Quantifying neonatal patient effort using non-invasive model-based methods.


Journal

Medical & biological engineering & computing
ISSN: 1741-0444
Titre abrégé: Med Biol Eng Comput
Pays: United States
ID NLM: 7704869

Informations de publication

Date de publication:
Mar 2022
Historique:
received: 08 04 2021
accepted: 15 12 2021
pubmed: 20 1 2022
medline: 22 2 2022
entrez: 19 1 2022
Statut: ppublish

Résumé

Patient-specific spontaneous breathing effort (SB) is common in invasively mechanically ventilated (MV) adult patients, and especially common in preterm neonates who are not typically sedated. However, there is no proven, ethically feasible and non-invasive method to quantify SB effort in neonates, creating the potential for model-based measures. Lung mechanics and SB effort are segregated using a basis function model to identify passive lung mechanics, and an additional time-varying elastance model to identify patient-specific SB effort and asynchrony as negative and positive added elastances, respectively. Data from ten preterm neonates on standard MV care in the neonatal intensive care unit (NICU) are used to assess this model-based approach, using area under the curve (AUC) for positive (asynchrony) and negative (SB effort) time-varying elastance. Median [interquartile-range (IQR)] of passive pulmonary lung elastance was 3.82 [2.09-5.80] cmH

Identifiants

pubmed: 35043368
doi: 10.1007/s11517-021-02491-y
pii: 10.1007/s11517-021-02491-y
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

739-751

Informations de copyright

© 2021. International Federation for Medical and Biological Engineering.

Références

Hendriks G, Stephenson R, Yajamanyam PK (2018) Current practice in early management of neonatal respiratory distress syndrome: Is it evidence-based? Arch Dis Child Fetal Neonatal Ed 103(2):F190–F191
Sweet DG et al (2010) European consensus guidelines on the management of neonatal respiratory distress syndrome in preterm infants - 2010 update. Neonatology 97(4):402–417
pubmed: 20551710
Liggins GC, Howie RN, Liggins GC, Howie RN (1972) A controlled trial of antepartum glucocorticoid treatment for prevention of the respiratory distress syndrome in premature infants. Pediatrics 50(4):515–525
pubmed: 4561295
Wood AJJ, Jobe AH (1993) Pulmonary Surfactant Therapy. N Engl J Med 328(12):861–868
Sweet D et al (2007) European consensus guidelines on the management of neonatal respiratory distress syndrome. J Perinat Med 35(3):175–186
pubmed: 17480144
Lozano SM, Newnam KM (2016) Modalities of mechanical ventilation: Volume-targeted versus pressure-limited. Adv Neonatal Care 16(2):99–107
pubmed: 26954584
Jobe AH, Bancalari E (2001) Bronchopulmonary Dysplasia. Am J Respir Crit Care Med 163(7):1723–1729
pubmed: 11401896
Kair LR, Leonard DT, Anderson JDM (2012) Bronchopulmonary dysplasia. Pediatr Rev 33(6):255–263
pubmed: 22659256
Chiew YS et al (2015) Time-varying respiratory system elastance: A physiological model for patients who are spontaneously breathing. PLoS ONE 10(1):1–13
Karbing DS et al (2012) Retrospective evaluation of a decision support system for controlled mechanical ventilation. Med Biol Eng Comput 50(1):43–51
pubmed: 22105216
Karbing DS, Kjærgaard S, Andreassen S, Espersen K, Rees SE (2011) Minimal model quantification of pulmonary gas exchange in intensive care patients. Med Eng Phys 33(2):240–248
pubmed: 21050794
Morton SE et al (2019) Predictive Virtual Patient Modelling of Mechanical Ventilation: Impact of Recruitment Function. Ann Biomed Eng 47(7):1626–1641
pubmed: 30927170
Kjaergaard S et al (2003) Non-invasive estimation of shunt and ventilation-perfusion mismatch. Intensive Care Med 29(5):727–734
pubmed: 12698242
Chiew YSW et al (2018) Assessing mechanical ventilation asynchrony through iterative airway pressure reconstruction. Comput Methods Programs Biomed 157:217–224
pubmed: 29477430
Kim KT, Knopp J, Dixon B, Chase G (2019) Quantifying neonatal pulmonary mechanics in mechanical ventilation. Biomed Signal Process Control 52:206–217
Chiew YS, Chase JG, Shaw GM, Sundaresan A, Desaive T (2011) Model-based PEEP optimisation in mechanical ventilation. Biomed Eng Online 10(1):111
pubmed: 22196749 pmcid: 3339371
J. H. T. Bates, 2009 “Pulmonary mechanics: A system identification perspective,” Proc. 31st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. Eng. Futur. Biomed. EMBC 2009, pp. 170–172
Szlavecz A et al (2014) “The Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management.,.” Biomed. Eng. Online 13(1):140
pubmed: 25270094 pmcid: 4192763
Chase JG et al (2018) Next-generation, personalised, model-based critical care medicine: A state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed Eng Online 17(1):1–29
Tawhai MH, Clark AR, Chase JG (2019) The Lung Physiome and virtual patient models: From morphometry to clinical translation. Morphologie 103(343):131–138
pubmed: 31570307
Redmond DP, Chiew YS, Major V, Chase JG (2019) Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort. Comput Methods Programs Biomed 171:67–79
pubmed: 27697371
K. T. Kim, J. Knopp, B. Dixon, and J. G. Chase, “Mechanically ventilated premature babies have sex differences in specific elastance: A pilot study,” Pediatr. Pulmonol., no. July, p. ppul.24538, Oct. 2019.
Jarreau PH et al (1999) Estimation of inspiratory pressure drop in neonatal and pediatric endotracheal tubes. J Appl Physiol 87(1):36–46
pubmed: 10409556
Kim KT, Redmond DP, Morton SE, Howe SL, Chiew YS, Chase JG (2017) Quantifying patient effort in spontaneously breathing patient using negative component of dynamic Elastance. IFAC-PapersOnLine 50(1):5486–5491
Morton SE et al (2019) Optimising mechanical ventilation through model-based methods and automation. Annu Rev Control 48:369–382
Morton SE et al (2018) A virtual patient model for mechanical ventilation. Comput Methods Programs Biomed 165:77–87
pubmed: 30337083
Langdon R, Docherty PD, Chiew YS, Chase JG (2017) Extrapolation of a non-linear autoregressive model of pulmonary mechanics. Math Biosci 284:32–39
pubmed: 27513728
Brown MK, DiBlasi RM (2011) Mechanical Ventilation of the Premature Neonate. Respir Care 56(9):1298–1313
pubmed: 21944682
Van Drunen EJ et al (2014) Visualisation of time-varying respiratory system elastance in experimental ARDS animal models. BMC Pulm Med 14(1):1–9
Kannangara DO et al (2016) Estimating the true respiratory mechanics during asynchronous pressure controlled ventilation. Biomed Signal Process Control 30:70–78
Knopp JL, Chase JG, Kim KT, Shaw GM (2021) “Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation.” Comput. Methods Programs Biomed 208:106300
pubmed: 34348200
Sundaresan A, Chase JG, Shaw GM, Chiew YS, Desaive T (2011) “Model-based optimal PEEP in mechanically ventilated ARDS patients in the intensive care unit.,.” Biomed. Eng. Online 10(1):64
pubmed: 21794116 pmcid: 3167768
C. Schranz, T. Becher, D. Schädler, N. Weiler, and K. Möller, 2013 “Model-Based Ventilator Settings in Pressure Controlled Ventilation,” vol. c, no. 6, pp. 10–11
Greenspan J, Abbasi S, Bhutani V (1988) Sequential changes in pulmonary mechanics in the very low birth weight (≤1000 grams) infant. J Pediatr 113(4):732–737
pubmed: 3171798
Sundaresan A, Chase JG (2012) Positive end expiratory pressure in patients with acute respiratory distress syndrome - The past, present and future. Biomed Signal Process Control 7(2):93–103
S. E. Rees and D. S. Karbing, “Determining the appropriate model complexity for patient-specific advice on mechanical ventilation,” Biomed. Eng. / Biomed. Tech., vol. 62, no. 2, Jan. 2017.
Howe SL et al (2020) “Inspiratory respiratory mechanics estimation by using expiratory data for reverse-triggered breathing cycles,.” Comput. Methods Programs Biomed 186:105184
pubmed: 31715280
Sweet DG et al (2013) European consensus guidelines on the management of neonatal respiratory distress syndrome in preterm infants-2013 update. Neonatology 103(4):353–368
pubmed: 23736015
Lozano-Zahonero S et al (2011) Automated mechanical ventilation: Adapting decision making to different disease states. Med Biol Eng Comput 49(3):349–358
pubmed: 21069471
Karbing DS et al (2007) Variation in the PaO2/FiO2 ratio with FiO2: mathematical and experimental description, and clinical relevance. Crit Care 11(6):R118
pubmed: 17988390 pmcid: 2246207
Garofalo E et al (2018) Recognizing, quantifying and managing patient-ventilator asynchrony in invasive and noninvasive ventilation. Expert Rev Respir Med 12(7):557–567
pubmed: 29792537
D. C. Chao, D. J. Scheinhorn, M. A. Hassenpflug, and M. S. Barlow, “Patient-ventilator trigger asynchrony in prolonged mechanical ventilation,” Chest, vol. 110, no. 4 SUPPL., 1996.
Torday JS, Nielsen HC (1987) The sex difference in fetal lung surfactant production. Exp Lung Res 12(1):1–19
pubmed: 3545796
Peacock JL, Marston L, Marlow N, Calvert SA, Greenough A (2012) Neonatal and infant outcome in boys and girls born very prematurely. Pediatr Res 71(3):305–310
pubmed: 22258087
Chiew YSW et al (2015) Feasibility of titrating PEEP to minimum elastance for mechanically ventilated patients. Pilot Feasibility Stud 1:1–10
Chiew YS et al (2012) Physiological relevance and performance of a minimal lung model – an experimental study in healthy and acute respiratory distress syndrome model piglets. BMC Pulm Med 12(1):1
Beck J et al (2009) Patient-ventilator interaction during neurally adjusted ventilatory assist in low birth weight infants. Pediatr Res 65(6):663–668
pubmed: 19218884 pmcid: 2762820
Longhini F et al (2015) Neurally adjusted ventilatory assist in preterm neonates with acute respiratory failure. Neonatology 107(1):60–67
pubmed: 25401284
Langdon R, Docherty PD, Chiew YS, Möller K, Chase JG (2016) Use of basis functions within a non-linear autoregressive model of pulmonary mechanics. Biomed Signal Process Control 27:44–50
Spinelli E, Mauri T, Beitler JR, Pesenti A, Brodie D (2020) Respiratory drive in the acute respiratory distress syndrome: pathophysiology, monitoring, and therapeutic interventions. Intensive Care Med 46(4):606–618
pubmed: 32016537 pmcid: 7224136
Kim KT, Knopp J, Chase JG (2021) “Quantifying patient spontaneous breathing effort using model-based methods,.” Biomed. Signal Process. Control 69:102809
Sonia Rodriguez Rivero AMR (2014) “Neurally Adjusted Vetilatory Assist in the Newborn.” J. Neonatal Biol 03(02):2–5
Beck J, Tucci M, Emeriaud G, Lacroix J, Sinderby C (2004) Prolonged Neural Expiratory Time Induced by Mechanical Ventilation in Infants. Pediatr Res 55(5):747–754
pubmed: 14739354

Auteurs

Kyeong Tae Kim (KT)

Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand. kyeongtaekim92@gmail.com.

Jennifer Knopp (J)

Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand.

Bronwyn Dixon (B)

Neonatal Intensive Care Unit, Christchurch Women's Hospital, Christchurch, New Zealand.

J Geoffrey Chase (JG)

Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand.

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