Acute Kidney Injury

Solomon, Richard; Dauerman, Harold L.

doi:10.1161/circinterventions.116.004742

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…AKI is a common complication among patients undergoing interventional procedures, with a reported incidence of up to 30% 26 . AKI is directly associated with both a fivefold increase in intrahospital mortality and an increased risk of end‐stage renal failure in the long term 27 . An updated simple risk score (Mehran 2 CA‐AKI risk score) that included age, clinical presentation, eGFR, congestive heart failure, diabetes, hemoglobin, basal glucose, and left ventricular ejection fraction could accurately discriminate the risk of AKI, and AKI occurrence was strongly associated with 30‐day all‐cause mortality (HR: 14.64, 95% CI: 10.04−21.34) 28 .…”

Section: Discussionmentioning

confidence: 99%

“…26 AKI is directly associated with both a fivefold increase in intrahospital mortality and an increased risk of end-stage renal failure in the long term. 27 An updated simple risk score ( 28 Hence, this score could be widely utilized in the clinic based on traditional methods. However, the logistic regression model is theory-driven and requires several putative conditions, such as multiple collinearity problems, a linear relationship between the dependent and logit value of the independent variable, and no outliers.…”

Section: Aki Is a Common Complication Among Patients Undergoingmentioning

confidence: 99%

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Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study

Song,

Li,

Nie

et al. 2023

Clinical Cardiology

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BackgroundUp to 30% of patients with acute coronary syndrome (ACS) die from adverse events, mainly renal failure and myocardial infarction (MI). Accurate prediction of adverse events is therefore essential to improve patient prognosis.HypothesisMachine learning (ML) methods can accurately identify risk factors and predict adverse events.MethodsA total of 5240 patients diagnosed with ACS who underwent PCI were enrolled and followed for 1 year. Support vector machine, extreme gradient boosting, adaptive boosting, K‐nearest neighbors, random forest, decision tree, categorical boosting, and linear discriminant analysis (LDA) were developed with 10‐fold cross‐validation to predict acute kidney injury (AKI), MI during hospitalization, and all‐cause mortality within 1 year. Features with mean Shapley Additive exPlanations score >0.1 were screened by XGBoost method as input for model construction. Accuracy, F1 score, area under curve (AUC), and precision/recall curve were used to evaluate the performance of the models.ResultsOverall, 2.6% of patients died within 1 year, 4.2% had AKI, and 4.7% had MI during hospitalization. The LDA model was superior to the other seven ML models, with an AUC of 0.83, F1 score of 0.90, accuracy of 0.85, recall of 0.85, specificity of 0.68, and precision of 0.99 in predicting all‐cause mortality. For AKI and MI, the LDA model also showed good discriminating capacity with an AUC of 0.74.ConclusionThe LDA model, using easily accessible variables from in‐hospital patients, showed the potential to effectively predict the risk of adverse events and mortality within 1 year in ACS patients after PCI.

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Section: Discussionmentioning

confidence: 99%

Section: Aki Is a Common Complication Among Patients Undergoingmentioning

confidence: 99%

Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study

Song,

Li,

Nie

et al. 2023

Clinical Cardiology

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Retinal Vascular Density as A Novel Biomarker of Acute Renal Injury after Acute Coronary Syndrome

Alan

Guenancia

Arnould

et al. 2019

Sci Rep

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Iodinated contrast agent (ICA)-induced acute kidney injury (AKI) following acute coronary syndrome (ACS) is a frequent complication, which may lead to chronic kidney disease and increased mortality. Optical coherence tomography angiography (OCT-A) of the retina is new tool delivering a rapid and noninvasive assessment of systemic microvascularization, which is potentially involved in the occurrence of ICA-induced AKI. Between October 2016 and March 2017, 452 ACS patients were admitted to our cardiac intensive care unit. OCT-A was performed within 48 h after the ICA injection. Patients with a history of retinal disease were excluded. The patients included were divided into two groups depending on whether or not AKI occurred after injection of ICA, according to KDIGO criteria. Of the 216 patients included, 21 (10%) presented AKI. AKI was significantly associated with age, Mehran score, GRACE score, and NT-proBNP. AKI patients had significantly lower retinal vascular density (RVD)) and had more frequent low RVD (81% vs 45%, P = 0.002). Adding low RVD to the Mehran score and the NT-proBNP, or to the GRACE score and the NT-proBNP, significantly improved their predictive values, suggesting that systemic microvascular involvement remains incompletely addressed by either standard risk scores or factors known to be associated with ICA-induced AKI.

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Oral hydration

Solomon¹,

Lahoud

2018

Coronary Artery Disease

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Acute Kidney Injury

Cited by 6 publications

References 21 publications

Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study

Using machine learning to predict adverse events in acute coronary syndrome: A retrospective study

Retinal Vascular Density as A Novel Biomarker of Acute Renal Injury after Acute Coronary Syndrome

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