Early heart rate variability evaluation enables to predict ICU patients' outcome.
Sci Rep
; 12(1): 2498, 2022 02 15.
Article
em En
| MEDLINE
| ID: mdl-35169170
ABSTRACT
Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has been proposed in various types of patients. We attempted to evaluate the best determinants of such variation in survival prediction using a physiological data-warehousing program. Plethysmogram tracings (PPG) were recorded at 75 Hz from the standard monitoring system, for a 2 h period, during the 24 h following ICU admission. Physiological data recording was associated with metadata collection. HRV was derived from PPG in either the temporal and non-linear domains. 540 consecutive patients were recorded. A lower LF/HF, SD2/SD1 ratios and Shannon entropy values on admission were associated with a higher ICU mortality. SpO2/FiO2 ratio and HRV parameters (LF/HF and Shannon entropy) were independent correlated with mortality in the multivariate analysis. Machine-learning using neural network (kNN) enabled to determine a simple decision tree combining the three best determinants (SDNN, Shannon Entropy, SD2/SD1 ratio) of a composite outcome index. HRV measured on admission enables to predict outcome in the ICU or at Day-28, independently of the admission diagnosis, treatment and mechanical ventilation requirement.Trial registration ClinicalTrials.gov identifier NCT02893462.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Admissão do Paciente
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Redes Neurais de Computação
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Mortalidade Hospitalar
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Aprendizado de Máquina
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Frequência Cardíaca
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Unidades de Terapia Intensiva
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Tempo de Internação
Tipo de estudo:
Observational_studies
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Prognostic_studies
/
Risk_factors_studies
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
França