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Mechanical Power Ratio and Respiratory Treatment Escalation in COVID-19 Pneumonia: A Secondary Analysis of a Prospectively Enrolled Cohort. Anesthesiology 2023; 138:289-298. [PMID: 36571571 PMCID: PMC9904389 DOI: 10.1097/aln.0000000000004465] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND Under the hypothesis that mechanical power ratio could identify the spontaneously breathing patients with a higher risk of respiratory failure, this study assessed lung mechanics in nonintubated patients with COVID-19 pneumonia, aiming to (1) describe their characteristics; (2) compare lung mechanics between patients who received respiratory treatment escalation and those who did not; and (3) identify variables associated with the need for respiratory treatment escalation. METHODS Secondary analysis of prospectively enrolled cohort involving 111 consecutive spontaneously breathing adults receiving continuous positive airway pressure, enrolled from September 2020 to December 2021. Lung mechanics and other previously reported predictive indices were calculated, as well as a novel variable: the mechanical power ratio (the ratio between the actual and the expected baseline mechanical power). Patients were grouped according to the outcome: (1) no-treatment escalation (patient supported in continuous positive airway pressure until improvement) and (2) treatment escalation (escalation of the respiratory support to noninvasive or invasive mechanical ventilation), and the association between lung mechanics/predictive scores and outcome was assessed. RESULTS At day 1, patients undergoing treatment escalation had spontaneous tidal volume similar to those of patients who did not (7.1 ± 1.9 vs. 7.1 ± 1.4 ml/kgIBW; P = 0.990). In contrast, they showed higher respiratory rate (20 ± 5 vs. 18 ± 5 breaths/min; P = 0.028), minute ventilation (9.2 ± 3.0 vs. 7.9 ± 2.4 l/min; P = 0.011), tidal pleural pressure (8.1 ± 3.7 vs. 6.0 ± 3.1 cm H2O; P = 0.003), mechanical power ratio (2.4 ± 1.4 vs. 1.7 ± 1.5; P = 0.042), and lower partial pressure of alveolar oxygen/fractional inspired oxygen tension (174 ± 64 vs. 220 ± 95; P = 0.007). The mechanical power (area under the curve, 0.738; 95% CI, 0.636 to 0.839] P < 0.001), the mechanical power ratio (area under the curve, 0.734; 95% CI, 0.625 to 0.844; P < 0.001), and the pressure-rate index (area under the curve, 0.733; 95% CI, 0.631 to 0.835; P < 0.001) showed the highest areas under the curve. CONCLUSIONS In this COVID-19 cohort, tidal volume was similar in patients undergoing treatment escalation and in patients who did not; mechanical power, its ratio, and pressure-rate index were the variables presenting the highest association with the clinical outcome. EDITOR’S PERSPECTIVE
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Groenendaal W, Lee S, van Hoof C. Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions. JMIR BIOMEDICAL ENGINEERING 2021; 6:e22911. [PMID: 38907374 PMCID: PMC11041432 DOI: 10.2196/22911] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 03/01/2021] [Accepted: 04/06/2021] [Indexed: 01/20/2023] Open
Abstract
Currently, nearly 6 in 10 US adults are suffering from at least one chronic condition. Wearable technology could help in controlling the health care costs by remote monitoring and early detection of disease worsening. However, in recent years, there have been disappointments in wearable technology with respect to reliability, lack of feedback, or lack of user comfort. One of the promising sensor techniques for wearable monitoring of chronic disease is bioimpedance, which is a noninvasive, versatile sensing method that can be applied in different ways to extract a wide range of health care parameters. Due to the changes in impedance caused by either breathing or blood flow, time-varying signals such as respiration and cardiac output can be obtained with bioimpedance. A second application area is related to body composition and fluid status (eg, pulmonary congestion monitoring in patients with heart failure). Finally, bioimpedance can be used for continuous and real-time imaging (eg, during mechanical ventilation). In this viewpoint, we evaluate the use of wearable bioimpedance monitoring for application in chronic conditions, focusing on the current status, recent improvements, and challenges that still need to be tackled.
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Affiliation(s)
| | - Seulki Lee
- Imec the Netherlands / Holst Centre, Eindhoven, Netherlands
| | - Chris van Hoof
- Imec, Leuven, Belgium
- One Planet Research Center, Wageningen, Netherlands
- Department of Engineering Science, KU Leuven, Leuven, Belgium
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Balleza-Ordaz M, Estrella-Cerón R, Romero-Muñiz T, Vargas-Luna M. Lung ventilation monitoring by electrical bioimpedance technique using three different 4-electrode thoracic configurations: Variability of calibration equations. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Dumond R, Gastinger S, Rahman HA, Le Faucheur A, Quinton P, Kang H, Prioux J. Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning. Eur J Appl Physiol 2017; 117:1533-1555. [PMID: 28612121 DOI: 10.1007/s00421-017-3630-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 05/01/2017] [Indexed: 11/25/2022]
Abstract
PURPOSE The purposes of this study were to both improve the accuracy of respiratory volume (V) estimates using the respiratory magnetometer plethysmography (RMP) technique and facilitate the use of this technique. METHOD We compared two models of machine learning (ML) for estimating [Formula: see text]: a linear model (multiple linear regression-MLR) and a nonlinear model (artificial neural network-ANN), and we used cross-validation to validate these models. Fourteen healthy adults, aged [Formula: see text] years participated in the present study. The protocol was conducted in a laboratory test room. The anteroposterior displacements of the rib cage and abdomen, and the axial displacements of the chest wall and spine were measured using two pairs of magnetometers. [Formula: see text] was estimated from these four signals, and the respiratory volume was simultaneously measured using a spirometer ([Formula: see text]) under lying, sitting and standing conditions as well as various exercise conditions (working on computer, treadmill walking at 4 and 6 km[Formula: see text], treadmill running at 9 and 12 km [Formula: see text] and ergometer cycling at 90 and 110 W). RESULTS The results from the ANN model fitted the spirometer volume significantly better than those obtained through MLR. Considering all activities, the difference between [Formula: see text] and [Formula: see text] (bias) was higher for the MLR model ([Formula: see text] L) than for the ANN model ([Formula: see text] L). CONCLUSION Our results demonstrate that this new processing approach for RMP seems to be a valid tool for estimating V with sufficient accuracy during lying, sitting and standing and under various exercise conditions.
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Affiliation(s)
- Rémy Dumond
- Laboratoire Mouvement, Sport, Santé (EA 1274), Université de Rennes 2, Avenue Robert Schuman, 35170, Bruz, France.
- Département Sciences du sport et éducation physique, Ecole normale supérieure de Rennes, Campus de Ker Lann, Avenue Robert Schuman, 35170, Bruz, France.
| | - Steven Gastinger
- Laboratoire Mouvement, Sport, Santé (EA 1274), Université de Rennes 2, Avenue Robert Schuman, 35170, Bruz, France
- APCoSS, Institut de Formation en Éducation Physique et en Sport d'Angers (IFEPSA), Les Ponts de Cé, France
| | - Hala Abdul Rahman
- Laboratoire Mouvement, Sport, Santé (EA 1274), Université de Rennes 2, Avenue Robert Schuman, 35170, Bruz, France
- Laboratoire du Traitement du Signal et de l'Image, Université de Rennes 1, Campus de Beaulieu, Bâtiment 22, Rennes, 35042 Cedex, France
| | - Alexis Le Faucheur
- Laboratoire Mouvement, Sport, Santé (EA 1274), Université de Rennes 2, Avenue Robert Schuman, 35170, Bruz, France
- Département Sciences du sport et éducation physique, Ecole normale supérieure de Rennes, Campus de Ker Lann, Avenue Robert Schuman, 35170, Bruz, France
| | - Patrice Quinton
- Laboratoire Mouvement, Sport, Santé (EA 1274), Université de Rennes 2, Avenue Robert Schuman, 35170, Bruz, France
- Departement Informatique et télécommunications, Ecole normale supérieure de Rennes, Campus de Ker Lann, Avenue Robert Schuman, 35170, Bruz, France
| | - Haitao Kang
- Yuewu Electronic Technology Co., Ltd, Room 1008, Building B, No. 2305, Zuchongzhi Road, Shanghai, 201203, China
| | - Jacques Prioux
- Laboratoire Mouvement, Sport, Santé (EA 1274), Université de Rennes 2, Avenue Robert Schuman, 35170, Bruz, France.
- Département Sciences du sport et éducation physique, Ecole normale supérieure de Rennes, Campus de Ker Lann, Avenue Robert Schuman, 35170, Bruz, France.
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