Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis

Luo, Xiao-Qin; Yan, Ping; Zhang, Ning-Ya; Luo, Bei; Wang, Mei; Deng, Ying-Hao; Wu, Ting; Wu, Xiaoxiang; Liu, Qian; Wang, Hongshen; Wang, Lin; Kang, Yixin; Duan, Shao‐Bin

doi:10.1038/s41598-021-99840-6

Cited by 32 publications

(40 citation statements)

References 59 publications

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“…Advanced machine learning methods are adept at handling high-order interactions and fitting complex non-linear relationships, which can be used to integrate large amounts of data from electronic health records (EHRs). The application of data-driven analytics by machine learning has shown promise to improve predictive performance in medical fields ( 12 – 15 ).…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury

et al. 2022

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BackgroundSepsis-associated acute kidney injury (SA-AKI) is common in critically ill patients, which is associated with significantly increased mortality. Existing mortality prediction tools showed insufficient predictive power or failed to reflect patients' dynamic clinical evolution. Therefore, the study aimed to develop and validate machine learning-based models for real-time mortality prediction in critically ill patients with SA-AKI.MethodsThe multi-center retrospective study included patients from two distinct databases. A total of 12,132 SA-AKI patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) were randomly allocated to the training, validation, and internal test sets. An additional 3,741 patients from the eICU Collaborative Research Database (eICU-CRD) served as an external test set. For every 12 h during the ICU stays, the state-of-the-art eXtreme Gradient Boosting (XGBoost) algorithm was used to predict the risk of in-hospital death in the following 48, 72, and 120 h and in the first 28 days after ICU admission. Area under the receiver operating characteristic curves (AUCs) were calculated to evaluate the models' performance.ResultsThe XGBoost models, based on routine clinical variables updated every 12 h, showed better performance in mortality prediction than the SOFA score and SAPS-II. The AUCs of the XGBoost models for mortality over different time periods ranged from 0.848 to 0.804 in the internal test set and from 0.818 to 0.748 in the external test set. The shapley additive explanation method provided interpretability for the XGBoost models, which improved the understanding of the association between the predictor variables and future mortality.ConclusionsThe interpretable machine learning XGBoost models showed promising performance in real-time mortality prediction in critically ill patients with SA-AKI, which are useful tools for early identification of high-risk patients and timely clinical interventions.

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Section: Introductionmentioning

confidence: 99%

Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury

et al. 2022

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“…The prediction models established by these studies were limited by only using the logistic regression method rather than other machine learning models to improve the predictive ability. Moreover, a study including 5984 septic patients with AKI established five prediction models, including logistic regression, random forest, support vector machine, artificial neural network, and extreme gradient boosting to predict persistent AKI [22]. The artificial neural network and logistic regression models achieved the highest AUC of 0.76.…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning models have already been applied in many fields, such as outcome prediction [17][18][19], and these models may potentially be used to identify high-risk patients. Machine learning models have been mostly described to predict episodes of the occurrence of AKI during sepsis [20][21][22]. However, there is no study to evaluate their effects on rehospitalization with AKI after patients who survived to discharge from sepsis.…”

Section: Introductionmentioning

confidence: 99%

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

Lee

Tsai

et al. 2022

JPM

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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.

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“…We used clinical indicators to predict the future risk of ESRD by using machine learning models. In previous studies that used AI to predict the progression of renal function, the primary endpoints were the occurrence of AKI or acute kidney disease following ICU admission, cardiovascular surgery, or sepsis [17][18][19]. Nevertheless, the follow-up period of 1-2 years in these studies was too short to effectively assess the long-term eGFR decline or progression to ESRD.…”

Section: Discussionmentioning

confidence: 99%

“…The application of artificial intelligence (AI) in the early prediction and risk stratification of AKI has attracted considerable research attention. Previous studies have demonstrated the use of machine learning algorithms for the early prediction of AKI 48 h after intensive care unit (ICU) admission and for distinguishing between transient and persistent AKI in patients following sepsis or cardiac surgery [17][18][19]. Nevertheless, these algorithms could not predict subsequent ESRD development.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Risk Prediction of End-Stage Renal Disease in Sepsis Survivors with Chronic Kidney Disease

Lee

Tsai

et al. 2022

Biomedicines

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Sepsis may lead to kidney function decline in patients with chronic kidney disease (CKD), and the deleterious effect may persist in patients who survive sepsis. We used a machine learning approach to predict the risk of end-stage renal disease (ESRD) in sepsis survivors. A total of 11,661 sepsis survivors were identified from a single-center database of 112,628 CKD patients between 2010 and 2018. During a median follow-up of 3.5 years, a total of 1366 (11.7%) sepsis survivors developed ESRD after hospital discharge. We adopted the random forest, extra trees, extreme gradient boosting, light gradient boosting machine (LGBM), and gradient boosting decision tree (GBDT) algorithms to predict the risk of ESRD development among these patients. GBDT yielded the highest area under the receiver operating characteristic curve of 0.879, followed by LGBM (0.868), and extra trees (0.865). The GBDT model revealed the strong effect of estimated glomerular filtration rates <25 mL/min/1.73 m2 at discharge in predicting ESRD development. In addition, hemoglobin and proteinuria were also essential predictors. Based on a large-scale dataset, we established a machine learning model computing the risk for ESRD occurrence among sepsis survivors with CKD. External validation is required to evaluate the generalizability of this model.

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Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis

Cited by 32 publications

References 59 publications

Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury

Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury

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

Artificial Intelligence for Risk Prediction of End-Stage Renal Disease in Sepsis Survivors with Chronic Kidney Disease

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