Felix, SEA;
Bagheri, A;
Ramjankhan, FR;
Spruit, MR;
Oberski, D;
De Jonge, N;
Van Laake, LW;
... Asselbergs, FW; + view all
(2021)
A data mining-based cross-industry process for predicting major bleeding in mechanical circulatory support.
European Heart Journal - Digital Health
, 2
(4)
pp. 635-642.
10.1093/ehjdh/ztab082.
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Abstract
Aims: Over a third of patients, treated with mechanical circulatory support (MCS) for end-stage heart failure, experience major bleeding. Currently, the prediction of a major bleeding in the near future is difficult because of many contributing factors. Predictive analytics using data mining could help calculating the risk of bleeding; however, its application is generally reserved for experienced researchers on this subject. We propose an easily applicable data mining tool to predict major bleeding in MCS patients. Methods and results: All data of electronic health records of MCS patients in the University Medical Centre Utrecht were included. Based on the cross-industry standard process for data mining (CRISP-DM) methodology, an application named Auto-Crisp was developed. Auto-Crisp was used to evaluate the predictive models for a major bleeding in the next 3, 7, and 30 days after the first 30 days post-operatively following MCS implantation. The performance of the predictive models is investigated by the area under the curve (AUC) evaluation measure. In 25.6% of 273 patients, a total of 142 major bleedings occurred during a median follow-up period of 542 [interquartile range (IQR) 205-1044] days. The best predictive models assessed by Auto-Crisp had AUC values of 0.792, 0.788, and 0.776 for bleedings in the next 3, 7, and 30 days, respectively. Conclusion: The Auto-Crisp-based predictive model created in this study had an acceptable performance to predict major bleeding in MCS patients in the near future. However, further validation of the application is needed to evaluate Auto-Crisp in other research projects.
Type: | Article |
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Title: | A data mining-based cross-industry process for predicting major bleeding in mechanical circulatory support |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1093/ehjdh/ztab082 |
Publisher version: | https://doi.org/10.1093/ehjdh/ztab082 |
Language: | English |
Additional information: | © The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
Keywords: | Mechanical circulatory support • Cross-industry standard process for data mining (CRISP-DM) • Bleeding • Data mining • Prediction |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
URI: | https://discovery.ucl.ac.uk/id/eprint/10170175 |
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