Development and Validation of a Model for Predicting the Risk of Acute Kidney Injury Associated With Contrast Volume Levels During Percutaneous Coronary Intervention

Huang, Chenxi; Li, Shuxia; Mahajan, Shiwani; Testani, Jeffrey M.; Wilson, F. Perry; Mena, Carlos; Masoudi, Frederick A.; Rumsfeld, John S.; Spertus, John A.; Mortazavi, Bobak J.; Krumholz, Harlan M.

doi:10.1001/jamanetworkopen.2019.16021

Cited by 30 publications

(20 citation statements)

References 34 publications

(64 reference statements)

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“…A comparison of our predictive models with the current standard-of-care predicting models showed a significant improvement in accuracy, discrimination and reclassification. Recently, Huang et al (21) published an elegant model to predict AKI after PCI using the large NCDR CathPCI dataset emanating from 1,694 US hospitals. They employed a generalized additive model to account for the potential nonlinear relationships.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Percutaneous Coronary Intervention: Improved Prediction of Pci-Related Complications Using an Artificial Neural Network

Kulkarni¹,

Amin

2020

Preprint

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Importance: Complications after percutaneous coronary intervention (PCI) are common and costly. Risk models for predicting the likelihood of acute kidney injury (AKI), bleeding, stroke and death are limited by accuracy and inability to use non-linear relationships among predictors. Additionally, if non-linear relationships among predictors can be leveraged, then the prediction of any adverse event (i.e. the patient who will not do well with PCI) is perhaps of greater interest to clinicians than prediction of adverse events in isolation. Objective: To develop and validate a set of artificial neural networks (ANN) models to predict five adverse outcomes after PCI - AKI, bleeding, stroke, death and one or more of these four (any adverse outcome). Design: Cross-sectional study, using institutional NCDR CathPCI data. Setting and participants: 28,005 patients undergoing PCI at five hospitals in the Barnes-Jewish Hospital system. Main Outcome(s): AKI, bleeding, stroke, death, and one or more of these four (any adverse outcome). We divided 28,005 PCI patients into a training cohort of 21,004 (75%) and a test cohort of 7,001 (25%). We used an artificial neural network (ANN) multilayer perceptron (MLP) model to predict each outcome based on a set of 278 encoded and preprocessed variables. Model accuracy was tested using area under the receiver-operating-characteristic curve (AUC). Performance and validation of the MLP model was compared with existing regression models using integrated discrimination improvement (IDI) and continuous net reclassification index (NRI). Results: The prevalence of AKI, bleeding, stroke and death in the study cohort was 4.6%, 3.6%, 0.3% and 1.1%, respectively. The fully trained MLP model achieved convergence quickly (<10 epochs) and could predict accurately predict AKI (77.9%), bleeding (86.5%), death (90.3%) and any adverse outcome (80.6%) in the independent test set. However, prediction of stroke was not satisfactory (69.9%). Compared to the currently used models for AKI, bleeding and death prediction, our models showed a significantly higher AUC (range 1.6% - 5.6%), IDI (range 4.9% - 7.2%) and NRI (range 0.07 - 0.61). Conclusions and Relevance: By using neural network-based models, we accurately predict major adverse events after PCI. Larger studies for replicability and longitudinal studies for evidence of impact are needed to establish these artificial intelligence methods in current PCI practice.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Percutaneous Coronary Intervention: Improved Prediction of Pci-Related Complications Using an Artificial Neural Network

Kulkarni¹,

Amin

2020

Preprint

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“…In another retrospective study of 2,076,694 patients submitted to percutaneous coronary intervention, Huang et al applied an ML method to predict AKI risk according to contrast volume [110]. The generalized additive model produced an AUROC of 0.777 (95% CI, 0.775-0.779) for predicting the risk of a creatinine level increase of at least 0.3 mg/dL [110]. The model was developed from a random 50% of the cohort, and performance was evaluated in the remaining 50% of the cohort.…”

Section: The Era Of Artificial Intelligencementioning

confidence: 99%

“…The model was developed from a random 50% of the cohort, and performance was evaluated in the remaining 50% of the cohort. The association of contrast volume with AKI risk was nonlinear, and this model proved useful to quantify individual risk and adjust contrast volume to decrease AKI risk [110].…”

Section: The Era Of Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence in Acute Kidney Injury Risk Prediction

Gameiro

Branco

Lopes

2020

JCM

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Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is associated with worse short and long-term outcomes. It is crucial to develop methods to identify patients at risk for AKI and to diagnose subclinical AKI in order to improve patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed for the development of artificial intelligence predictive models of risk estimation in AKI. In this review, we discussed the progress of AKI risk prediction from risk scores to electronic alerts to machine learning methods.

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“…В настоящее время разработаны различные шкалы расчета риска развития контраст-ассоциированного ОПП, включающие как сведения, доступные до проведения ангиографии [5,6], так и такие данные, как количество введенного контрастного вещества, доступные лишь после проведения вмешательства [7,8]. Однако по-прежнему точное сочетание неблагоприятных факторов у пациентов с большим количеством сопутствующих заболеваний остается неизвестным.…”

Section: ââåäåíèåunclassified

“…КИ-ОПП относится к синдромам, развития которых можно не допустить при верной и тщательной оценке множества факторов риска. Многие исследования, в том числе проводимые отечественными учеными [8,9], являются ретроспективными. Такой подход позволяет оценить большее количество данных, но обладает и своими ограничениями.…”

Section: îáñóAeäåíèåunclassified

Contrast-induced acute kidney injury in patients with stable coronary artery disease: the most important risk factors and prevalence

Mironova

Staroverov

Sivakova

et al. 2020

Terapevticheskii arkhiv

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Aim.The aim of our study was to assess the prevalence of contrast-induced acute kidney injury (CI-AKI) in patients with stable coronary artery disease (CAD) receiving optimal medical treatment with indications to coronary angiography and intraarterial administration of contrast agents. Materials and methods.1023 patients with stable CAD were included in the open prospective observational cohort study. The CI-AKI was defined as a rise in serum creatinine 25% from baseline. The mean age of the study group was 61.710.1 years; 72.4% were males and 84.4% had arterial hypertension. A multiple logistic regression model of prediction of CI-AKI was created. Results.CI-AKI developed in 132 (12.9%) of the patients. The multiple logistic regression model included gender, BMI, weight, age, heart failure, diabetes mellitus, arterial hypertension, anemia, hyperuricemia, proteinuria and baseline serum creatinine. Area under the curve for the model was 0.749 (95% confidence interval 0.7030,795;p0.0001). When trying to build a prognostic model, including baseline GFR and contrast volume, the model lost significance and the AUC diminished. Conclusion.The CI-AKI remains quite a common kidney injury developing in patients with stable CAD undergoing percutaneous interventions. Several risk factors need to be assessed very carefully before any intervention requiring intraarterial contrast media administration especially in patients with comorbidities.

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Development and Validation of a Model for Predicting the Risk of Acute Kidney Injury Associated With Contrast Volume Levels During Percutaneous Coronary Intervention

Cited by 30 publications

References 34 publications

Artificial Intelligence in Percutaneous Coronary Intervention: Improved Prediction of Pci-Related Complications Using an Artificial Neural Network

Artificial Intelligence in Percutaneous Coronary Intervention: Improved Prediction of Pci-Related Complications Using an Artificial Neural Network

Artificial Intelligence in Acute Kidney Injury Risk Prediction

Contrast-induced acute kidney injury in patients with stable coronary artery disease: the most important risk factors and prevalence

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