 3.02; 95%CI: 1.17-7.82) were risk factors for severe AKI development.Conclusions: severe AKI was associated with high in-hospital mortality and inflammatory response may play a role in AKI development in critically ill patients with COVID-19.

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