| Autor: |
Raymond J. van Wijk, Vincent M. Quinten, Mathilde C. van Rossum, Hjalmar R. Bouma, Jan C. ter Maaten |
| Přispěvatelé: |
Faculteit Medische Wetenschappen/UMCG, Critical care, Anesthesiology, Peri-operative and Emergency medicine (CAPE) |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 31:15. BMC |
| ISSN: |
1757-7241 |
| DOI: |
10.1186/s13049-023-01078-w |
| Popis: |
Background Sepsis is a life-threatening disease with an in-hospital mortality rate of approximately 20%. Physicians at the emergency department (ED) have to estimate the risk of deterioration in the coming hours or days and decide whether the patient should be admitted to the general ward, ICU or can be discharged. Current risk stratification tools are based on measurements of vital parameters at a single timepoint. Here, we performed a time, frequency, and trend analysis on continuous electrocardiograms (ECG) at the ED to try and predict deterioration of septic patients. Methods Patients were connected to a mobile bedside monitor that continuously recorded ECG waveforms from triage at the ED up to 48 h. Patients were post-hoc stratified into three groups depending on the development of organ dysfunction: no organ dysfunction, stable organ dysfunction or progressive organ dysfunction (i.e., deterioration). Patients with de novo organ dysfunction and those admitted to the ICU or died were also stratified to the group of progressive organ dysfunction. Heart rate variability (HRV) features over time were compared between the three groups. Results In total 171 unique ED visits with suspected sepsis were included between January 2017 and December 2018. HRV features were calculated over 5-min time windows and summarized into 3-h intervals for analysis. For each interval, the mean and slope of each feature was calculated. Of all analyzed features, the average of the NN-interval, ultra-low frequency, very low frequency, low frequency and total power were different between the groups at multiple points in time. Conclusions We showed that continuous ECG recordings can be automatically analyzed and used to extract HRV features associated with clinical deterioration in sepsis. The predictive accuracy of our current model based on HRV features derived from the ECG only shows the potential of HRV measurements at the ED. Unlike other risk stratification tools employing multiple vital parameters this does not require manual calculation of the score and can be used on continuous data over time. Trial registration The protocol of this study is published by Quinten et al., 2017. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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