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001-es BibID:BIBFORM122916
035-os BibID:(scopus)85195642584
Első szerző:Epstein, Richard H.
Cím:Area under the curve and amplitude of the compound motor action potential are clinically interchangeable quantitative measures of neuromuscular block : a method comparison study / Epstein Richard H., Nemes Réka, Renew Johnathan R., Brull Sorin J.
Dátum:2024
ISSN:2772-6096
Megjegyzések:Background: Current guidelines recommend quantitative neuromuscular block monitoring during neuromuscular blocking agent administration. Monitors using surface electromyography (EMG) determine compound motor action potential (cMAP) amplitude or area under the curve (AUC). Rigorous evaluation of the interchangeability of these methods is lacking but necessary for clinical and research assurance that EMG interpretations of the depth of neuromuscular block are not affected by the methodology. Methods: Digitised EMG waveforms were studied from 48 patients given rocuronium during two published studies. The EMG amplitudes and AUCs were calculated pairwise from all cMAPs classified as valid by visual inspection. Ratios of the first twitch (T1) to the control T1 before administration of rocuronium (T1c) and train-of-four ratios (TOFRs) were compared using repeated measures Bland?Altman analysis. Results: Among the 2419 paired T1/T1c differences where the average T1/T1c was ?0.2, eight (0.33%) were outside prespecified clinical limits of agreement (?0.148 to 0.164). Among the 1781 paired TOFR differences where the average TOFR was ?0.8, 70 (3.93%) were outside the prespecified clinical limits of agreement ((?0.109 to 0.134). Among all 7286 T1/T1c paired differences, the mean bias was 0.32 (95% confidence interval 0.202?0.043), and among all 5559 paired TOFR differences, the mean bias was 0.011 (95% confidence interval 0.0050?0.017). Among paired T1/T1c and TOFR differences, Lin's concordance correlation coefficients were 0.98 and 0.995, respectively. Repeatability coefficients for T1/T1c and TOFR were <0.08, with no differences between methods. Conclusions: Quantitative assessment neuromuscular block depth is clinically interchangeable when calculated using cMAP amplitude or the AUC. ? 2024 The Authors
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
action potentials
electromyography
monitoring
neuromuscular block
signal processing
Megjelenés:BJA Open. - 11 (2024), p. 1-10.-
További szerzők:Nemes Réka (1985-) (aneszteziológus, intenzív terápiás szakorvos) Renew, J. Ross Brull, Sorin J.
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2.

001-es BibID:BIBFORM121179
035-os BibID:(Scopus)85188214468
Első szerző:Epstein, Richard H.
Cím:Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation : an algorithm development experimental study - Reply to: Br J Anaesth Open 2024:100264 / Richard H. Epstein, Olivia F. Perez, Ira S. Hofer, J. Ross Renew, Reka Nemes, Sorin J. Brull
Dátum:2024
ISSN:2772-6096
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
electromyography
machine learning
neural network
neuromuscular block
train-of-four
Megjelenés:BJA Open. - 9 (2024), p. 1-8. -
További szerzők:Perez, Olivia F. Hofer, Ira S. Renew, J. Ross Nemes Réka (1985-) (aneszteziológus, intenzív terápiás szakorvos) Brull, Sorin J.
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DOI
Intézményi repozitóriumban (DEA) tárolt változat
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3.

001-es BibID:BIBFORM119600
035-os BibID:(Scopus)85186398760
Első szerző:Epstein, Richard H.
Cím:Corrigendum to "Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study" [BJA Open 8 (2023) 100236] / Richard H. Epstein, Olivia F. Perez, Ira S. Hofer, J. Ross Renew, Reka Nemes, Sorin J. Brull
Dátum:2024
ISSN:2772-6096
Megjegyzések:The authors regret errors in the above article regarding the use of the term ♭convolutional neural network'. The title should have read ♭Validation of a fully connected neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study.' Also, ♭fully connected neural network' should replace ♭convolutional neural network' and ♭FCNN' should replace ♭CNN' in the abstract and the body of the article, respectively. None of the results or conclusions are affected by these corrections. The authors apologise for any inconvenience caused.
Tárgyszavak:Orvostudományok Klinikai orvostudományok hozzászólás
folyóiratcikk
Megjelenés:BJA Open. - 9 (2024), p. 1. -
További szerzők:Perez, Olivia F. Hofer, Ira S. Renew, J. Ross Nemes Réka (1985-) (aneszteziológus, intenzív terápiás szakorvos) Brull, Sorin J.
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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4.

001-es BibID:BIBFORM117741
035-os BibID:(cikkazonosító)100236 (Scopus)85181231741
Első szerző:Epstein, Richard H.
Cím:Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation : an algorithm development experimental study / Richard H. Epstein, Olivia F. Perez, Ira S. Hofer, J. Ross Renew, Reka Nemes, Sorin J. Brull
Dátum:2023
ISSN:2772-6096
Megjegyzések:Background: International guidelines recommend quantitative neuromuscular monitoring when administering neuromuscular blocking agents. The train-of-four count is important for determining the depth of block and appropriate reversal agents and doses. However, identifying valid compound motor action potentials (cMAPs) during surgery can be challenging because of low-amplitude signals and an inability to observe motor responses. A convolutional neural network (CNN) to classify cMAPs as valid or not might improve the accuracy of such determinations. Methods: We modified a high-accuracy CNN originally developed to identify handwritten numbers. For training, we used digitised electromyograph waveforms (TetraGraph) from a previous study of 29 patients and tuned the model parameters using leave-one-out cross-validation. External validation used a dataset of 19 patients from another study with the same neuromuscular block monitor but with different patient, surgical, and protocol characteristics. All patients underwent ulnar nerve stimulation at the wrist and the surface electromyogram was recorded from the adductor pollicis muscle. Results: The tuned CNN performed highly on the validation dataset, with an accuracy of 0.9997 (99% confidence interval 0.9994?0.9999) and F1 score=0.9998. Performance was equally good for classifying the four individual responses in the train-of-four sequence. The calibration plot showed excellent agreement between the predicted probabilities and the actual prevalence of valid cMAPs. Ten-fold cross-validation using all data showed similar high performance. Conclusions: The CNN distinguished valid cMAPs from artifacts after ulnar nerve stimulation at the wrist with >99.5% accuracy. Incorporation of such a process within quantitative electromyographic neuromuscular block monitors is feasible.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
electromyography
machine learning
neural network
neuromuscular block
train-of-four
Megjelenés:BJA Open. - 8 (2023), p. 1-9. -
További szerzők:Perez, Olivia F. Hofer, Ira S. Renew, J. Ross Nemes Réka (1985-) (aneszteziológus, intenzív terápiás szakorvos) Brull, Sorin J.
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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