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.

OBJECTIVES: To analyze the temporal trend in enrollment rates in a COVID-19 platform trial during the first three waves of the pandemic in the United States. DESIGN: Secondary analysis of data from the I-SPY COVID randomized controlled trial (RCT). SETTING: Thirty-one hospitals throughout the United States. PATIENTS: Patients who were approached, either directly or via a legally authorized representative, for consent and enrollment into the I-SPY COVID RCT. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 1,338 patients approached for the I-SPY COVID trial from July 30, 2020, to February 17, 2022, the number of patients who enrolled (n = 1,063) versus declined participation (n = 275) was used to calculate monthly enrollment rates. Overall, demographic and baseline clinical characteristics were similar between those who enrolled versus declined. Enrollment rates fluctuated over the course of the COVID-19 pandemic, but there were no significant trends over time (Mann-Kendall test, p = 0.21). Enrollment rates were also comparable between vaccinated and unvaccinated patients. In multivariable logistic regression analysis, age, sex, region of residence, COVID-19 severity of illness, and vaccination status were not significantly associated with the decision to decline consent. CONCLUSIONS: In this secondary analysis of the I-SPY COVID clinical trial, there was no significant association between the enrollment rate and time period or vaccination status among all eligible patients approached for clinical trial participation. Additional studies are needed to better understand whether the COVID-19 pandemic has altered clinical trial participation and to develop strategies for encouraging participation in future COVID-19 and critical care clinical trials.