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An identifiable model of lung mechanics to diagnose and monitor COPD. Comput Biol Med 2023; 152:106430. [PMID: 36543001 DOI: 10.1016/j.compbiomed.2022.106430] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/23/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Current methods to diagnose and monitor COPD employ spirometry as the gold standard to identify lung function reduction with reduced forced expiratory volume (FEV1)/vital capacity (VC) ratio. Current methods utilise linear assumptions regarding airway resistance, where nonlinear resistance modelling may provide rapid insight into patient specific condition and disease progression. This study examines model-based expiratory resistance in healthy lungs and those with progressively more severe COPD. METHODS Healthy and COPD pressure (P)[cmH2O] and flow (Q)[L/s] data is obtained from the literature, and 5 intermediate levels of COPD and responses are created to simulate COPD progression and assess model-based metric resolution. Linear and nonlinear single compartment models are used to identify changes in inspiratory (R1,insp) and linear (R1,exp)/nonlinear (R2Φ) expiratory resistance with disease severity and over the course of expiration. RESULTS R1,insp increases from 2.1 to 7.3 cmH2O/L/s, R1,exp increases from 2.4 to 10.0 cmH2O/L/s with COPD severity. Nonlinear R2Φ increases (mean R2Φ: 2.5 cmH2O/L/s (healthy) to 24.4 cmH2O/L/s (COPD)), with increasing end-expiratory nonlinearity as COPD severity increases. CONCLUSION Expiratory resistance is increasingly highly nonlinear with COPD severity. These results show a simple, nonlinear model can capture fundamental COPD dynamics and progression from regular breathing data, and such an approach may be useful for patient-specific diagnosis and monitoring.
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Xun H, Shallal C, Unger J, Tao R, Torres A, Vladimirov M, Frye J, Singhala M, Horne B, Kim BS, Burke B, Montana M, Talcott M, Winters B, Frisella M, Kushner BS, Sacks JM, Guest JK, Kang SH, Caffrey J. Translational design for limited resource settings as demonstrated by Vent-Lock, a 3D-printed ventilator multiplexer. 3D Print Med 2022; 8:29. [PMID: 36102998 PMCID: PMC9471031 DOI: 10.1186/s41205-022-00148-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 06/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Mechanical ventilators are essential to patients who become critically ill with acute respiratory distress syndrome (ARDS), and shortages have been reported due to the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS We utilized 3D printing (3DP) technology to rapidly prototype and test critical components for a novel ventilator multiplexer system, Vent-Lock, to split one ventilator or anesthesia gas machine between two patients. FloRest, a novel 3DP flow restrictor, provides clinicians control of tidal volumes and positive end expiratory pressure (PEEP), using the 3DP manometer adaptor to monitor pressures. We tested the ventilator splitter circuit in simulation centers between artificial lungs and used an anesthesia gas machine to successfully ventilate two swine. RESULTS As one of the first studies to demonstrate splitting one anesthesia gas machine between two swine, we present proof-of-concept of a de novo, closed, multiplexing system, with flow restriction for potential individualized patient therapy. CONCLUSIONS While possible, due to the complexity, need for experienced operators, and associated risks, ventilator multiplexing should only be reserved for urgent situations with no other alternatives. Our report underscores the initial design and engineering considerations required for rapid medical device prototyping via 3D printing in limited resource environments, including considerations for design, material selection, production, and distribution. We note that optimization of engineering may minimize 3D printing production risks but may not address the inherent risks of the device or change its indications. Thus, our case report provides insights to inform future rapid prototyping of medical devices.
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Affiliation(s)
- Helen Xun
- Johns Hopkins School of Medicine, Baltimore, MD, 21231, USA
| | - Christopher Shallal
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21231, USA
| | - Justin Unger
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Runhan Tao
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21231, USA
| | - Alberto Torres
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Michael Vladimirov
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jenna Frye
- Maryland Institute College of Art, Baltimore, MD, 21217, USA
| | - Mohit Singhala
- Department of Mechanical Engineering and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Brockett Horne
- Maryland Institute College of Art, Baltimore, MD, 21217, USA
| | - Bo Soo Kim
- Johns Hopkins School of Medicine, Baltimore, MD, 21231, USA
| | - Broc Burke
- Washington University in St. Louis School of Medicine, St. Louis, MO, 63130, USA
| | - Michael Montana
- Washington University in St. Louis School of Medicine, St. Louis, MO, 63130, USA
| | - Michael Talcott
- Washington University in St. Louis School of Medicine, St. Louis, MO, 63130, USA
| | | | - Margaret Frisella
- Washington University in St. Louis School of Medicine, St. Louis, MO, 63130, USA
| | - Bradley S Kushner
- Washington University in St. Louis School of Medicine, St. Louis, MO, 63130, USA
| | - Justin M Sacks
- Washington University in St. Louis School of Medicine, St. Louis, MO, 63130, USA
| | - James K Guest
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Sung Hoon Kang
- Department of Mechanical Engineering and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Julie Caffrey
- Johns Hopkins School of Medicine, Baltimore, MD, 21231, USA.
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