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Abstract
Objectives 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. Our objective was to develop and validate a set of artificial neural networks (ANN) models to predict five adverse outcomes after PCI—AKI, bleeding, stroke, death and any adverse outcome.
Methods We conducted a study of 28 005 patients (training and test cohorts of 21 004 and 7001 patients, respectively) undergoing PCI at five hospitals in the Barnes-Jewish Hospital system. We used an ANN multi-layer perceptron (MLP) architecture based on a set of 278 preprocessed variables. Model accuracy was tested using area under the receiver operating-characteristic curve (AUC). Improved prediction by the MLP model was assessed using integrated discrimination improvement (IDI) and Brier score.
Results The fully trained MLP model achieved convergence quickly (<10 epochs) and could accurately predict AKI (77.9%), bleeding (86.5%), death (90.3%) and any adverse outcome (80.6%) in the independent test set. Prediction of stroke was not satisfactory (69.9%). Compared with the currently used models for AKI, bleeding and death prediction, our models showed a significantly higher AUC, IDI and Brier score.
Conclusions 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.
- cardiovascular diseases
- myocardial ischaemia
- integrative medicine
Data availability statement
No data are available. The patient-level data used in this study is confidential and cannot be shared.
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Data availability statement
No data are available. The patient-level data used in this study is confidential and cannot be shared.
Footnotes
Contributors APA and HK conceptualised the study, conducted analyses, wrote the manuscript and approved the final draft.
Funding Funding for this work was obtained from an unrestricted grant from Terumo Medical Corporation.
Disclaimer No sponsor participated in the design and conduct of the study, collection, analysis, or interpretation of the data, nor in the preparation, review, nor approval of the manuscript.
Competing interests APA has received funding via a comparative effectiveness research KM1 career development award from the Clinical and Translational Science Award (CTSA) programme of the National Center for Advancing Translational Sciences of the National Institutes of Health, Grant Numbers UL1TR000448, KL2TR000450, TL1TR000449 and the National Cancer Institute of the National Institutes of Health, Grant Number 1KM1CA156708-01; an AHRQ R18 grant award (Grant Number R18HS0224181-01A1), has received an unrestricted grant from Volcano corporation, and MedAxiom Synergistic Healthcare Solutions Austin, Texas and is a consultant to Terumo, GE Healthcare and AstraZeneca. HK provides research consultancy to MedAxiom—Synergistic Healthcare Solutions Austin, Texas.
Provenance and peer review Not commissioned; externally peer reviewed.
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