Open AccessHeart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department
Author(s) -
Nan Liu,
Marcel Lucas Chee,
Mabel Z.Q. Foo,
Jeremy Zhenwen Pong,
Di Guo,
Zhi Xiong Koh,
Andrew Fu Wah Ho,
Chenglin Niu,
Shu-Ling Chong,
Marcus Eng Hock Ong
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0249868
Subject(s) - medicine , emergency department , sepsis , logistic regression , receiver operating characteristic , emergency medicine , confidence interval , heart rate variability , retrospective cohort study , mortality rate , vital signs , stepwise regression , intensive care medicine , heart rate , surgery , blood pressure , psychiatry
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.