HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF)

Preethi Krishnan; Milad G Rad; Palak Agarwal; Curtis Marshall; Philip Yang; Sivasubramanium V Bhavani; Andre L Holder; Annette Esper; Rishikesan Kamaleswaran

doi:10.1088/1361-6579/acf5c7

HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF)

Physiol Meas. 2023 Oct 13;44(10):105006. doi: 10.1088/1361-6579/acf5c7.

Authors

Preethi Krishnan^{1

2

3}, Milad G Rad^{3

4}, Palak Agarwal³, Curtis Marshall³, Philip Yang^{5

6}, Sivasubramanium V Bhavani^{5

6}, Andre L Holder^{5

6}, Annette Esper^{5

6}, Rishikesan Kamaleswaran^{2

3

5

6}

Affiliations

¹ Department of Biomedical Engineering, Emory University, Atlanta, GA, Georgia.
² Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, Georgia.
³ Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, Georgia.
⁴ Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, Georgia.
⁵ Emory Critical Care Center, Emory University School of Medicine, Atlanta, GA, Georgia.
⁶ Department of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Atlanta, GA, Georgia.

Abstract

Objective. To examine whether heart rate interval based rapid alert (HIRA) score derived from a combination model of heart rate variability (HRV) and modified early warning score (MEWS) is a surrogate for the detection of acute respiratory failure (ARF) in critically ill sepsis patients.Approach. Retrospective HRV analysis of sepsis patients admitted to Emory healthcare intensive care unit (ICU) was performed between sepsis-related ARF and sepsis controls without ARF. HRV measures such as time domain, frequency domain, and nonlinear measures were analyzed up to 24 h after patient admission, 1 h before the onset of ARF, and a random event time in the sepsis controls. Statistical significance was computed by the Wilcoxon Rank Sum test. Machine learning algorithms such as eXtreme Gradient Boosting and logistic regression were developed to validate the HIRA score model. The performance of HIRA and early warning score models were evaluated using the area under the receiver operating characteristic (AUROC).Main Results. A total of 89 (ICU) patients with sepsis were included in this retrospective cohort study, of whom 31 (34%) developed sepsis-related ARF and 58 (65%) were sepsis controls without ARF. Time-domain HRV for Electrocardiogram (ECG) Beat-to-Beat RR intervals strongly distinguished ARF patients from controls. HRV measures for nonlinear and frequency domains were significantly altered (p< 0.05) among ARF compared to controls. The HIRA score AUC: 0.93; 95% confidence interval (CI): 0.88-0.98) showed a higher predictive ability to detect ARF when compared to MEWS (AUC: 0.71; 95% CI: 0.50-0.90).Significance. HRV was significantly impaired across patients who developed ARF when compared to controls. The HIRA score uses non-invasively derived HRV and may be used to inform diagnostic and therapeutic decisions regarding the severity of sepsis and earlier identification of the need for mechanical ventilation.

Keywords: acute respiratory failure; heart rate variability; machine learning; sepsis.

Creative Commons Attribution license.

MeSH terms

Cell Cycle Proteins
Heart Rate / physiology
Histone Chaperones
Humans
Intensive Care Units
ROC Curve
Respiratory Insufficiency* / complications
Respiratory Insufficiency* / diagnosis
Retrospective Studies
Sepsis* / complications
Sepsis* / diagnosis
Transcription Factors

Substances

HIRA protein, human
Transcription Factors
Cell Cycle Proteins
Histone Chaperones

Abstract

MeSH terms

Substances

Grants and funding