Impact of sedation and organ failure on continuous heart and respiratory rate variability monitoring in critically ill patients

Bradley, Beverly; Green, Geoffrey; Ramsay, Tim; Seely, Andrew J. E.

doi:10.1097/ccm.0b013e31826a47de

Cited by 35 publications

(26 citation statements)

References 44 publications

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“…22,23 It has been shown that HRV decreases with sedation 24 and increases with sedation interruption. 25 There is also evidence to suggest that depressed HRV is predictive of overall outcome in the critical care unit. 26 Risk stratification in the ICU and the role of the various components of HRV analysis has also been further delineated in some detail.…”

Section: Discussionmentioning

confidence: 99%

Heart rate variability predicts 30-day all-cause mortality in intensive care units

Bishop

Wise

Lee

et al. 2016

Southern African Journal of Anaesthesia and Analgesia

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Background: Autonomic nervous function, as quantified by heart rate variability (HRV), has shown promise in predicting clinically important outcomes in the critical care setting; however, there is debate concerning its utility. HRV analysis was assessed as a practical tool for outcome prediction in two South African hospitals and compared with Acute Physiology and Chronic Health Evaluation II (APACHE II) scoring. Method: In a dual centre, prospective, observational cohort study of patients admitted to the intensive care units (ICU) of two hospitals in KwaZulu-Natal, South Africa frequency domain HRV parameters were explored as predictors of: all-cause mortality at 30 days after admission; ICU stay duration; the need for invasive ventilation; the need for inotrope/vasopressor therapy; and the need for renal replacement therapy. The predictive ability of HRV parameters against the APACHE II score for the study outcomes was also compared. Results: A total of 55 patients were included in the study. Very low frequency power (VLF) was shown to predict 30-day mortality in ICU (odds ratio 0.6; 95% confidence interval 0.396-0.911). When compared with APACHE II, VLF remained a significant predictor of outcome, suggesting that it adds a unique component of prediction. No HRV parameters were predictive for the other secondary outcomes. Conclusion: This study found that VLF independently predicted all-cause mortality at 30 days after ICU admission. VLF provided additional predictive ability above that of the APACHE II score. As suggested by this exploratory analysis larger multi-centre studies seem warranted.

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Section: Discussionmentioning

confidence: 99%

Heart rate variability predicts 30-day all-cause mortality in intensive care units

Bishop

Wise

Lee

et al. 2016

Southern African Journal of Anaesthesia and Analgesia

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“…Several studies have shown that conditions such as sepsis, anoxic brain injury, and multiple organ dysfunction syndrome strongly influence autonomic function (34–38). Similarly, previous work has shown that the modulation of HRV by depth of sedation is reduced in the setting of severe organ system dysfunction (8). It is thus likely that the nature and severity of underlying medical illness will need to be accounted for to optimize HRV-based sedation monitoring accuracy.…”

Section: Discussionmentioning

confidence: 54%

“…Previous pilot studies have suggested that some HRV features show systematic, drug-specific responses to anesthetic drugs (6, 7). Recently it was shown that sedation reduces heart rate and respiratory variability in patients without severe organ failure (8). …”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Sedation Levels in ICU Patients Using Heart Rate Variability

Nagaraj

McClain

Biswal

et al. 2016

Critical Care Medicine

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Objective To explore the potential value of HRV features for automated monitoring of sedation levels in mechanically ventilated ICU patients. Methods ECG recordings from 40 mechanically ventilated adult patients receiving sedatives in an ICU setting were utilized to develop and test the proposed automated system. Richmond Agitation-Sedation Scale (RASS) scores were acquired prospectively to assess patient sedation levels, and were used as ground truth. RASS scores were grouped into four levels, denoted “unarousable” (RASS = -5,-4), “sedated” (-3,-2,-1), “awake” (0), “agitated” (+1,+2,+3,+4). A multi-class support vector machine algorithm was used for classification. Classifier training and performance evaluations were carried out using leave-one-out cross validation. Results An overall accuracy of 69% was achieved for discriminating between the 4 levels of sedation. The proposed system was able to reliably discriminate (accuracy = 79%) between sedated (RASS <0) and non-sedated states (RASS >0). Conclusions With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and under-sedation.

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“…Intuitively, we suspect that these signals, if appropriately deconstructed, could provide real-time information about patient status and the need for and efficacy of interventions far beyond what could be extracted from observation of the signals or their trends (1). There are multiple studies, including the one reported in this issue of Critical Care Medicine (2), that support this possibility, but the clinical feasibility and benefit of doing so have not yet been conclusively demonstrated. One limitation is that simple plug-and-play technology that could permit anyone with normal computer skills to start archiving and analyzing data from all patients in a given ICU without a massive effort to provide coordination and technical support has yet to be developed, and even if it had been, there is no consensus on what to do with the data.…”

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confidence: 88%

Challenges of Heart Rate Variability Research in the ICU*

Stein¹

2013

Critical Care Medicine

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T he amount of long-term, multichannel ICU patient data that could potentially be archived and analyzed continues to mushroom with the growth of high-volume data acquisition and storage capability and faster and more sophisticated processors. Intuitively, we suspect that these signals, if appropriately deconstructed, could provide real-time information about patient status and the need for and efficacy of interventions far beyond what could be extracted from observation of the signals or their trends (1). There are multiple studies, including the one reported in this issue of Critical Care Medicine (2), that support this possibility, but the clinical feasibility and benefit of doing so have not yet been conclusively demonstrated. One limitation is that simple plug-and-play technology that could permit anyone with normal computer skills to start archiving and analyzing data from all patients in a given ICU without a massive effort to provide coordination and technical support has yet to be developed, and even if it had been, there is no consensus on what to do with the data. Thus, generating sophisticated real-time, clinically vital measures derived from ICU signals remains an exciting but unrealized dream. There are, however for the interested reader, numerous databases of stored ICU signals (e.g., on http://www. PhysioNet.org (3)) available for algorithm development (4). An extensive list of these resources can be found in a recent article by Burykin et al (4).The electrocardiogram (ECG) signal, which along with the respiratory signal, is the focus of the Bradley et al (2) article commented on here, is monitored in every ICU patient, and there is a huge literature on the prognostic information that can be obtained from it in various clinical and nonclinical populations, although much less information about its specific prognostic value when collected in real time in individuals (5). Analyzing the ECG signal permits the measurement of heart rate variability (HRV), in its many flavors and over a large range of time periods, a potentially important time-varying clinical measure in the ICU (6). Underlying this are two assumptions. The first is that the ECG can reliably be analyzed by automated methods, and the second is that specific HRV measures speak a language that can be unambiguously understood, for example, that high-frequency power always reflects parasympathetic control of heart rate or that the low-to-high frequency power ratio is a surrogate for sympathetic:parasympathetic balance. Piggybacking on this set of assumptions, various manufacturers offer black box HRV software for various applications, ECG in, numbers out, voilà! Although the practical problems in research level analysis of the huge volume of ECG signals from the ICU using good Holter scanning algorithms are apparent, attempting to measure HRV for clinical purposes without the ability to examine the actual ECG signal at the time of analysis and see the exact fiducial point at which successive beats are detected and the beat labels applied to th...

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Impact of sedation and organ failure on continuous heart and respiratory rate variability monitoring in critically ill patients

Cited by 35 publications

References 44 publications

Heart rate variability predicts 30-day all-cause mortality in intensive care units

Heart rate variability predicts 30-day all-cause mortality in intensive care units

Automatic Classification of Sedation Levels in ICU Patients Using Heart Rate Variability

Challenges of Heart Rate Variability Research in the ICU*

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