Heart rate complexity: an early prognostic marker of patient outcome after cardiac arrest

Riganello, Francesco; Zubler, Frédéric; Haenggi, Matthias; De Lucia, Marzia (2022). Heart rate complexity: an early prognostic marker of patient outcome after cardiac arrest. Clinical neurophysiology, 134, pp. 27-33. Elsevier 10.1016/j.clinph.2021.10.019

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Abstract

Objective: Early prognostication in comatose patients after cardiac arrest (CA) is difficult but essential to inform relatives and optimize treatment. Here we investigate the predictive value of heart-rate variability captured by multiscale entropy (MSE) for long-term outcomes in comatose patients during the first 24 hours after CA.

Methods: In this retrospective analysis of prospective multi-centric cohort, we analyzed MSE of the heart rate in 79 comatose patients after CA while undergoing targeted temperature management and sedation during the first day of coma. From the MSE, two complexity indices were derived by summing values over short and long time scales (CIs and CIl). We splitted the data in training and test
datasets for analysing the predictive value for patient outcomes (defined as best cerebral performance category within 3 months) of CIs and CIlo.

Results: Across the whole dataset, CIl provided the best sensitivity, specificity, and accuracy (88%, 75%, and 82%, respectively). Positive and negative predictive power were 81% and 84%.

Conclusions: Characterizing the complexity of the ECG in patients after CA provides an accurate prediction of both favorable and unfavorable outcomes.

Significance: The analysis of heartrate variability by means of MSE provides accurate outcome prediction on the first day of coma.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > Clinic of Intensive Care
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology

UniBE Contributor:

Zubler, Frédéric and Hänggi, Matthias

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1388-2457

Publisher:

Elsevier

Language:

English

Submitter:

Isabelle Arni

Date Deposited:

23 Dec 2021 15:45

Last Modified:

04 Jan 2022 07:15

Publisher DOI:

10.1016/j.clinph.2021.10.019

PubMed ID:

34953334

BORIS DOI:

10.48350/162280

URI:

https://boris.unibe.ch/id/eprint/162280

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