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Nair PJ, Pfaller MR, Dual SA, McElhinney DB, Ennis DB, Marsden AL. Non-invasive Estimation of Pressure Drop Across Aortic Coarctations: Validation of 0D and 3D Computational Models with In Vivo Measurements. Ann Biomed Eng 2024; 52:1335-1346. [PMID: 38341399 DOI: 10.1007/s10439-024-03457-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
Blood pressure gradient ( Δ P ) across an aortic coarctation (CoA) is an important measurement to diagnose CoA severity and gauge treatment efficacy. Invasive cardiac catheterization is currently the gold-standard method for measuring blood pressure. The objective of this study was to evaluate the accuracy of Δ P estimates derived non-invasively using patient-specific 0D and 3D deformable wall simulations. Medical imaging and routine clinical measurements were used to create patient-specific models of patients with CoA (N = 17). 0D simulations were performed first and used to tune boundary conditions and initialize 3D simulations. Δ P across the CoA estimated using both 0D and 3D simulations were compared to invasive catheter-based pressure measurements for validation. The 0D simulations were extremely efficient ( ∼ 15 s computation time) compared to 3D simulations ( ∼ 30 h computation time on a cluster). However, the 0D Δ P estimates, unsurprisingly, had larger mean errors when compared to catheterization than 3D estimates (12.1 ± 9.9 mmHg vs 5.3 ± 5.4 mmHg). In particular, the 0D model performance degraded in cases where the CoA was adjacent to a bifurcation. The 0D model classified patients with severe CoA requiring intervention (defined as Δ P ≥ 20 mmHg) with 76% accuracy and 3D simulations improved this to 88%. Overall, a combined approach, using 0D models to efficiently tune and launch 3D models, offers the best combination of speed and accuracy for non-invasive classification of CoA severity.
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Affiliation(s)
- Priya J Nair
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Martin R Pfaller
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Seraina A Dual
- Department of Biomedical Signaling and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Doff B McElhinney
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA
| | - Daniel B Ennis
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
- Division of Radiology, VA Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Alison L Marsden
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA.
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA.
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
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Nair PJ, Pfaller MR, Dual SA, McElhinney DB, Ennis DB, Marsden AL. Non-invasive estimation of pressure drop across aortic coarctations: validation of 0D and 3D computational models with in vivo measurements. medRxiv 2023:2023.09.05.23295066. [PMID: 37732242 PMCID: PMC10508787 DOI: 10.1101/2023.09.05.23295066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Purpose Blood pressure gradient (Δ P ) across an aortic coarctation (CoA) is an important measurement to diagnose CoA severity and gauge treatment efficacy. Invasive cardiac catheterization is currently the gold-standard method for measuring blood pressure. The objective of this study was to evaluate the accuracy of Δ P estimates derived non-invasively using patient-specific 0 D and 3 D deformable wall simulations. Methods Medical imaging and routine clinical measurements were used to create patient-specific models of patients with CoA (N = 17 ). 0 D simulations were performed first and used to tune boundary conditions and initialize 3 D simulations. Δ P across the CoA estimated using both 0 D and 3 D simulations were compared to invasive catheter-based pressure measurements for validation. Results The 0 D simulations were extremely efficient (~15 secs computation time) compared to 3 D simulations (~30 hrs computation time on a cluster). However, the 0 D Δ P estimates, unsurprisingly, had larger mean errors when compared to catheterization than 3 D estimates (12.1 ± 9.9 mmHg vs 5.3 ± 5.4 mmHg). In particular, the 0 D model performance degraded in cases where the CoA was adjacent to a bifurcation. The 0 D model classified patients with severe CoA requiring intervention (defined as Δ P ≥ 20 mmHg) with 76% accuracy and 3 D simulations improved this to 88%. Conclusion Overall, a combined approach, using 0 D models to efficiently tune and launch 3 D models, offers the best combination of speed and accuracy for non-invasive classification of CoA severity.
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Affiliation(s)
- Priya J. Nair
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Martin R. Pfaller
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Seraina A. Dual
- Department of Biomedical Signaling and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Doff B. McElhinney
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA
| | - Daniel B. Ennis
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
- Division of Radiology, VA Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Alison L. Marsden
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Pediatrics - Cardiology, Stanford University, Stanford, CA, USA
- Maternal and Child Health Research Institute, Stanford University, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
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Nair PJ, Chakraborty A, Varikoden H, Francis PA, Kuttippurath J. The local and global climate forcings induced inhomogeneity of Indian rainfall. Sci Rep 2018; 8:6026. [PMID: 29662104 PMCID: PMC5902448 DOI: 10.1038/s41598-018-24021-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 03/26/2018] [Indexed: 11/21/2022] Open
Abstract
India is home for more than a billion people and its economy is largely based on agrarian society. Therefore, rainfall received not only decides its livelihood, but also influences its water security and economy. This situation warrants continuous surveillance and analysis of Indian rainfall. These kinds of studies would also help forecasters to better tune their models for accurate weather prediction. Here, we introduce a new method for estimating variability and trends in rainfall over different climate regions of India. The method based on multiple linear regression helps to assess contributions of different remote and local climate forcings to seasonal and regional inhomogeneity in rainfall. We show that the Indian Summer Monsoon Rainfall (ISMR) variability is governed by Eastern and Central Pacific El Niño Southern Oscillation, equatorial zonal winds, Atlantic zonal mode and surface temperatures of the Arabian Sea and Bay of Bengal, and the North East Monsoon Rainfall variability is controlled by the sea surface temperature of the North Atlantic and extratropial oceans. Also, our analyses reveal significant positive trends (0.43 mm/day/dec) in the North West for ISMR in the 1979–2017 period. This study cautions against the significant changes in Indian rainfall in a perspective of global climate change.
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Affiliation(s)
- P J Nair
- CORAL, Indian Institute of Technology Kharagpur, West Bengal, India.
| | - A Chakraborty
- CORAL, Indian Institute of Technology Kharagpur, West Bengal, India
| | - H Varikoden
- Indian Institute of Tropical Meteorology, Pashan, Pune, 411008, India
| | - P A Francis
- ESSO-Indian National Centre for Ocean Information Services, Hyderabad, India
| | - J Kuttippurath
- CORAL, Indian Institute of Technology Kharagpur, West Bengal, India
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