Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Hansrivijit, Panupong; Vallabhajosyula, Saraschandra; Mekraksakit, Poemlarp; Kaewput, Wisit; Cheungpasitporn, Wisit

doi:10.3390/jcm9061767

Cited by 27 publications

(17 citation statements)

References 121 publications

(113 reference statements)

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Section: Discussionmentioning

confidence: 99%

“…22 Therefore, future approach using machine learning-assisted monitoring combining both clinical information and specific biomarkers is a novel and promising approach with better prediction of AKI after cardiac surgery or in patients suffering from cardiogenic shock than traditional, commonly used models. [23][24][25][26][27][28][29] Our study is not without limitations. Although prospective, it was a single center study; therefore, a prospective sample with a larger volume of patients to confirm our observations would be very important.…”

Section: Dovepressmentioning

confidence: 94%

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Long-Term Consequences of Increased Activity of Urine Enzymes After Cardiac Surgery – A Prospective Observational Study

Biernawska¹,

Kotfis²,

Szymańska-Pasternak³

et al. 2022

TCRM

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Introduction Cardiac surgery associated AKI (CSA-AKI) complicates recovery and may be associated with a greater risk of developing chronic kidney disease and mortality. The aim of this study was to assess long-term clinical consequences of transient increased activity of urinary enzymes after cardiac surgery (CS). Methods An observational study was conducted in a group of 88 adult patients undergoing planned coronary artery bypass grafting (CABG), but all samples were obtained from 79 patients. The activity of urinary enzymes: N-acetyl-beta-glucosaminidase (NAG), arylsulfatase A (ASA) and beta-glucuronidase was evaluated in sequential urine samples. A comparative analysis of biochemical parameters was performed regarding the occurrence of acute kidney injury (AKI) defined by KIDGO at 24 hours, at day 30 and 5-years after the operation. Results During the first 24 hours after CS AKI was diagnosed in 13 patients. A comparison of the activity of urinary enzymes in pre-defined time-points showed significant differences for ASA and NAG (post OP-sample p < 0.028 and p < 0.022; POD 1 sample p < 0.004 and p < 0.001 respectively). No patient had any biochemical or clinical features of kidney failure at day 30. In the AKI group kidney failure was diagnosed in 36% of patients within 5 years of follow-up as opposed to 5% in the no AKI group. The activities of tubular enzymes in urine reflect a general injury of kidney tubules during and after the operation. However, they are not ideal biomarkers for prediction of the degree of kidney injury and further poor prognosis of CS-AKI.

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Section: Discussionmentioning

confidence: 99%

Section: Dovepressmentioning

confidence: 94%

Long-Term Consequences of Increased Activity of Urine Enzymes After Cardiac Surgery – A Prospective Observational Study

Biernawska¹,

Kotfis²,

Szymańska-Pasternak³

et al. 2022

TCRM

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“…Many perioperative prediction models for AKI have been developed in recent years. However, the general utility of these models is poor due to differences in variable selection ( 7 , 12 , 13 ). Consequently, there is neither a consensus nor guidelines recommending the use of the existing predictive models for AKI after cardiac surgery.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for the prediction of acute kidney injury in patients after cardiac surgery

Xue

et al. 2022

Front. Surg.

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Cardiac surgery-associated acute kidney injury (CSA-AKI) is the most prevalent major complication of cardiac surgery and exerts a negative effect on a patient's prognosis, thereby leading to mortality. Although several risk assessment models have been developed for patients undergoing cardiac surgery, their performances are unsatisfactory. In this study, a machine learning algorithm was employed to obtain better predictive power for CSA-AKI outcomes relative to statistical analysis. In addition, random forest (RF), logistic regression with LASSO regularization, extreme gradient boosting (Xgboost), and support vector machine (SVM) methods were employed for feature selection and model training. Moreover, the calibration capacity and differentiation ability of the model was assessed using net reclassification improvement (NRI) along with Brier scores and receiver operating characteristic (ROC) curves, respectively. A total of 44 patients suffered AKI after surgery. Fatty acid-binding protein (FABP), hemojuvelin (HJV), neutrophil gelatinase-associated lipocalin (NGAL), mechanical ventilation time, and troponin I (TnI) were correlated significantly with the incidence of AKI. RF was the best model for predicting AKI (Brier score: 0.137, NRI: 0.221), evidenced by an AUC value of 0.858 [95% confidence interval (CI): 0.792–0.923]. Overall, RF exhibited the best performance as compared to other machine learning algorithms. These results thus provide new insights into the early identification of CSA-AKI.

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“…With the advancement of the electronic health record (EHR), machine learning (ML) approaches have been utilized to assist in clinical decision-making processes [8][9][10][11][12][13]. Consensus clustering is an unsupervised ML technique used to identify novel data patterns [14].…”

Section: Introductionmentioning

confidence: 99%

Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering

et al. 2021

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Background: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. Methods: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster’s key features. We assessed the association of each acute kidney injury cluster with hospital and one-year mortality. Results: Consensus clustering analysis identified four distinct clusters. There were 1201 (28%) patients in cluster 1, 1396 (33%) patients in cluster 2, 1191 (28%) patients in cluster 3, and 501 (12%) patients in cluster 4. Cluster 1 patients were the youngest and had the least comorbidities. Cluster 2 and cluster 3 patients were older and had lower baseline kidney function. Cluster 2 patients had lower serum bicarbonate, strong ion difference, and hemoglobin, but higher serum chloride, whereas cluster 3 patients had lower serum chloride but higher serum bicarbonate and strong ion difference. Cluster 4 patients were younger and more likely to be admitted for genitourinary disease and infectious disease but less likely to be admitted for cardiovascular disease. Cluster 4 patients also had more severe acute kidney injury, lower serum sodium, serum chloride, and serum bicarbonate, but higher serum potassium and anion gap. Cluster 2, 3, and 4 patients had significantly higher hospital and one-year mortality than cluster 1 patients (p < 0.001). Conclusion: Our study demonstrated using machine learning consensus clustering analysis to characterize a heterogeneous cohort of patients with acute kidney injury on hospital admission into four clinically distinct clusters with different associated mortality risks.

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Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Cited by 27 publications

References 121 publications

Long-Term Consequences of Increased Activity of Urine Enzymes After Cardiac Surgery – A Prospective Observational Study

Long-Term Consequences of Increased Activity of Urine Enzymes After Cardiac Surgery – A Prospective Observational Study

Machine learning for the prediction of acute kidney injury in patients after cardiac surgery

Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering

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