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Predicting target l...
Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models
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- Mamas, Mamas A. (författare)
- Keele Cardiovascular Research Group, Centre for Prognosis Research, Institutes of Applied Clinical Science and Primary Care and Health Sciences, Keele University, Keele ST5 5BG, Newcastle, UK
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- Roffi, Marco (författare)
- Department of Cardiology, University Hospitals Geneva, Geneva 1205, Switzerland
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- Fröbert, Ole, 1964- (författare)
- Örebro universitet,Institutionen för medicinska vetenskaper
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- Chieffo, Alaide (författare)
- Interventional Cardiology Unit, San Raffaele Scientific Institute, Milan 20132, Italy
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- Beneduce, Alessandro (författare)
- Interventional Cardiology Unit, San Raffaele Scientific Institute, Milan 20132, Italy
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- Matetic, Andrija (författare)
- Keele Cardiovascular Research Group, Centre for Prognosis Research, Institutes of Applied Clinical Science and Primary Care and Health Sciences, Keele University, Keele ST5 5BG, Newcastle, UK; Department of Cardiology, University Hospital of Split, Split 21000, Croatia
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- Tonino, Pim A. L. (författare)
- Department of Cardiology, Catharina Hospital, Eindhoven 5623, The Netherlands
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- Paunovic, Dragica (författare)
- Board of Directors, European Cardiovascular Research Centre (CERC), Massy 91300, France
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- Jacobs, Lotte (författare)
- Medical and Clinical Division, Terumo Europe NV, Leuven 3001, Belgium
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- Debrus, Roxane (författare)
- Biostatistics Division, Genmab A/S, Copenhagen 1560, Denmark
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- El Aissaoui, Jérémy (författare)
- Artificial Intelligence Division, Business and Decision, Woluwe St Lambert, Brusells 1200, Belgium
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- van Leeuwen, Frank (författare)
- Medical and Clinical Division, Terumo Europe NV, Leuven 3001, Belgium
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- Kontopantelis, Evangelos (författare)
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre (MAHSC), University of Manchester, Manchester M13 9PL, UK
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(creator_code:org_t)
- Oxford University Press, 2023
- 2023
- Engelska.
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Ingår i: The European Heart Journal - Digital Health. - : Oxford University Press. - 2634-3916. ; 4:6, s. 433-443
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- AIMS: Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting.METHODS AND RESULTS: Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69-0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk. CONCLUSION: Machine learning-derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted.REGISTRATION: Clinicaltrial.gov identifier is NCT02188355.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Nyckelord
- Drug-eluting stent
- Machine learning
- Outcomes
- Percutaneous coronary intervention
- Target lesion failure
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Mamas, Mamas A.
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Roffi, Marco
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Fröbert, Ole, 19 ...
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Chieffo, Alaide
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Beneduce, Alessa ...
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Matetic, Andrija
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Tonino, Pim A. L ...
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Paunovic, Dragic ...
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Jacobs, Lotte
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Debrus, Roxane
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El Aissaoui, Jér ...
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van Leeuwen, Fra ...
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Kontopantelis, E ...
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