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Stratification of coronary artery disease patients for revascularization procedure based on estimating adverse effects

Overview of attention for article published in BMC Medical Informatics and Decision Making, February 2015
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Title
Stratification of coronary artery disease patients for revascularization procedure based on estimating adverse effects
Published in
BMC Medical Informatics and Decision Making, February 2015
DOI 10.1186/s12911-015-0131-0
Pubmed ID
Authors

Sebastian Pölsterl, Maneesh Singh, Amin Katouzian, Nassir Navab, Adnan Kastrati, Lance Ladic, Ali Kamen

Abstract

Percutaneous coronary intervention (PCI) is the most commonly performed treatment for coronary atherosclerosis. It is associated with a higher incidence of repeat revascularization procedures compared to coronary artery bypass grafting surgery. Recent results indicate that PCI is only cost-effective for a subset of patients. Estimating risks of treatment options would be an effort toward personalized treatment strategy for coronary atherosclerosis. In this paper, we propose to model clinical knowledge about the treatment of coronary atherosclerosis to identify patient-subgroup-specific classifiers to predict the risk of adverse events of different treatment options. We constructed one model for each patient subgroup to account for subgroup-specific interpretation and availability of features and hierarchically aggregated these models to cover the entire data. In addition, we deviated from the current clinical workflow only for patients with high probability of benefiting from an alternative treatment, as suggested by this model. Consequently, we devised a two-stage test with optimized negative and positive predictive values as the main indicators of performance. Our analysis was based on 2,377 patients that underwent PCI. Performance was compared with a conventional classification model and the existing clinical practice by estimating effectiveness, safety, and costs for different endpoints (6 month angiographic restenosis, 12 and 36 month hazardous events). Compared to the current clinical practice, the proposed method achieved an estimated reduction in adverse effects by 25.0% (95% CI, 17.8 to 30.2) for hazardous events at 36 months and 31.2% (95% CI, 25.4 to 39.0) for hazardous events at 12 months. Estimated total savings per patient amounted to $693 and $794 at 12 and 36 months, respectively. The proposed subgroup-specific method outperformed conventional population wide regression: The median area under the receiver operating characteristic curve increased from 0.57 to 0.61 for prediction of angiographic restenosis and from 0.76 to 0.85 for prediction of hazardous events. The results of this study demonstrated the efficacy of deployment of bare-metal stents and coronary artery bypass grafting surgery for subsets of patients. This is one effort towards development of personalized treatment strategies for patients with coronary atherosclerosis that could significantly impact associated treatment costs.

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The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 2 25%
Researcher 2 25%
Professor > Associate Professor 1 13%
Student > Bachelor 1 13%
Unknown 2 25%
Readers by discipline Count As %
Business, Management and Accounting 2 25%
Engineering 2 25%
Medicine and Dentistry 1 13%
Computer Science 1 13%
Unknown 2 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 March 2015.
All research outputs
#18,401,956
of 22,793,427 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,570
of 1,987 outputs
Outputs of similar age
#262,472
of 359,553 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#19
of 22 outputs
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