What is the clinical evidence from this study?

Study design: Cohort. Population: Comatose patients after cardiac arrest (n=79). Intervention: Heart rate complexity (Multiscale entropy, CIl). Primary outcome: Prognostic value of CIl for patient outcomes (best cerebral performance category) within 3 months (AUC 0.85, 95% CI 0.75-0.94, p=<0.0001).

December 10, 2021Open Access

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

Q: What are the key findings of this study?

The complexity index in long time scales (CIl) of heart rate variability from a 15-minute ECG during the first day of coma accurately predicted 3-month outcomes with an AUC of 0.85.

Key Result

The complexity index in long time scales (CIl) of heart rate variability from a 15-minute ECG during the first day of coma accurately predicted 3-month outcomes with an AUC of 0.85.

Study Design

Type

Cohort (n=79)

Multicenter

Yes

Structured PICO

Does heart-rate variability captured by multiscale entropy predict long-term neurological outcomes in comatose patients during the first 24 hours after cardiac arrest?

Population

79 comatose patients after cardiac arrest (CA) undergoing targeted temperature management and sedation during the first day of coma (21 female, age 68 ± 9; 58 male, age 60 ± 15), from a prospectively acquired multicentric register.

Intervention

Heart-rate variability analysis using multiscale entropy (MSE) derived from 15 minutes of electrocardiographic activity to calculate complexity indices in short (CIs) and long (CIl) time scales.

Outcome

Best Cerebral Performance Category (CPC) within 3 months (favorable outcome defined as CPC 1 or 2; unfavorable as CPC 3, 4, or 5).hard clinical

Analysis of heart rate variability using multiscale entropy from a 15-minute ECG on the first day of coma provides an accurate, non-invasive early prediction of neurological outcomes in cardiac arrest survivors.

Main Result

Effect estimate: AUC 0.85 (95% CI 0.75-0.94)

p-value: p=<0.0001

Limitations

Retrospective analysis of prospectively recorded datasets
Bias toward favorable outcome due to exclusion of patients who had premature withdrawal of life support
Unknown role and best weights of HRV indices in early clinical setting
Dependence on an artificial pacemaker in some cardiac arrest patients may render HRV numbers useless

Abstract

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. 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 CIl. 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%. Characterizing the complexity of the ECG in patients after CA provides an accurate prediction of both favorable and unfavorable outcomes. The analysis of heartrate variability by means of MSE provides accurate outcome prediction on the first day of coma.

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