Mortality Reduction by Heart Rate Characteristic Monitoring in Very Low Birth Weight Neonates: A Randomized Trial

Abstract

Objective To test the hypothesis that heart rate characteristics (HRC) monitoring improves neonatal outcomes. Study design Two-group, parallel, individually randomized controlled clinical trial of 3003 very low birth weight infants in 9 NICUs. In one group, HRC monitoring was displayed; in the other, it was masked. The primary outcome was number of days alive and ventilator-free in the 120 days after randomization. Secondary outcomes were mortality, number of ventilator days, NICU stay and antibiotic use. Results Mortality was reduced in infants whose HRC monitoring was displayed, from 10.2% to 8.1% (HR = 0.78, 95% CI = 0.61 to 0.99, P = 0.04, number needed to monitor 48), and there was a trend toward increased days alive and ventilator-free (95.9 of 120 days compared to 93.6 in controls, P = 0.08). Mortality benefit was concentrated in infants with birth weight <1000g (HR=0.74, 95% CI 0.57 to 0.95, P=0.02, number needed to monitor 23). There were no significant differences in the other outcomes. Conclusion Heart rate characteristics monitoring can reduce mortality in very low birth weight infants.

Robust efficiency and actuator saturation explain healthy heart rate control and variability

“…Clinicians know that changes in actuator signals (e.g., HR increases) can signify a great variety of potentially important derangements such as hypovolemia, congestive heart failure, inflammation, sepsis, etc. (6)(7)(8)(9)(10)(11)(12)(13). Even without specific diagnostic content, alerts to clinicians that HRV is changing can be useful.…”

“…In this case, monitoring of HR characteristics is used to report a statistic that reflects the performance of the actuator mechanisms. The utilization of this statistic by clinicians was sufficient to change practice (i.e., reevaluation of the patient with consideration of need for additional therapies) and achieve a significant improvement in outcomes (10,13). This particular example of integration of mathematical analysis into monitors represents a special situation because the alert also incorporates the occurrence of cardiac decelerations, a phenomenon not observed in adults.…”

“…Decades of research demonstrate that heart rate (HR) in healthy humans has high variability, and loss of this high HR variability (HRV) is correlated with adverse states such as stress, fatigue, physiologic senescence, or disease (6)(7)(8)(9)(10)(11)(12)(13). The dominant approach to analysis of HRV has been to focus on statistics and patterns in HR time series that have been interpreted as fractal, chaotic, scale-free, critical, etc.…”

“…The dominant approach to analysis of HRV has been to focus on statistics and patterns in HR time series that have been interpreted as fractal, chaotic, scale-free, critical, etc. (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17). The appeal of time series analysis is understandable as it puts HRV in the context of a broad and popular approach to complex systems (5,18), all while requiring minimal attention to domain-specific (e.g., physiological) details.…”

Data intelligence is the future of monitoring

“…In contrast, a pragmatic randomized controlled trial, where patients are exposed simply to the presence or absence of monitoring, provides an attempt to assess the 'real world' impact of a novel monitor at the bedside in isolation. As detailed by Lake et al [1], the HeRO monitor was introduced at the bedside in a randomized fashion with no mandated response to the values being displayed [4]. As no intervention is mandated by its results, it is subject to variable interpretation and variable interventions by care providers.…”