Early warning of infection is critical to reduce risk of deterioration and mortality, especially in neutropenic patients following hematopoietic stem cell transplantation (HCT). Given that heart rate variability (HRV) is a sensitive and early marker for infection and serum inflammatory biomarkers can have high specificity for infection, we hypothesized their combination may be useful for accurate early warning of infection.
Develop and evaluate a composite predictive model utilizing continuous HRV with daily serum biomarker measurements to provide risk stratification of future deterioration in HCT patients.
116 ambulatory outpatients about to undergo HCT consented to collection of prospective demographic, clinical (daily vital signs), HRV (continuous electrocardiogram (ECG) monitoring, laboratory (daily serum samples frozen -80°C) and infection outcome variables (defined as the time of escalation of antibiotics), all from 24 hours (h) pre-transplant until infection or 14 days post-transplant. Indications for antibiotic escalation were adjudicated as “true infection” or not by two blinded HCT clinicians. A composite time series of 8 HRV metrics was created for each patient and the probability of deterioration within the next 72h was estimated using logistic regression modelling of composite HRV and serum biomarkers using a rule-based Naïve-Bayes model, if the HRV-based probability exceeded a median threshold.
35(30%) patients withdrew within 90% incidence of subsequent infection within 72h), average risk (∼50%) and low risk (<10%), with an area under the receiver operating characteristic curve (AUC-ROC) of 0.87.
We derived a predictive model using HRV and serum biomarker to predict being diagnosed with infection within 72h combined in patients at high risk of infection. As prophylactic predictive ECG monitoring and daily serum collection proved challenging for many patients, further refinement in measurement is necessary for further study.

Copyright © 2021. Published by Elsevier Inc.

Author