ArticlePloS one2021
Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Associations of Heart Rate Trajectories with Mortality and AKI Occurrence in Septic Patients: A Retrospective Study from MIMIC-IV and eICU Databases.Journal of clinical medicine · 2026Article
- Heart rate variability findings in neurological decompression sickness: an exploratory case series.Frontiers in physiology · 2026Article
- Analyzing Heart Rate Variability for COVID-19 ICU Mortality Prediction Using Continuous Signal Processing Techniques.Journal of clinical medicine · 2025Article
- Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.BMC medical informatics and decision making · 2024Article
- Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock.Biomedical signal processing and control · 2024Article
- The Pulse of Singapore: Short-Term HRV Norms.Applied psychophysiology and biofeedback · 2024Article
- Article
- Embracing complexity in sepsis.Critical care (London, England) · 2023Review
- 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
- Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study.Frontiers in digital health · 2023Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a potentially life-threatening condition that requires prompt recognition and treatment. Recently, heart rate variability (HRV), a measure of the cardiac autonomic regulation derived from short electrocardiogram tracings, has been found to correlate with sepsis mortality. This paper presents using novel heart rate n-variability (HRnV) measures for sepsis mortality risk prediction and comparing against current mortality prediction scores. This study was a retrospective cohort study on patients presenting to the emergency department of a tertiary hospital in Singapore between September 2014 to April 2017. Patients were included if they were above 21 years old and were suspected of having sepsis by their attending physician. The primary outcome was 30-day in-hospital mortality. Stepwise multivariable logistic regression model was built to predict the outcome, and the results based on 10-fold cross-validation were presented using receiver operating curve analysis. The final predictive model comprised 21 variables, including four vital signs, two HRV parameters, and 15 HRnV parameters. The area under the curve of the model was 0.77 (95% confidence interval 0.70-0.84), outperforming several established clinical scores. The HRnV measures may have the potential to allow for a rapid, objective, and accurate means of patient risk stratification for sepsis severity and mortality. Our exploration of the use of wealthy inherent information obtained from novel HRnV measures could also create a new perspective for data scientists to develop innovative approaches for ECG analysis and risk monitoring.
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