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Assessment of neonatal respiratory rate variability.
Coleman, Jesse; Ginsburg, Amy Sarah; Macharia, William M; Ochieng, Roseline; Chomba, Dorothy; Zhou, Guohai; Dunsmuir, Dustin; Karlen, Walter; Ansermino, J Mark.
Afiliação
  • Coleman J; Evaluation of Technologies for Neonates in Africa (ETNA), Nairobi, Kenya. denots@gmail.com.
  • Ginsburg AS; Centre for International Child Health, 305 - 4088 Cambie Street, Vancouver, BC, V5Z 2X8, Canada. denots@gmail.com.
  • Macharia WM; Clinical Trials Unit, University of Washington, Seattle, WA, USA.
  • Ochieng R; Department of Pediatrics, Aga Khan University, Nairobi, Kenya.
  • Chomba D; Department of Pediatrics, Aga Khan University, Nairobi, Kenya.
  • Zhou G; Department of Pediatrics, Aga Khan University, Nairobi, Kenya.
  • Dunsmuir D; Center for Clinical Investigation, Brigham and Women's Hospital, Boston, MA, USA.
  • Karlen W; Anesthesiology, Pharmacology & Therapeutics, The University of British Columbia, Vancouver, BC, Canada.
  • Ansermino JM; Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
J Clin Monit Comput ; 36(6): 1869-1879, 2022 12.
Article em En | MEDLINE | ID: mdl-35332406
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Capnografia / Taxa Respiratória Tipo de estudo: Guideline Limite: Humans / Newborn País/Região como assunto: Africa Idioma: En Revista: J Clin Monit Comput Assunto da revista: INFORMATICA MEDICA / MEDICINA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Quênia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Capnografia / Taxa Respiratória Tipo de estudo: Guideline Limite: Humans / Newborn País/Região como assunto: Africa Idioma: En Revista: J Clin Monit Comput Assunto da revista: INFORMATICA MEDICA / MEDICINA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Quênia