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

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.

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

10.21203/rs.3.rs-412422/v1 ◽

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

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

Frontiers in Medicine ◽

10.3389/fmed.2021.792974 ◽

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.

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

10.21203/rs.3.rs-102978/v1 ◽

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.

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Estimation of Baseline Serum Creatinine with Machine Learning

American Journal of Nephrology ◽

10.1159/000518902 ◽

2021 ◽

pp. 1-10

Author(s):

Erina Ghosh ◽

Larry Eshelman ◽

Stephanie Lanius ◽

Emma Schwager ◽

Kalyan S. Pasupathy ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care ◽

Serum Creatinine ◽

Test Data ◽

Mayo Clinic ◽

Gradient Boosting ◽

Baseline Serum ◽

Baseline Serum Creatinine ◽

Baseline Creatinine ◽

Mimic Iii

Introduction: Comparing current to baseline serum creatinine is important in detecting acute kidney injury. In this study, we report a regression-based machine learning model to predict baseline serum creatinine. Methods: We developed and internally validated a gradient boosting model on patients admitted in Mayo Clinic intensive care units from 2005 to 2017 to predict baseline creatinine. The model was externally validated on the Medical Information Mart for Intensive Care III (MIMIC III) cohort in all ICU admissions from 2001 to 2012. The predicted baseline creatinine from the model was compared with measured serum creatinine levels. We compared the performance of our model with that of the backcalculated estimated serum creatinine from the Modification of Diet in Renal Disease (MDRD) equation. Results: Following ascertainment of eligibility criteria, 44,370 patients from the Mayo Clinic and 6,112 individuals from the MIMIC III cohort were enrolled. Our model used 6 features from the Mayo Clinic and MIMIC III datasets, including the presence of chronic kidney disease, weight, height, and age. Our model had significantly lower error than the MDRD backcalculation (mean absolute error [MAE] of 0.248 vs. 0.374 in the Mayo Clinic test data; MAE of 0.387 vs. 0.465 in the MIMIC III cohort) and higher correlation (intraclass correlation coefficient [ICC] of 0.559 vs. 0.050 in the Mayo Clinic test data; ICC of 0.357 vs. 0.030 in the MIMIC III cohort). Discussion/Conclusion: Using machine learning models, baseline serum creatinine could be estimated with higher accuracy than the backcalculated estimated serum creatinine level.

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A Comparative Study of Machine Learning Techniques for Predicting Sepsis for MIMIC-III Patients

10.21203/rs.3.rs-697902/v1 ◽

2021 ◽

Author(s):

Xuze Zhao ◽

Bo Qu

Keyword(s):

Machine Learning ◽

Intensive Care ◽

Intensive Care Units ◽

Medical Information ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

Mortality And Morbidity ◽

Extreme Gradient Boosting ◽

Mimic Iii

Abstract Background: Sepsis is one of the dominating causes of mortality and morbidity in-hospital especially in intensive care units (ICU) patients. Therefore, a reliable decision-making model for predicting sepsis is of great importance. The purpose of this study was to develop an eXtreme Gradient Boosting (XGBoost) based model and explore whether it performs better in predicting sepsis from the time of admission in intensive care units (ICU) than other machine learning (ML) methods. Methods: The source data used for model establishment in this study were from a retrospective medical information mart for intensive care (MIMIC) III dataset, restricted to intensive care units (ICUs) patients aged between 18 and 89. Model performance of the XGBoost model was compared to logistic regression (LR), recursive neural network (RNN), and support vector machine (SVM). Then, the performances of the models were evaluated and compared by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves.Results: A total of 6430 MIMIC-III cases are included in this article, in which, 3021 cases have encountered sepsis while 3409 cases have not, respectively. As for the AUC (0.808 (95% CI): 0.767-0.848,DT), 0.802 (95%CI: 0.762-0.842，RNN), 0.790 (95%CI: 0.751-0.830,SVM), 0.775 (95%CI: 0.736-0.813,LR) , results of the models, XGBoost performs best in predicting sepsis.Conclusions: By using the DT algorithm, a more accurate prediction model can be established. Amongst other ML methods, the XGBoost model demonstrated the best ability in detecting the sepsis of the patients in ICU.

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

Journal of Clinical Medicine ◽

10.3390/jcm7110428 ◽

2018 ◽

Vol 7 (11) ◽

pp. 428 ◽

Cited By ~ 31

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.

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

Scientific Reports ◽

10.1038/s41598-021-95019-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yeonhee Lee ◽

Jiwon Ryu ◽

Min Woo Kang ◽

Kyung Ha Seo ◽

Jayoun Kim ◽

...

Keyword(s):

Machine Learning ◽

Renal Cell Carcinoma ◽

Acute Kidney Injury ◽

Cell Carcinoma ◽

Renal Cell ◽

Kidney Injury ◽

Gradient Boosting ◽

Scoring Model ◽

Postoperative Aki ◽

Better Than

AbstractThe 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.

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Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach

BMJ Open Gastroenterology ◽

10.1136/bmjgast-2021-000761 ◽

2021 ◽

Vol 8 (1) ◽

pp. e000761

Author(s):

Hao Du ◽

Kewin Tien Ho Siah ◽

Valencia Zhang Ru-Yan ◽

Readon Teh ◽

Christopher Yu En Tan ◽

...

Keyword(s):

Machine Learning ◽

Critical Care ◽

Intensive Care ◽

Hospital Mortality ◽

Receiver Operating Curve ◽

Study Cohort ◽

Risk Of Death ◽

Severity Scores ◽

Mimic Iii ◽

Care Database

Research objectivesClostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of this study is to derive and validate a clinical prediction tool for CDI in-hospital mortality using a large critical care database.MethodologyThe demographics, clinical parameters, laboratory results and mortality of CDI were extracted from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. The individual performances of the models were compared against current severity scores (Clostridiodes difficile Associated Risk of Death Score (CARDS) and ATLAS (Age, Treatment with systemic antibiotics, leukocyte count, Albumin and Serum creatinine as a measure of renal function) by calculating area under receiver operating curve (AUROC). We identified factors associated with higher mortality risk in each model.Summary of resultsFrom 61 532 intensive care unit stays in the MIMIC-III database, there were 1315 CDI cases. The mortality rate for CDI in the study cohort was 18.33%. AUROC was 0.69 (95% CI, 0.60 to 0.76) for LR, 0.71 (95% CI, 0.62 to 0.77) for RF and 0.72 (95% CI, 0.64 to 0.78) for GBM, while previously AUROC was 0.57 (95% CI, 0.51 to 0.65) for CARDS and 0.63 (95% CI, 0.54 to 0.70) for ATLAS. Albumin, lactate and bicarbonate were significant mortality factors for all the models. Free calcium, potassium, white blood cell, urea, platelet and mean blood pressure were present in at least two of the three models.ConclusionOur machine learning derived CDI in-hospital mortality prediction model identified pertinent factors that can assist critical care clinicians in identifying patients at high risk of dying from CDI.

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

Gastroenterology Research and Practice ◽

10.1155/2020/3431290 ◽

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.

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Acute Kidney Injury Associated with Combination Antimicrobial Therapy in the Medical Information Mart for Intensive Care (MIMIC) III Database

Open Forum Infectious Diseases ◽

10.1093/ofid/ofx163.821 ◽

2017 ◽

Vol 4 (suppl_1) ◽

pp. S344-S344

Author(s):

W Cliff Rutter ◽

David S Burgess

Keyword(s):

Acute Kidney Injury ◽

Logistic Regression ◽

Intensive Care ◽

Critically Ill ◽

Critically Ill Patients ◽

Hepatorenal Syndrome ◽

Medical Information ◽

Kidney Injury ◽

Rifle Criteria ◽

Multivariable Logistic Regression

Abstract Background Increased acute kidney injury (AKI) incidence is linked with coadministration of vancomycin (VAN) and piperacillin-tazobactam (TZP) in the general hospital population when compared with VAN and cefepime (FEP); however, this phenomenon was not found in critically ill patients. Methods Patients receiving VAN in combination with FEP or TZP for at least 48 hours during an intensive care unit stay were included in this retrospective review. AKI was defined with the Risk, Injury, Failure, Loss, and End-stage (RIFLE) criteria. Exposure to common nephrotoxins was captured within 24 hours of combination therapy initiation through the entire treatment window. Basic descriptive statistics were performed, along with bivariable and multivariable logistic regression models of AKI odds. Results In total, 2230 patients were included, with 773 receiving FEP+VAN and 1457 receiving TZP+VAN. The groups were well balanced at baseline in most covariates, with the exception of hepatorenal syndrome diagnosis (TZP+VAN 1.4% vs. FEP+VAN 0.3%, P = 0.02) and vasopressor exposure (TZP+VAN 26.2% vs 21.5%, P = 0.01) being more common in the TZP+VAN group. Patients in the FEP+VAN group had a higher underlying severity of disease (Charlson comorbidity index [CCI] 2.7 vs. 2.3, P =0.0002). AKI incidence was higher in the TZP+VAN cohort (35.1% vs. 26.5%, P = 0.00004), with each stratification of the RIFLE criteria being higher. The time until onset of AKI was similar between groups (TZP+VAN median 1 [0–3] days vs. FEP+VAN 1 [0–4] days, P =0.2). After multivariable logistic regression, TZP+VAN therapy was associated with an adjust odds ratio (aOR) of AKI of 1.54 (95% confidence interval [CI] 1.25–1.89) compared with FEP+VAN. Other variables associated with increased odds of AKI included: age >= 65, duration of antibiotic therapy, higher baseline renal function, sepsis, endocarditis, hepatorenal syndrome, thiazide diuretic exposure, and increased CCI. Conclusion Treatment with TZP+VAN is associated with significant increases in AKI incidence among critically ill patients, independent of other risks for AKI. Disclosures All authors: No reported disclosures.

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