ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019
Serial Heart Rate Variability Measures for Risk Prediction of Septic Patients in the Emergency Department.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Adding heart rate n-variability (HRnV) to clinical assessment potentially improves prediction of serious bacterial infections in young febrile infants at the emergency department: a prospective observational study.Annals of translational medicine · 2023Article
- Can Prehospital Data Improve Early Identification of Sepsis in Emergency Department? An Integrative Review of Machine Learning Approaches.Applied clinical informatics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, deltabased 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.
Indexed as
Identifiers
32308821PMC7153136W3019273933What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.