Description
In this study, we used serial heart rate variability (HRV) measures over 2 hours to improve the prediction of 30-day in-hospital mortality among septic patients in the emergency department (ED). We presented a generalizable methodology for processing and analysing HRV time series (HRVTS) data which may be noisy and incomplete. Feature sets were created from the HRVTS data of 162 patients with suspected sepsis using aggregation-based, delta-based and regression-based series-to-point transformations, and modelled over 100 random stratified splits. An optimized feature set comprising 12 selected HRVTS features performed better than baseline feature sets which only included patient demographics, vital signs and single time-point HRV measures taken at triage. This improved risk stratification approach could be used in the ED to identify high-risk septic patients for appropriate management and disposition.
Learning Objective: - Use heart rate variability (HRV) measures to risk stratify septic patients presenting to the emergency department
- Process and analyse HRV time series data derived from continuous electrocardiogram monitoring over time
Authors:
Calvin Chiew, National University Health System
Han Wang, National University of Singapore, National University Health System
Marcus Eng Hock Ong, Singapore General Hospital
Ting Hway Wong, Singapore General Hospital
Zhi Xiong Koh, Singapore General Hospital
Nan Liu, Singapore Health Services
Feng Mengling (Presenter)
National University of Singapore, National University Health System
Presentation Materials: