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Coleman J, Ginsburg AS, Macharia WM, Ochieng R, Chomba D, Zhou G, Dunsmuir D, Karlen W, Ansermino JM. Assessment of neonatal respiratory rate variability. J Clin Monit Comput 2022; 36:1869-1879. [PMID: 35332406 PMCID: PMC9637627 DOI: 10.1007/s10877-022-00840-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 03/02/2022] [Indexed: 11/30/2022]
Abstract
Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could inform and guide neonatal care. We sought to quantify neonatal RRV during a clinical study in which we compared multiparameter continuous physiological monitoring (MCPM) devices. Measurements of capnography-recorded exhaled carbon dioxide across 60-s epochs were collected from neonates admitted to the neonatal unit at Aga Khan University-Nairobi hospital. Breaths were manually counted from capnograms and using an automated signal detection algorithm which also calculated mean and median RR for each epoch. Outcome measures were between- and within-neonate RRV, between- and within-epoch RRV, and 95% limits of agreement, bias, and root-mean-square deviation. Twenty-seven neonates were included, with 130 epochs analysed. Mean manual breath count (MBC) was 48 breaths per minute. Median RRV ranged from 11.5% (interquartile range (IQR) 6.8-18.9%) to 28.1% (IQR 23.5-36.7%). Bias and limits of agreement for MBC vs algorithm-derived breath count, MBC vs algorithm-derived median breath rate, MBC vs algorithm-derived mean breath rate were - 0.5 (- 2.7, 1.66), - 3.16 (- 12.12, 5.8), and - 3.99 (- 11.3, 3.32), respectively. The marked RRV highlights the challenge of performing accurate RR measurements in neonates. More research is required to optimize the use of RRV to improve care. When evaluating MCPM devices, accuracy thresholds should be less stringent in newborns due to increased RRV. Lastly, median RR, which discounts the impact of extreme outliers, may be more reflective of the underlying physiological control of breathing.
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Affiliation(s)
- Jesse Coleman
- Evaluation of Technologies for Neonates in Africa (ETNA), Nairobi, Kenya.
- Centre for International Child Health, 305 - 4088 Cambie Street, Vancouver, BC, V5Z 2X8, Canada.
| | | | | | | | - Dorothy Chomba
- Department of Pediatrics, Aga Khan University, Nairobi, Kenya
| | - Guohai Zhou
- Center for Clinical Investigation, Brigham and Women's Hospital, Boston, MA, USA
| | - Dustin Dunsmuir
- Anesthesiology, Pharmacology & Therapeutics, The University of British Columbia, Vancouver, BC, Canada
| | - Walter Karlen
- Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - J Mark Ansermino
- Anesthesiology, Pharmacology & Therapeutics, The University of British Columbia, Vancouver, BC, Canada
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Gozal D, Ismail M, Brockmann PE. Alternatives to surgery in children with mild OSA. World J Otorhinolaryngol Head Neck Surg 2021; 7:228-235. [PMID: 34430830 PMCID: PMC8356096 DOI: 10.1016/j.wjorl.2021.03.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 02/17/2021] [Accepted: 03/18/2021] [Indexed: 12/01/2022] Open
Abstract
Precision medicine requires coordinated and integrated evidence-based combinatorial approaches so that diagnosis and treatment can be tailored to the individual patient. In this context, the treatment approach to mild obstructive sleep apnea (OSA) is fraught with substantial debate as to what is mild OSA, and as to what constitutes appropriate treatment. As such, it is necessary to first establish a proposed consensus of what criteria need to be employed to reach the diagnosis of mild OSA, and then examine the circumstances under which treatment is indicated, and if so, whether and when anti-inflammatory therapy (AIT), rapid maxillary expansion (RME), and/or myofunctional therapy (MFT) may be indicated.
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Affiliation(s)
- David Gozal
- Department of Child Health and Child Health Research Institute, and MU Women and Children's Hospital, University of Missouri School of Medicine, Columbia, MO, USA
| | - Mahmoud Ismail
- Department of Neurology and Sleep Medicine, University of Missouri School of Medicine, Columbia, MO, USA
| | - Pablo E Brockmann
- Department of Pediatric Cardiology and Pulmonology, Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Pediatric Sleep Center, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
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Barroso-García V, Gutiérrez-Tobal GC, Gozal D, Vaquerizo-Villar F, Álvarez D, del Campo F, Kheirandish-Gozal L, Hornero R. Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children. SENSORS 2021; 21:s21041491. [PMID: 33669996 PMCID: PMC7926995 DOI: 10.3390/s21041491] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/16/2021] [Accepted: 02/18/2021] [Indexed: 01/08/2023]
Abstract
This study focused on the automatic analysis of the airflow signal (AF) to aid in the diagnosis of pediatric obstructive sleep apnea (OSA). Thus, our aims were: (i) to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, (ii) to evaluate its diagnostic utility, and (iii) to assess its complementarity with the 3% oxygen desaturation index (ODI3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: (i) DWT-derived feature extraction, (ii) feature selection, and (iii) pattern recognition. AF recordings from OSA patients showed both lower detail coefficients and decreased activity associated with the normal breathing band. Wavelet analysis also revealed that OSA disturbed the frequency and energy distribution of the AF signal, increasing its irregularity. Moreover, the information obtained from the wavelet analysis was complementary to ODI3. In this regard, the combination of both wavelet information and ODI3 achieved high diagnostic accuracy using the common OSA-positive cutoffs: 77.97%, 81.91%, and 90.99% (AdaBoost.M2), and 81.96%, 82.14%, and 90.69% (Bayesian multi-layer perceptron) for 1, 5, and 10 apneic events/hour, respectively. Hence, these findings suggest that DWT properly characterizes OSA-related severity as embedded in nocturnal AF, and could simplify the diagnosis of pediatric OSA.
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Affiliation(s)
- Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| | - Gonzalo C. Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
- Correspondence: ; Tel.: +34-983-423000 (ext. 4713)
| | - David Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO 65212, USA; (D.G.); (L.K.-G.)
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| | - Daniel Álvarez
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
- Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain
| | - Félix del Campo
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
- Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain
| | - Leila Kheirandish-Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO 65212, USA; (D.G.); (L.K.-G.)
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
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