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Madike R, Muecke T, Dishnica N, Zhu L, Tan S, Kovoor J, Stretton B, Gupta A, Harroud A, Bersten A, Schultz D, Bacchi S. A vital parameter? Systematic review of spirometry in evaluation for intensive care unit admission and intubation and ventilation for Guillain-Barré syndrome. J Clin Neurosci 2023; 113:13-19. [PMID: 37146475 DOI: 10.1016/j.jocn.2023.04.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 04/09/2023] [Accepted: 04/28/2023] [Indexed: 05/07/2023]
Abstract
BACKGROUND Patients with Guillain-Barré syndrome (GBS) may require intensive care unit (ICU) admission for intubation and ventilation (I + V). The means to predict which patients will require I + V include spirometry measures. The aims of this study were to determine, for adult patients with GBS, how effectively different spirometry parameter thresholds predict the need for ICU admission and the requirement for I + V; and what effects these different parameter thresholds have on GBS patient outcomes. METHOD A systematic review was conducted of the databases PubMed, EMBASE, and Cochrane library in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The systematic review was registered prospectively on PROSPERO. RESULTS Initial searches returned 1011 results, of which 8 fulfilled inclusion criteria. All included studies were observational in nature. Multiple studies suggest that a vital capacity below 60% of predicted value on admission is associated with the need for eventual I + V. No included studies evaluated peak expiratory flow rate, or interventions with different thresholds for ICU or I + V. CONCLUSIONS There is a relationship between vital capacity and the need for I + V. However, there is limited evidence supporting specific thresholds for I + V. In addition to evaluating these factors, future research may evaluate the effect of different patient characteristics, including clinical presentation, weight, age, and respiratory comorbidities, on the effectiveness of spirometry parameters in the prediction of the need for I + V.
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Affiliation(s)
- Reema Madike
- University of Adelaide, Adelaide SA 5005, Australia; Health and Information, Adelaide SA 5000, Australia.
| | - Thomas Muecke
- University of Adelaide, Adelaide SA 5005, Australia; Health and Information, Adelaide SA 5000, Australia
| | - Noel Dishnica
- Health and Information, Adelaide SA 5000, Australia; Flinders University, Bedford Park SA 5042, Australia
| | - Linyi Zhu
- University of Adelaide, Adelaide SA 5005, Australia; Health and Information, Adelaide SA 5000, Australia
| | - Sheryn Tan
- University of Adelaide, Adelaide SA 5005, Australia; Health and Information, Adelaide SA 5000, Australia
| | - Joshua Kovoor
- University of Adelaide, Adelaide SA 5005, Australia; Health and Information, Adelaide SA 5000, Australia; Royal Adelaide Hospital, Adelaide SA 5000, Australia
| | - Brandon Stretton
- University of Adelaide, Adelaide SA 5005, Australia; Health and Information, Adelaide SA 5000, Australia; Royal Adelaide Hospital, Adelaide SA 5000, Australia
| | - Aashray Gupta
- University of Adelaide, Adelaide SA 5005, Australia; Health and Information, Adelaide SA 5000, Australia; Gold Coast University Hospital, Southport QLD 4215, Australia
| | - Adil Harroud
- McGill University, Montreal, Quebec H3A 0G4, Canada
| | | | - David Schultz
- Flinders University, Bedford Park SA 5042, Australia
| | - Stephen Bacchi
- University of Adelaide, Adelaide SA 5005, Australia; Health and Information, Adelaide SA 5000, Australia; Flinders University, Bedford Park SA 5042, Australia; Royal Adelaide Hospital, Adelaide SA 5000, Australia
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Zhou B, Baucells Costa A, Lukowicz P. Accurate Spirometry with Integrated Barometric Sensors in Face-Worn Garments. SENSORS 2020; 20:s20154234. [PMID: 32751385 PMCID: PMC7435382 DOI: 10.3390/s20154234] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 07/20/2020] [Accepted: 07/24/2020] [Indexed: 11/25/2022]
Abstract
Cardiorespiratory (CR) signals are crucial vital signs for fitness condition tracking, medical diagnosis, and athlete performance evaluation. Monitoring such signals in real-life settings is among the most widespread applications of wearable computing. We investigate how miniaturized barometers can be used to perform accurate spirometry in a wearable system that is built on off-the-shelf training masks often used by athletes as a training aid. We perform an evaluation where differential barometric pressure sensors are compared concurrently with a digital spirometer, during an experimental setting of clinical forced vital capacity (FVC) test procedures with 20 participants. The relationship between the two instruments is derived by mathematical modeling first, then by various regression methods from experiment data. The results show that the error of FVC vital values between the two instruments can be as low as 2∼3%. Beyond clinical tests, the method can also measure continuous tidal breathing air volumes with a 1∼3% error margin. Overall, we conclude that barometers with millimeter footprints embedded in face mask apparel can perform similarly to a digital spirometer to monitor breathing airflow and volume in pulmonary function tests.
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Affiliation(s)
- Bo Zhou
- Research Group Embedded Intelligence, German Research Center for Artificial Intelligence, 67663 Kaiserslautern, Germany; (A.B.C.); (P.L.)
- Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany
- Correspondence:
| | - Alejandro Baucells Costa
- Research Group Embedded Intelligence, German Research Center for Artificial Intelligence, 67663 Kaiserslautern, Germany; (A.B.C.); (P.L.)
- Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Paul Lukowicz
- Research Group Embedded Intelligence, German Research Center for Artificial Intelligence, 67663 Kaiserslautern, Germany; (A.B.C.); (P.L.)
- Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany
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