scholarly journals Prediction and Clinically Important Factors of Acute Kidney Injury Non-recovery

2022 ◽  
Vol 8 ◽  
Author(s):  
Chien-Liang Liu ◽  
You-Lin Tain ◽  
Yun-Chun Lin ◽  
Chien-Ning Hsu
Keyword(s):  
Acute Kidney Injury ◽  
Risk Model ◽  
Healthcare Delivery ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Healthcare Delivery System ◽  
Post Discharge ◽  
Electronic Health Record Data ◽  
Temporal Validation ◽  
Extreme Gradient Boosting

ObjectiveThis study aimed to identify phenotypic clinical features associated with acute kidney injury (AKI) to predict non-recovery from AKI at hospital discharge using electronic health record data.MethodsData for hospitalized patients in the AKI Recovery Evaluation Study were derived from a large healthcare delivery system in Taiwan between January 2011 and December 2017. Living patients with AKI non-recovery were used to derive and validate multiple predictive models. In total, 64 candidates variables, such as demographic characteristics, comorbidities, healthcare services utilization, laboratory values, and nephrotoxic medication use, were measured within 1 year before the index admission and during hospitalization for AKI.ResultsAmong the top 20 important features in the predictive model, 8 features had a positive effect on AKI non-recovery prediction: AKI during hospitalization, serum creatinine (SCr) level at admission, receipt of dialysis during hospitalization, baseline comorbidity of cancer, AKI at admission, baseline lymphocyte count, baseline potassium, and low-density lipoprotein cholesterol levels. The predicted AKI non-recovery risk model using the eXtreme Gradient Boosting (XGBoost) algorithm achieved an area under the receiver operating characteristic (AUROC) curve statistic of 0.807, discrimination with a sensitivity of 0.724, and a specificity of 0.738 in the temporal validation cohort.ConclusionThe machine learning model approach can accurately predict AKI non-recovery using routinely collected health data in clinical practice. These results suggest that multifactorial risk factors are involved in AKI non-recovery, requiring patient-centered risk assessments and promotion of post-discharge AKI care to prevent AKI complications.

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 Changing trends in dialysis modalities utilization and mortality in children, adolescents and young adults with acute kidney injury, 2010–2017

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
You-Lin Tain ◽  
Hsiao-Ching Kuo ◽  
Chien-Ning Hsu
Keyword(s):  
Acute Kidney Injury ◽  
Service Use ◽  
Pediatric Patients ◽  
Healthcare Delivery ◽  
Kidney Injury ◽  
Multivariate Logistic Regression Model ◽  
Intermittent Hemodialysis ◽  
Inpatient Setting ◽  
Healthcare Delivery System ◽  
Vasoactive Medication

AbstractThe aim of the study was to assess trends in the relative use of dialysis modalities in the hospital-based pediatric cohort and to determine risk factors associated with in-hospital morality among pediatric patients receiving dialysis for acute kidney injury (AKI). Patients aged < 20 years who received dialysis between 2010 and 2017 were identified from electronic health records databases of a Taiwan’s healthcare delivery system. The annual uses of intermittent hemodialysis (HD), continuous and automated peritoneal dialysis (PD) and continuous kidney replacement therapy (CKRT) were assessed using Cochran-Armitage Tests for trend. Among patients who received their first dialysis as inpatients for AKI, a multivariate logistic regression model was employed to assess mortality risks associated with dialysis modalities, patient demographics, complexity of baseline chronic disease, and healthcare service use during their hospital stays. Kidney dialysis was performed 37.9 per patient per year over the study period. Intermittent hemodialysis (HD) (73.3%) was the most frequently used dialysis modality. In the inpatient setting, the relative annual use of CKRT increased over the study period, while HD use concomitantly declined (P < 0.0001). The overall in-hospital mortality rate after dialysis for AKI was 33.6%, which remained steady over time (P = 0.2411). Patients aged < 2 years [adjusted odds ratio: (aOR) 3.36; 95% confidence interval (CI) 1.34–8.93] and greater vasoactive regimen use (aOR: 17.1; 95% CI: 5.3–55.21) were significantly associated with dialysis-related mortality. Overall treatment modality used for dialysis in pediatric patients increased slowly in the study period, and HD and CRKT modality uses largely evolved in the inpatient setting. Younger ages and use of more vasoactive medication regimens were independently associated with increased early mortality in patients on AKI-dialysis.

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 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 Comparison of Approaches for Prediction of Renal Replacement Therapy-Free Survival in Patients with Acute Kidney Injury

2021 ◽  
pp. 1-7
Author(s):  
Pattharawin Pattharanitima ◽  
Akhil Vaid ◽  
Suraj K. Jaladanki ◽  
Ishan Paranjpe ◽  
Ross O’Hagan ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Kidney Injury ◽  
Support Vector ◽  
Adaptive Boosting ◽  
Free Survival ◽  
Extreme Gradient Boosting

Background/Aims: Acute kidney injury (AKI) in critically ill patients is common, and continuous renal replacement therapy (CRRT) is a preferred mode of renal replacement therapy (RRT) in hemodynamically unstable patients. Prediction of clinical outcomes in patients on CRRT is challenging. We utilized several approaches to predict RRT-free survival (RRTFS) in critically ill patients with AKI requiring CRRT. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify patients ≥18 years old with AKI on CRRT, after excluding patients who had ESRD on chronic dialysis, and kidney transplantation. We defined RRTFS as patients who were discharged alive and did not require RRT ≥7 days prior to hospital discharge. We utilized all available biomedical data up to CRRT initiation. We evaluated 7 approaches, including logistic regression (LR), random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and MLP with long short-term memory (MLP + LSTM). We evaluated model performance by using area under the receiver operating characteristic (AUROC) curves. Results: Out of 684 patients with AKI on CRRT, 205 (30%) patients had RRTFS. The median age of patients was 63 years and their median Simplified Acute Physiology Score (SAPS) II was 67 (interquartile range 52–84). The MLP + LSTM showed the highest AUROC (95% CI) of 0.70 (0.67–0.73), followed by MLP 0.59 (0.54–0.64), LR 0.57 (0.52–0.62), SVM 0.51 (0.46–0.56), AdaBoost 0.51 (0.46–0.55), RF 0.44 (0.39–0.48), and XGBoost 0.43 (CI 0.38–0.47). Conclusions: A MLP + LSTM model outperformed other approaches for predicting RRTFS. Performance could be further improved by incorporating other data types.

Paucity of renal follow-up by school age after neonatal cardiac surgery

2020 ◽  
Vol 30 (6) ◽  
pp. 822-828
Author(s):  
Sara Rodriguez-Lopez ◽  
Louis Huynh ◽  
Kelly Benisty ◽  
Adrian Dancea ◽  
Daniel Garros ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Cardiac Surgery ◽  
Glomerular Filtration Rate ◽  
Glomerular Filtration ◽  
Filtration Rate ◽  
Kidney Injury ◽  
Post Discharge ◽  
Heart Repair ◽  
Neonatal Cardiac Surgery

AbstractIntroduction:There are little data about renal follow-up of neonates after cardiovascular surgery and no guidelines for long-term renal follow-up. Our objectives were to assess renal function follow-up practice after neonatal cardiac surgery, evaluate factors that predict follow-up serum creatinine measurements including acute kidney injury following surgery, and evaluate the estimated glomerular filtration rate during follow-up using routinely collected laboratory values.Methods:Two-centre retrospective cohort study of children 5–7 years of age with a history of neonatal cardiac surgery. Univariable and multivariable analyses were performed to determine factors associated with post-discharge creatinine measurements. Glomerular filtration rate was estimated for each creatinine using a height-independent equation.Results:Seventeen of 55 children (30%) did not have any creatinine measured following discharge after surgery until the end of study follow-up, which occurred at a median time of 6 years after discharge. Of the 38 children who had the kidney function checked, 15 (40%) had all of their creatinine drawn only in the context of a hospitalisation or emergency department visit. Acute kidney injury following surgery did not predict the presence of follow-up creatinine measurements.Conclusions:A large proportion of neonates undergoing congenital heart repair did not have a follow-up creatinine measured in the first years following surgery. In those that did have a creatinine measured, there did not appear to be any identified pattern of follow-up. A follow-up system for children who are discharged from cardiac surgery is needed to identify children with or at risk of chronic kidney disease.

Sign in / Sign up

Export Citation Format

Share Document