Dataset Information

Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability.

ABSTRACT: OBJECTIVE:To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability. DESIGN:Multicenter, pilot study. SETTING:Several ICUs at Massachusetts General Hospital, Boston, MA. PATIENTS:We gathered 21,912 hours of routine electrocardiogram recordings from a heterogenous group of 70 adult ICU patients. All patients included in the study were mechanically ventilated and were receiving sedatives. MEASUREMENTS AND MAIN RESULTS:As "ground truth" for developing our method, we used Richmond Agitation Sedation Scale scores grouped into four levels denoted "comatose" (-5), "deeply sedated" (-4 to -3), "lightly sedated" (-2 to 0), and "agitated" (+1 to +4). We trained a support vector machine learning algorithm to calculate the probability of each sedation level from heart rate variability measures derived from the electrocardiogram. To estimate algorithm performance, we calculated leave-one-subject out cross-validated accuracy. The patient-independent version of the proposed system discriminated between the four sedation levels with an overall accuracy of 59%. Upon personalizing the system supplementing the training data with patient-specific calibration data, consisting of an individual's labeled heart rate variability epochs from the preceding 24 hours, accuracy improved to 67%. The personalized system discriminated between light- and deep-sedation states with an average accuracy of 75%. 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.

SUBMITTER: Nagaraj SB

PROVIDER: S-EPMC5474145 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability.

Nagaraj Sunil B SB Biswal Siddharth S Boyle Emily J EJ Zhou David W DW McClain Lauren M LM Bajwa Ednan K EK Quraishi Sadeq A SA Akeju Oluwaseun O Barbieri Riccardo R Purdon Patrick L PL Westover M Brandon MB

Critical care medicine 20170701 7

<h4>Objective</h4>To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability.<h4>Design</h4>Multicenter, pilot study.<h4>Setting</h4>Several ICUs at Massachusetts General Hospital, Boston, MA.<h4>Patients</h4>We gathered 21,912 hours of routine electrocardiogram recordings from a heterogenous group of 70 adult ICU patients. All patients included in the study were mechanically ventilated and were receiving sedatives.<h4>Measuremen ...[more]

PMID: 28441231

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Project description:Purpose: To investigate physicians' perception of patients' tolerance levels regarding sedation, which could affect sedation practice for mechanically ventilated (MV) patients. Methods: This is a questionnaire survey combined with a 24 h cross-sectional study. The physician's propensity score for light sedation (PS-LS) was estimated by his/her response to the given answers for each item of the questionnaire, which tested the levels of interviewee's desire to manage MV patient with light sedation. Thereby, the mean physicians' PS-LS of each participating ICU (ICU-meanPS-LS) was calculated. The practical measurements of all variables listed on the questionnaire were used to semi-quantitatively assess stimulus intensity of what the recruited patients suffered (i.e., semi-quantitative stimulus intensity, SSI). Sedation depth was assessed by Richmond Agitation Sedation Scale (RASS). Results: 555 of 558 (99.5%) physicians from 102 ICUs were concerned with patients' tolerance levels regarding sedation while titrating sedation depth. The physician's PS-LS was non-normally distributed with median (IQR) of 3 (0-5). ICU-meanPS-LS was calculated in 92 out of 102 ICUs participating in the cross-sectional study, which was ranged from -5 to 7 with a median (IQR) of 2.37 (0.16-4.33). A significant increasing trend in prevalence of light sedation was observed over increasing ICU-meanPS-LS quartiles (from Q1 to Q4, ?2-test for trend, p = 0.002). Moreover, odds ratio for probability of light sedation remained significant in MV patients from Q4 ICUs vs. Q1 ICUs, adjusted by APACHE II score (OR, 2.332; 95% CI: 1.463-3.717; p < 0.001) or SSI score (OR, 2.445; 95% CI: 1.468-4.074; p = 0.001). Notably, adjusted OR for mortality was significant in deeply sedated MV patients (OR, 2.034; 95% CI: 1.435-2.884; p < 0.001). Conclusions: ICU physician's individualized perception for patients' tolerance levels regarding sedation, in light sedation affected sedation practice for MV patients.

Project description:ImportanceAltered heart rate variability has been associated with autonomic dysfunction in a number of disease profiles, in this work we elucidate differences in the biomarker among patients with all-cause sepsis and coronavirus disease 2019.ObjectivesTo measure heart rate variability metrics in critically ill coronavirus disease 2019 patients with comparison to all-cause critically ill sepsis patients.Design setting and participantsRetrospective analysis of coronavirus disease 2019 patients admitted to an ICU for at least 24 hours at any of Emory Healthcare ICUs between March 2020 and April 2020 up to 5 days of ICU stay. The comparison group was a cohort of all-cause sepsis patients prior to coronavirus disease 2019 pandemic.Main outcomes and measuresContinuous waveforms were captured from the patient monitor. The electrocardiogram was then analyzed for each patient over a 300 seconds observational window that was shifted by 30 seconds in each iteration from admission till discharge. A total of 23 heart rate variability metrics were extracted in each iteration. We use the Kruskal-Wallis and Steel-Dwass tests (p < 0.05) for statistical analysis and interpretations of heart rate variability multiple measures.ResultsA total of 141 critically ill coronavirus disease 2019 patients met inclusion criteria, who were compared with 208 patients with all-cause sepsis. Three nonlinear markers, including the ratio of standard deviation derived from the Poincaré plot, sample entropy, and approximate entropy and four linear features, including mode of beat-to-beat interval, acceleration capacity, deceleration capacity, and the proportion of consecutive RR intervals that differ by more than 50 ms, were all statistically significant (p < 0.05) between the coronavirus disease 2019 and all-cause sepsis cohorts. The three nonlinear features and acceleration capacity, deceleration capacity, and beat-to-beat interval (mode) were statistically significant (p < 0.05) when comparing pairwise analysis among the combinations of survivors and nonsurvivors between the coronavirus disease 2019 and sepsis cohorts. Temporal analysis of the main markers showed low variability across the 5 days of analysis compared with sepsis patients.Conclusions and relevanceIn this descriptive statistical study, heart rate variability measures were found to be statistically different across critically ill patients infected with severe acute respiratory syndrome coronavirus 2 and distinct from bacterial sepsis.

Project description:Heart rate variability (HRV) is the variation of cardiac inter-beat intervals over time resulting largely from the interplay between the sympathetic and parasympathetic branches of the autonomic nervous system. Individual differences in HRV are associated with emotion regulation, personality, psychopathology, cardiovascular health, and mortality. Previous studies have shown significant heritability of HRV measures. Here we extend genetic research on HRV by investigating sex differences in genetic underpinnings of HRV, the degree of genetic overlap among different measurement domains of HRV, and phenotypic and genetic relationships between HRV and the resting heart rate (HR). We performed electrocardiogram (ECG) recordings in a large population-representative sample of young adult twins (n=1060 individuals) and computed HRV measures from three domains: time, frequency, and nonlinear dynamics. Genetic and environmental influences on HRV measures were estimated using linear structural equation modeling of twin data. The results showed that variability of HRV and HR measures can be accounted for by additive genetic and non-shared environmental influences (AE model), with no evidence for significant shared environmental effects. Heritability estimates ranged from 47 to 64%, with little difference across HRV measurement domains. Genetic influences did not differ between genders for most variables except the square root of the mean squared differences between successive R-R intervals (RMSSD, higher heritability in males) and the ratio of low to high frequency power (LF/HF, distinct genetic factors operating in males and females). The results indicate high phenotypic and especially genetic correlations between HRV measures from different domains, suggesting that >90% of genetic influences are shared across measures. Finally, about 40% of genetic variance in HRV was shared with HR. In conclusion, both HR and HRV measures are highly heritable traits in the general population of young adults, with high degree of genetic overlap across different measurement domains.

Dataset Information

Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability.

Publications

Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability.

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