Models and Methods for Computational Cardiology: 2nd Edition

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Special Issue Editors


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Guest Editor
Biomedical Engineering Department, College of Engineering and Computing, University of South Carolina, Greenville, SC 29605, USA
Interests: cardiovascular development; extracellular matrix development; the interplay between genomic programing and the mechanical environment during development and disease
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Guest Editor
Department of Biomedical Engineering, School of Medicine, Oregon Health Sciences University, Portland, OR 97239, USA
Interests: cardiovascular development; hemodynamics; heart function; computational fluid dynamics (CFD); mechanotransduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Journal of Cardiovascular Development and Disease is planning to create a Special Issue that focuses on computational cardiology. In this Special Issue, we will highlight some of the recent developments advancing our understanding and diagnosis of cardiovascular disease. The enhancement of imaging modalities facilitated the resolution of disease initiation and progression, which until recently was only available in humans post mortem. Advances in cardiovascular computational modeling and cardiovascular informatics, together with these novel imaging modalities, engender new opportunities for earlier interventions and better outcomes in cardiovascular disease, among the most prevalent causes of human mortality. In this Special Issue, entitled “Models and Methods for Computational Cardiology”, we welcome you to contribute a research paper or review article addressing any aspect of this topic, including novel basic science or clinical approaches that better define the mechanisms of cardiovascular development and pathology. Novel models for pediatric and adult heart disease, including those that seek to improve the outcomes of surgical interventions, are also relevant topics for this Special Issue. This is an excellent opportunity for clinical and basic sciences trainees in your group to contribute to the field.

Prof. Dr. Richard L. Goodwin
Prof. Dr. Sandra Rugonyi
Guest Editors

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Keywords

  • cardiovascular development
  • congenital heart disease
  • cardiovascular imaging
  • patient-specific cardiovascular modeling
  • cardiovascular growth and remodeling
  • cardiovascular computational fluid dynamics
  • cardiovascular flow tissue interaction
  • hemodynamics
  • mechanotransduction

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Published Papers (1 paper)

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Research

13 pages, 1533 KiB  
Article
A New Risk Prediction Model for the Assessment of Myocardial Injury in Elderly Patients Undergoing Non-Elective Surgery
by Vedat Cicek, Mert Babaoglu, Faysal Saylik, Samet Yavuz, Ahmet Furkan Mazlum, Mahmut Salih Genc, Hatice Altinisik, Mustafa Oguz, Berke Cenktug Korucu, Mert Ilker Hayiroglu, Tufan Cinar and Ulas Bagci
J. Cardiovasc. Dev. Dis. 2025, 12(1), 6; https://doi.org/10.3390/jcdd12010006 - 26 Dec 2024
Viewed by 585
Abstract
Background: Currently, recommended pre-operative risk assessment models including the revised cardiac risk index (RCRI) are not very effective in predicting postoperative myocardial damage after non-elective surgery, especially for elderly patients. Aims: This study aimed to create a new risk prediction model to assess [...] Read more.
Background: Currently, recommended pre-operative risk assessment models including the revised cardiac risk index (RCRI) are not very effective in predicting postoperative myocardial damage after non-elective surgery, especially for elderly patients. Aims: This study aimed to create a new risk prediction model to assess myocardial injury after non-cardiac surgery (MINS) in elderly patients and compare it with the RCRI, a well-known pre-operative risk prediction model. Materials and Methods: This retrospective study included 370 elderly patients who were over 65 years of age and had non-elective surgery in a tertiary hospital. Each patient underwent detailed physical evaluations before the surgery. The study cohort was divided into two groups: patients who had MINS and those who did not. Results: In total, 13% (48 out of 370 patients) of the patients developed MINS. Multivariable analysis revealed that creatinine, lymphocyte, aortic regurgitation (moderate-severe), stroke, hemoglobin, ejection fraction, and D-dimer were independent determinants of MINS. By using these parameters, a model called “CLASHED” was developed to predict postoperative MINS. The ROC analysis comparison demonstrated that the new risk prediction model was significantly superior to the RCRI in predicting MINS in elderly patients undergoing non-elective surgery (AUC: 0.788 vs. AUC: 0.611, p < 0.05). Conclusions: Our study shows that the new risk preoperative model successfully predicts MINS in elderly patients undergoing non-elective surgery. In addition, this new model is found to be superior to the RCRI in predicting MINS. Full article
(This article belongs to the Special Issue Models and Methods for Computational Cardiology: 2nd Edition)
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