Development of a new mortality scoring system for acute kidney injury with continuous renal replacement therapy

Kim, Yaerim; Park, Nanhee; Kim, Jayoun; Chin, Ho Jun; Na, Ki Young; Joo, Kwon Wook; Kim, Yon Su; Kim, Sejoong; Han, Seung Seok

doi:10.1111/nep.13661

Cited by 22 publications

(33 citation statements)

References 30 publications

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“…Nevertheless, it is necessary to develop a mortality prediction model because a few clinical variables may not be sufficient to precisely predict patient outcome. Recently, our MOSAIC model achieved suitable performance with respect to mortality prediction for patients receiving CRRT (AUC = 0.772), but the approach requires further validation and the addition of new variables may be difficult [22]. Machine learning algorithms may solve these problems and will have the added benefit of increased accuracy with the accumulation of data.…”

Section: Discussionmentioning

confidence: 99%

“…The laboratory data such as white blood cell count, hemoglobin, blood urea nitrogen, creatinine, albumin, pH, sodium, and potassium were measured at the time of starting CRRT. APACHE II, SOFA, and MOSAIC scores were calculated based on the calculation methods presented in the original studies [13,14,22]. The primary output was the ICU mortality, and the discontinuation of CRRT was censored.…”

Section: Study Variablesmentioning

confidence: 99%

“…Conventional scoring models such as APACHE II and SOFA show limitations, for example, a low prediction accuracy for the CRRT subset and difficulty of adding new variables to the models. Our new abbreviated mortality scoring system for AKI with CRRT (MOSAIC model) has not been validated in other cohorts despite a high prediction accuracy of mortality for the CRRT subset [22]. Because of the success of machine learning in other clinical applications, the study explored whether machine learning algorithms are also applicable for predicting the mortality of patients initiating CRRT for AKI.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy

Kang

Kim

et al. 2020

Crit Care

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Background Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing continuous renal replacement therapy (CRRT) for severe acute kidney injury. Accordingly, the present study applies machine learning algorithms to improve prediction accuracy for this patient subset. Methods We randomly divided a total of 1571 adult patients who started CRRT for acute kidney injury into training (70%, n = 1094) and test (30%, n = 477) sets. The primary output consisted of the probability of mortality during admission to the intensive care unit (ICU) or hospital. We compared the area under the receiver operating characteristic curves (AUCs) of several machine learning algorithms with that of the APACHE II, SOFA, and the new abbreviated mortality scoring system for acute kidney injury with CRRT (MOSAIC model) results. Results For the ICU mortality, the random forest model showed the highest AUC (0.784 [0.744–0.825]), and the artificial neural network and extreme gradient boost models demonstrated the next best results (0.776 [0.735–0.818]). The AUC of the random forest model was higher than 0.611 (0.583–0.640), 0.677 (0.651–0.703), and 0.722 (0.677–0.767), as achieved by APACHE II, SOFA, and MOSAIC, respectively. The machine learning models also predicted in-hospital mortality better than APACHE II, SOFA, and MOSAIC. Conclusion Machine learning algorithms increase the accuracy of mortality prediction for patients undergoing CRRT for acute kidney injury compared with previous scoring models.

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Section: Discussionmentioning

confidence: 99%

Section: Study Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy

Kang

Kim

et al. 2020

Crit Care

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“…AKI adds a significant burden to critically ill patients and ultimately leads to a poor outcome and high mortality. Among several risk factors in this patient subset, an abnormal blood pressure range has been included in the scoring system of outcome prediction [9][10][11]. Although CRRT provides gentle modification of electrolyte, acid-base, and fluid imbalance with hemodynamic tolerance compared with intermittent hemodialysis, early exposure to hypotension during CRRT may be associated with high mortality [6].…”

Section: Discussionmentioning

confidence: 99%

Target value of mean arterial pressure in patients undergoing continuous renal replacement therapy due to acute kidney injury

et al. 2021

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Background Although patients undergoing continuous renal replacement therapy (CRRT) due to acute kidney injury (AKI) frequently have instability in mean arterial pressure (MAP), no consensus exists on the target value of MAP related to high mortality after CRRT. Methods A total of 2,292 patients who underwent CRRT due to AKI in three referral hospitals were retrospectively reviewed. The MAPs were divided into tertiles, and the 3rd tertile group served as a reference in the analyses. The major outcome was all-cause mortality during the intensive care unit period. The odds ratio (OR) of mortality was calculated using logistic regression after adjustment for multiple covariates. The nonlinear relationship regression model was applied to determine the threshold value of MAP related to increasing mortality. Results The mean value of MAP was 80.7 ± 17.3 mmHg at the time of CRRT initiation. The median intensive care unit stay was 5 days (interquartile range, 2–12 days), and during this time, 1,227 (55.5%) patients died. The 1st tertile group of MAP showed an elevated risk of mortality compared with the 3rd tertile group (adjusted OR, 1.28 [1.03–1.60]; P = 0.029). In the nonlinear regression analysis, the threshold value of MAP was calculated as 82.7 mmHg. Patients with MAP < 82.7 mmHg had a higher mortality rate than those with ≥ 82.7 mmHg (adjusted OR, 1.21 [1.01–1.45]; P = 0.037). Conclusions Low MAP at CRRT initiation is associated with a high risk of mortality, particularly when it is < 82.7 mmHg. This value may be used for risk classification and as a potential therapeutic target.

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“…The Glasgow coma scales were calculated. The SOFA, APACHE II, and MOSAIC scores were measured based on the methods presented in the original studies [14][15][16].…”

Section: Study Variables and Outcomesmentioning

confidence: 99%

Machine Learning Model to Predict Hypotension After Starting Continuous Renal Replacement Therapy

Kang

Kim

et al. 2020

Preprint

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Background: Hypotension after starting continuous renal replacement therapy (CRRT) is associated with worse outcome, but it is difficult to predict because several factors have interactive and complex effects on the risk. The present study applied machine learning algorithms to develop models to predict hypotension after initiating CRRT.Methods: Among 2,349 adult patients who started CRRT due to acute kidney injury, 70% and 30% were randomly assigned into the training and testing sets, respectively. Hypotension was defined as a reduction in mean arterial pressure (MAP) ≥20 mmHg from the initial value within 6 hours. The area under the receiver operating characteristic curves (AUROCs) in machine learning models, such as support vector machine (SVM), deep neural network (DNN), and light gradient boosting machine (LGBM), were compared with those in disease-severity scores such as the Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation II.Results: The DNN model showed the highest AUROC (0.822 [0.789–0.856]), and the LGBM and SVM models followed with AUROCs of 0.810 (0.776–0.845) and 0.807 (0.772–0.842), respectively; all machine learning AUROC values were higher than those obtained from disease-severity scores (AUROCs <0.6). Although different definitions of hypotension were used such as a reduction of MAP ≥30 mmHg or a reduction occurring within 1 hour, the AUROCs of machine learning models were higher than those of disease-severity scores. These machine learning models were well calibrated.Conclusion: Machine learning models successfully predict hypotension after starting CRRT and can serve as the basis of systems to predict hypotension before starting CRRT.

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Development of a new mortality scoring system for acute kidney injury with continuous renal replacement therapy

Cited by 22 publications

References 30 publications

Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy

Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy

Target value of mean arterial pressure in patients undergoing continuous renal replacement therapy due to acute kidney injury

Machine Learning Model to Predict Hypotension After Starting Continuous Renal Replacement Therapy

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