Mortality and Recovery Associated with Kidney Failure due to Acute Kidney Injury

Shah, Silvi; Leonard, Anthony; Harrison, Kathleen; Meganathan, Karthikeyan; Christianson, Annette; Thakar, Charuhas V.

doi:10.2215/cjn.11200919

Cited by 46 publications

(46 citation statements)

References 32 publications

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“…Previous studies showed that early detection and treatment of AKI can help most patients recover renal function and reach a better clinical outcome [ 41 , 42 ]. Therefore, it is particularly important to identify the risk factors and prognostic factors for acute pancreatitis with acute renal injury in the early stage, so as to develop a predictive model to help clinicians take preventive intervention measures and avoid renal function damage [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

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

Cheng

Mei

et al. 2020

Gastroenterology Research and Practice

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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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Section: Discussionmentioning

confidence: 99%

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

Cheng

Mei

et al. 2020

Gastroenterology Research and Practice

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“…Our ultimate goal is to advise nephrologists about the potential reversible predictors of outcomes to help improve the outcomes of these patients (e.g., improve specific laboratory parameters, optimize dialysis prescription and adequately manage reversible comorbidities). A recent study that evaluated a cohort of more than a million incident dialysis patients over 10 years (1/1/2005-12/31/2014) using the US Renal Data System (USRDS), of whom 1.1% were on dialysis secondary to AKI (n = 11,498), showed that women, non-whites (Blacks, Asians, Hispanics and Native Americans), and elderly patients are less likely to achieve kidney recovery compared to men, whites and young patients, respectively [12]. Similarly, Foley and colleagues showed a lower recovery rate of kidney function in women, Blacks, and Hispanics compared to men, Whites, and non-Hispanics, respectively [13].…”

Section: Introductionmentioning

confidence: 99%

Determinants of Outcomes of Acute Kidney Injury: Clinical Predictors and Beyond

Abdel‐Rahman

Turgut

Gautam

et al. 2021

JCM

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Acute kidney injury (AKI) is a common clinical syndrome characterized by rapid impairment of kidney function. The incidence of AKI and its severe form AKI requiring dialysis (AKI-D) has been increasing over the years. AKI etiology may be multifactorial and is substantially associated with increased morbidity and mortality. The outcome of AKI-D can vary from partial or complete recovery to transitioning to chronic kidney disease, end stage kidney disease, or even death. Predicting outcomes of patients with AKI is crucial as it may allow clinicians to guide policy regarding adequate management of this problem and offer the best long-term options to their patients in advance. In this manuscript, we will review the current evidence regarding the determinants of AKI outcomes, focusing on AKI-D.

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“…Our study highlights diabetes as a risk factor for the development of AKD in patients with AKI, which is consistent with many other studies. [19][20][21] See et al 20 reported that diabetes was associated with poor long-term outcomes of AKI in a meta-analysis of cohort studies. In a Canadian population-based cohort study (n=190,714), Pannu et al found that participants with complicated diabetes were less likely to recover renal function.…”

Section: Main Risk Factors For Akd In Patients With Aki Diabetesmentioning

confidence: 99%

Diabetes Mellitus as a Risk Factor for Progression from Acute Kidney Injury to Acute Kidney Disease: A Specific Prediction Model

Zhao

Liang

Pan

et al. 2021

DMSO

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Acute kidney injury is very common in hospitalized patients and carries a significant risk of mortality. Although timely intervention may improve patient prognosis, studies on the development of acute kidney disease in patients with acute kidney injury remain scarce. Thus, we constructed a prediction model to identify patients likely to develop acute kidney disease. Patients and Methods: Among 474 patients screened for eligibility, 261 were enrolled and randomly divided into training (185 patients) and independent validation cohorts (76 patients). Least absolute shrinkage and selection operator regression and multivariate logistic regression analyses were used to select features and build a nomogram incorporating the selected predictors: diabetes, anemia, oliguria, and peak creatinine. Calibration, discrimination, and the clinical usefulness of the model were assessed using calibration plots, the C-index, receiver operating characteristic curves, and decision curve analysis. Results: Diabetes was significantly associated with the presence of AKD. Peak creatinine, oliguria, and anemia also contributed to the progression of acute kidney injury. The model displayed good predictive power with a C-index of 0.834 and an AUC of 0.834 (95% confidence interval (CI): 0.773-0.895) in the training cohort and a C-index of 0.851 and an AUC of 0.851 (95% CI: 0.753-0.949) in the validation cohort. The calibration curves also showed that the model had a medium ability to predict acute kidney disease risk. Decision curve analysis showed that the nomogram was clinically useful when interventions were decided at the possibility threshold of 22%. Conclusion: This novel prediction nomogram may allow for convenient prediction of acute kidney disease in patients with acute kidney injury, which may help to improve outcomes.

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Mortality and Recovery Associated with Kidney Failure due to Acute Kidney Injury

Cited by 46 publications

References 32 publications

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

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

Determinants of Outcomes of Acute Kidney Injury: Clinical Predictors and Beyond

Diabetes Mellitus as a Risk Factor for Progression from Acute Kidney Injury to Acute Kidney Disease: A Specific Prediction Model

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