A predictive model incorporating novel heart rate n-variability (HRnV) measures achieved an area under the curve of 0.77 for predicting 30-day in-hospital mortality, outperforming established clinical scores.
Cohort (n=342)
No
Do novel heart rate n-variability (HRnV) measures improve the prediction of 30-day in-hospital mortality in adult patients with suspected sepsis compared to established clinical scores?
Novel heart rate n-variability (HRnV) measures incorporated into a predictive model can accurately predict 30-day in-hospital mortality in patients with suspected sepsis.
Effect estimate: AUC 0.77 (95% CI 0.70-0.84)
Absolute Event Rate: 0.77% vs 0.74%
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.
Liu et al. (2021) conducted a cohort in Sepsis (n=342). Heart rate n-variability (HRnV) measures vs. Established clinical scores (NEWS, MEWS, SOFA, APACHE II, qSOFA) was evaluated on 30-day in-hospital mortality prediction (AUC) (AUC 0.77, 95% CI 0.70-0.84). A predictive model incorporating novel heart rate n-variability (HRnV) measures achieved an area under the curve of 0.77 for predicting 30-day in-hospital mortality, outperforming established clinical scores.