Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

INTRODUCTION: Prolonged ventilation and failed extubation are associated with increased harm and cost. The added value of heart and respiratory rate variability (HRV and RRV) during spontaneous breathing trials (SBTs) to predict extubation failure remains unknown. METHODS: We enrolled 721 patients in a multicenter (12 sites), prospective, observational study, evaluating clinical estimates of risk of extubation failure, physiologic measures recorded during SBTs, HRV and RRV recorded before and during the last SBT prior to extubation, and extubation outcomes. We excluded 287 patients because of protocol or technical violations, or poor data quality. Measures of variability (97 HRV, 82 RRV) were calculated from electrocardiogram and capnography waveforms followed by automated cleaning and variability analysis using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software. Repeated randomized subsampling with training, validation, and testing were used to derive and compare predictive models. RESULTS: Of 434 patients with high-quality data, 51 (12%) failed extubation. Two HRV and eight RRV measures showed statistically significant association with extubation failure (P <0.0041, 5% false discovery rate). An ensemble average of five univariate logistic regression models using RRV during SBT, yielding a probability of extubation failure (called WAVE score), demonstrated optimal predictive capacity. With repeated random subsampling and testing, the model showed mean receiver operating characteristic area under the curve (ROC AUC) of 0.69, higher than heart rate (0.51), rapid shallow breathing index (RBSI; 0.61) and respiratory rate (0.63). After deriving a WAVE model based on all data, training-set performance demonstrated that the model increased its predictive power when applied to patients conventionally considered high risk: a WAVE score >0.5 in patients with RSBI >105 and perceived high risk of failure yielded a fold increase in risk of extubation failure of 3.0 (95% confidence interval (CI) 1.2 to 5.2) and 3.5 (95% CI 1.9 to 5.4), respectively. CONCLUSIONS: Altered HRV and RRV (during the SBT prior to extubation) are significantly associated with extubation failure. A predictive model using RRV during the last SBT provided optimal accuracy of prediction in all patients, with improved accuracy when combined with clinical impression or RSBI. This model requires a validation cohort to evaluate accuracy and generalizability. TRIAL REGISTRATION: ClinicalTrials.gov NCT01237886. Registered 13 October 2010.

Heart rate variability during exertional heat stress: effects of heat production and treatment.

PURPOSE: We assessed the efficacy of different treatments (i.e., treatment with ice water immersion vs. natural recovery) and the effect of exercise intensities (i.e., low vs. high) for restoring heart rate variability (HRV) indices during recovery from exertional heat stress (EHS). METHODS: Nine healthy adults (26 ± 3 years, 174.2 ± 3.8 cm, 74.6 ± 4.3 kg, 17.9 ± 2.8 % body fat, 57 ± 2 mL·kg·(-1) min(-1) peak oxygen uptake) completed four EHS sessions incorporating either walking (4.0-4.5 km·h(-1), 2 % incline) or jogging (~7.0 km·h(-1), 2 % incline) on a treadmill in a hot-dry environment (40 °C, 20-30 % relative humidity) while wearing a non-permeable rain poncho for a slow or fast rate of rectal temperature (T re) increase, respectively. Upon reaching a T re of 39.5 °C, participants recovered until T re returned to 38 °C either passively or with whole-body immersion in 2 °C water. A comprehensive panel of 93 HRV measures were computed from the time, frequency, time-frequency, scale-invariant, entropy and non-linear domains. RESULTS: Exertional heat stress significantly affected 60/93 HRV measures analysed. Analyses during recovery demonstrated that there were no significant differences between HRV measures that had been influenced by EHS at the end of passive recovery vs. whole-body cooling treatment (p > 0.05). Nevertheless, the cooling treatment required statistically significantly less time to reduce T re (p < 0.001). CONCLUSIONS: While EHS has a marked effect on autonomic nervous system modulation and whole-body immersion in 2 °C water results in faster cooling, there were no observed differences in restoration of autonomic heart rate modulation as measured by HRV indices with whole-body cold-water immersion compared to passive recovery in thermoneutral conditions.

Impact of sedation and organ failure on continuous heart and respiratory rate variability monitoring in critically ill patients: a pilot study.

OBJECTIVE: Our aim is to better characterize the impact of sedation and its interruption on continuously monitored heart rate variability and respiratory rate variability in critically ill patients. We aim to explore whether sedation reduces heart rate variability and respiratory rate variability in critically ill patients and whether the extent of reduction depends on degree of organ dysfunction. DESIGN: Prospective observational pilot study. SETTING: Intensive care unit in tertiary care teaching hospital. PATIENTS: Thirty-three critically ill adult patients experiencing respiratory and/or cardiac failure. INTERVENTIONS: Electrocardiogram and end-tidal capnography waveform capture were initiated from admission or intubation, respectively, and continued to intensive care unit discharge or a maximum of 14 d. MEASUREMENTS AND MAIN RESULTS: All patient days with a sedation interruption (defined as cessation of a continuous infusion of sedation agent) were identified. Mean heart rate variability and respiratory rate variability were computed over two periods: 4 hrs directly prior to the sedation interruption, and the duration of sedation interruption (median: 1 hr 45 mins, interquartile range: 4 hrs 15 mins or max 4 hrs). Severity of organ dysfunction was assessed through multiple organ dysfunction syndrome scores, and sedative agents were recorded for each sedation interruption. Multiple organ dysfunction syndrome levels were defined as low (0-2), medium (3-4), and high (> 4). Variability before and during sedation interruption was compared and analyzed across multiple organ dysfunction syndrome levels and sedative types. Our results suggest that both heart rate variability and respiratory rate variability increased during sedation interruption (p < 0.05 for coefficient of variation). Patients with low and medium multiple organ dysfunction syndrome experienced greater increase in heart rate variability during sedation interruption (p < 0.05 for coefficient of variation), compared to patients with high multiple organ dysfunction syndrome, who failed to mount a significant increase in heart rate variability when sedation was stopped. Similarly, sedation interruption led to increased respiratory rate variability for low multiple organ dysfunction syndrome patients (p < 0.05 for SD), but in contrast, a further deterioration in respiratory rate variability occurred in the high multiple organ dysfunction syndrome patients. All trends persisted when controlling for sedative agents. CONCLUSIONS: Interruption of sedation allows for uncovering a greater restoration of heart rate variability and respiratory rate variability in patients with low organ failure. The further reduction in respiratory variability during the elimination of sedation in patients with high multiple organ dysfunction syndrome suggests a differential response and benefit from sedation interruption, and merits further investigation. As reduced variability correlates with severity of illness, and need for sedation depends on organ failure, variability monitoring may offer a dynamic measure of a variable response to the benefit, timing, and duration of sedation interruption.

Neighborhood Sociodemographics and Change in Built Infrastructure.

While increasing evidence suggests an association between physical infrastructure in neighbourhoods and health outcomes, relatively little research examines how neighbourhoods change physically over time and how these physical improvements are spatially distributed across populations. This paper describes the change over 25 years (1985-2010) in bicycle lanes, off-road trails, bus transit service, and parks, and spatial clusters of changes in these domains relative to neighbourhood sociodemographics in four U.S. cities that are diverse in terms of geography, size and population. Across all four cities, we identified increases in bicycle lanes, off-road trails, and bus transit service, with spatial clustering in these changes that related to neighbourhood sociodemographics. Overall, we found evidence of positive changes in physical infrastructure commonly identified as supportive of physical activity. However, the patterning of infrastructure change by sociodemographic change encourages attention to the equity in infrastructure improvements across neighbourhoods.

Sampling rate of heart rate variability impacts the ability to detect acidemia in ovine fetuses near-term.

BACKGROUND: To evaluate the impact of sampling rate on the predictive capability of continuous fetal heart rate (FHR) variability (fHRV) monitoring for detecting fetal acidemia during labor, we tested the performance of the root mean square of successive differences (RMSSD) in R-R intervals from the ECG when acquired with the sampling rate of 4 Hz currently available in FHR monitors, in comparison to the gold standard of 1000 Hz. METHODS: Near-term ovine fetuses (N = 9) were chronically prepared with precordial electrodes for recording ECG, vascular catheters for blood sampling, and an umbilical cord occluder. For 1 min every 2.5 min, animals underwent mild partial umbilical cord occlusions (UCO) × 1 h, moderate partial UCO × 1 h, then complete UCO × 2 h, or until arterial pH reached <7.00. Arterial blood samples were drawn at baseline and every 20 min during the UCO series. RMSSD was calculated continuously in 5 min windows using an automated, standardized system (CIMVA.com). RESULTS are presented as mean ± SEM with significance assumed for p < 0.05. RESULTS: Repetitive UCO resulted in pH decreasing from 7.35 ± 0.01 to 7.00 ± 0.03. In all nine animals, RMSSD increased from 16.7 ± 1.0 ms at baseline to 44.4 ± 2.3 ms, 70 ± 15 min prior to reaching the pH nadir when sampled at 1000 Hz. When sampled at 4 Hz, RMSSD at baseline measured 36.1 ± 6.0 ms and showed no significant increase during the UCO series until the pH nadir was reached. Consequently, early detection of severe hypoxic-acidemia would have been missed in all fetuses. CONCLUSION: RMSSD as a measure of fHRV when calculated from FHR sampled at 1000 Hz allowed for the early detection of worsening hypoxic-acidemia in each fetus. However, when calculated at the low sampling rate of 4 Hz used clinically, RMSSD remained unchanged until terminally when the nadir pH was reached. For early detection of fetal acidemia during labor, more sensitive means of acquiring FHR are therefore recommended than currently deployed, e.g., trans-abdominal fetal ECG.

Continuous multiorgan variability analysis to track severity of organ failure in critically ill patients.

PURPOSE: The purpose of this study is to evaluate the utility of using continuous heart rate variability (HRV) and respiratory rate variability (RRV) monitoring for (a) tracking daily organ dysfunction in critically ill patients and (b) identifying patterns of variability changes during onset of shock and resolution of respiratory failure. MATERIALS AND METHODS: Thirty-three critically ill patients experiencing respiratory and/or cardiac failure underwent continuous recording of their electrocardiogram and capnogram (CO2) waveforms from admission or intubation until discharge (maximum 14 days). HRV and RRV were computed in 5-minute overlapping windows, using Continuous Individualized Multi-organ Variability Analysis software. Multiple organ dysfunction scores were recorded daily. HRV and RRV trajectories were characterized during onset of shock and resolution of respiratory failure. RESULTS: Both HRV and RRV decreased with increasing severity of multiple organ dysfunction scores for a variety of variability metrics. A decline in several measures of HRV and no decline in RRV were observed before onset of shock (n=6). In contrast, during resolution of respiratory failure, an increase in RRV was observed in patients who successfully passed extubation (n=12), with no change in RRV in those who subsequently failed extubation (n=2). CONCLUSIONS: There is an association between reduced HRV and RRV and increasing organ dysfunction in critically ill patients. The significance of observing trends of decreasing HRV (with onset of shock) and increasing RRV (with resolution of respiratory failure) merits further investigation.

Comparison of heart and respiratory rate variability measures using an intermittent incremental submaximal exercise model.

To better understand the alterations in cardiorespiratory variability during exercise, the present study characterized the patterns of change in heart rate variability (HRV), respiratory rate variability (RRV), and combined cardiorespiratory variability (HRV-RRV) during an intermittent incremental submaximal exercise model. Six males and six females completed a submaximal exercise protocol consisting of an initial baseline resting period followed by three 10-min bouts of exercise at 20%, 40%, and 60% of maximal aerobic capacity (V̇O2max). The R-R interval and interbreath interval variability were measured at baseline rest and throughout the submaximal exercise. A group of 93 HRV, 83 RRV, and 28 HRV-RRV measures of variability were tracked over time through a windowed analysis using a 5-min window size and 30-s window step. A total of 91 HRV measures were able to detect the presence of exercise, whereas only 46 RRV and 3 HRV-RRV measures were able to detect the same stimulus. Moreover, there was a loss of overall HRV and RRV, loss of complexity of HRV and RRV, and loss of parasympathetic modulation of HRV (up to 40% V̇O2max) with exercise. Conflicting changes in scale-invariant structure of HRV and RRV with increases in exercise intensity were also observed. In summary, in this simultaneous evaluation of HRV and RRV, we found more consistent changes across HRV metrics compared with RRV and HRV-RRV.

Do physiological and pathological stresses produce different changes in heart rate variability?

Although physiological (e.g., exercise) and pathological (e.g., infection) stress affecting the cardiovascular system have both been documented to be associated with a reduction in overall heart rate variability (HRV), it remains unclear if loss of HRV is ubiquitously similar across different domains of variability analysis or if distinct patterns of altered HRV exist depending on the stressor. Using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software, heart rate (HR) and four selected measures of variability were measured over time (windowed analysis) from two datasets, a set (n = 13) of patients who developed systemic infection (i.e., sepsis) after bone marrow transplant (BMT), and a matched set of healthy subjects undergoing physical exercise under controlled conditions. HR and the four HRV measures showed similar trends in both sepsis and exercise. The comparison through Wilcoxon sign-rank test of the levels of variability at baseline and during the stress (i.e., exercise or after days of sepsis development) showed similar changes, except for LF/HF, ratio of power at low (LF) and high (HF) frequencies (associated with sympathovagal modulation), which was affected by exercise but did not show any change during sepsis. Furthermore, HRV measures during sepsis showed a lower level of correlation with each other, as compared to HRV during exercise. In conclusion, this exploratory study highlights similar responses during both exercise and infection, with differences in terms of correlation and inter-subject fluctuations, whose physiologic significance merits further investigation.

Monitoring and identification of sepsis development through a composite measure of heart rate variability.

Tracking the physiological conditions of a patient developing infection is of utmost importance to provide optimal care at an early stage. This work presents a procedure to integrate multiple measures of heart rate variability into a unique measure for the tracking of sepsis development. An early warning system is used to illustrate its potential clinical value. The study involved 17 adults (age median 51 (interquartile range 46-62)) who experienced a period of neutropenia following chemoradiotherapy and bone marrow transplant; 14 developed sepsis, and 3 did not. A comprehensive panel (N = 92) of variability measures was calculated for 5 min-windows throughout the period of monitoring (12 ± 4 days). Variability measures underwent filtering and two steps of data reduction with the objective of enhancing the information related to the greatest degree of change. The proposed composite measure was capable of tracking the development of sepsis in 12 out of 14 patients. Simulating a real-time monitoring setting, the sum of the energy over the very low frequency range of the composite measure was used to classify the probability of developing sepsis. The composite revealed information about the onset of sepsis about 60 hours (median value) before of sepsis diagnosis. In a real monitoring setting this quicker detection time would be associated to increased efficacy in the treatment of sepsis, therefore highlighting the potential clinical utility of a composite measure of variability.

Feasibility of continuous multiorgan variability analysis in the intensive care unit.

PURPOSE: The aim of the study was to evaluate the feasibility of continuous heart and respiratory rate variability (HRV and RRV, respectively) monitoring in critically ill patients derived from electrocardiogram (ECG) and end-tidal capnography (etCO(2)) waveforms. METHODS: Thirty-four patients (age, 56.5 ± 15.9 years; Acute Physiology and Chronic Health Evaluation II score, 22.8 ± 6.7) underwent continuous recording of ECG and etCO(2) waveforms from intensive care unit admission and intubation to discharge or maximum of 14 days. Overlapping 5-minute windows were analyzed with a wide range of variability measures (time, frequency, entropy, and scale-invariant and nonlinear domains). Waveform data quality, presence of disconnections and arrhythmias, quality of beat and breath detection, and subsequent variability computations were evaluated. RESULTS: Patients were enrolled for 11.0 ± 3.6 days. The proportion of missing waveform data among all patients was (median [interquartile range, maximum]) 2.9% (1.3%-9.7%, 36.4%) for ECG and 3.1% (1.1%-11.4%, 84.5%) for etCO(2). Heart rate variability data loss (ie, proportion of windows removed) was 1.3% (1.0%-2.1%, 5.9%) due to disconnection, 0.6% (0.1%-3.9%, 39.5%) due to atrial fibrillation, and 6.6% (1.4%-17.9%, 89.0%) due to data cleaning. Respiratory rate variability data loss was 7.3% (2.9%-11.6%, 47.7%) due to disconnection (or apnea) and 5.5% (2.9%-8.4%, 56.4%) due to cleaning. Continuous individualized multiorgan variability analysis processing resulted in HRV and RRV computations for 81.2% ± 25.0% and 87.5% ± 11.9% of available ECG and etCO(2) waveform data, respectively. CONCLUSIONS: The quality of continuously recorded ECG and etCO(2) waveforms in critically ill patients is adequate for subsequent continuous variability monitoring in this pilot study. The clinical utility of continuous variability analysis merits further investigation.

Continuous Multiorgan Variability monitoring in critically ill patients--complexity science at the bedside.

Complex systems science has led to valuable insights regarding the care and understanding of critical illness, but has not led to fundamental improvements to care to date. Realizing the fact that there is inherent uncertainty in patient trajectory, we have developed Continuous Individual Multiorgan Variability Analysis (CIMVA) as a tool theoretically and practically designed to track the systemic emergent properties of the host response to injury or infection. We present an overview of CIMVA software, and discuss four separate potential clinical applications that we are evaluating; including early detection of infection, better prediction of extubation failure, continuous monitoring of severity of illness in the ICU, and the evaluation of cardiopulmonary fitness. Future challenges are discussed in conclusion.

Diurnal variation in heart rate variability before and after maximal exercise testing.

As heart-rate variability (HRV) is under evaluation in clinical applications, the authors sought to better define the interdependent impact of age, maximal exercise, and diurnal variation under physiologic conditions. The authors evaluated the diurnal changes in HRV 24-h pre- and post-maximal aerobic exercise testing to exhaustion in young (19-25 yrs, n = 12) and middle-aged (40-55 yrs, n = 12) adults. Subjects wore a portable 5-lead electrocardiogram holter for 48 h (24 h prior to and following a maximal aerobic capacity test). Time-, frequency-, time-frequency-, and scale-invariant-domain measures of HRV were computed from RR-interval data analyzed using a 5-min window size and a 2.5-min step size, resulting in a different set of outputs every 2.5 min. Results were averaged (mean ± SE) over four prespecified time periods during the morning, afternoon, evening, and night on Day 1 and Day 2. Diurnal changes in HRV in young and middle-aged adults were compared using a two-way, repeated-measures analysis of variance (ANOVA). Young adults demonstrated higher HRV compared to middle-aged adults during periods of wakefulness and sleep prior to maximal exercise stress testing (i.e., high-frequency power during Day 1: young adults: morning 1862 ± 496 ms(2), afternoon 1797 ± 384 ms(2), evening 1908 ± 431 ms(2), and night 3202 ± 728 ms(2); middle-aged adults: morning 341 ± 53 ms(2), afternoon 405 ± 68 ms(2), evening 469 ± 80 ms(2), and night 836 ± 136 ms(2)) (p < .05). Exercise resulted in reductions in HRV such that multiple measures of HRV were not significantly different between age groups during the afternoon and evening periods. All measures of HRV demonstrated between-group differences overnight on Day 2 (p < .05). Young adults are associated with higher baseline HRV during the daytime. Sleep increases variability equally and proportionally to daytime variability. Given the higher baseline awake HRV and equal rise in HRV during sleep, the change in HRV from sleep to morning with exercise is greater in younger subjects. These physiologic results have clinical significance in understanding the pathophysiology of altered variability in ill patients.

Comparison of the heart and breathing rate of acutely ill medical patients recorded by nursing staff with those measured over 5 min by a piezoelectric belt and ECG monitor at the time of admission to hospital.

BACKGROUND: Heart and breathing rates are predictors of disease severity and of a poor outcome. However, few reports have compared their machine measurements with traditional manual methods. SETTING: A small rural Irish hospital. METHODS: The heart and breathing rates of 377 acutely ill medical patients (mean age 68.3 SD 16.8 years) recorded by nursing staff at the time of admission to hospital was compared with those measured over 5 min by a piezoelectric belt and ECG monitor (the BT16 acquisition system). RESULTS: The mean breathing rate measured by the nursing staff (20.9 SD 4.8 breaths per min) and that measured by the BT16 piezoelectric belt (19.9 SD 4.5 breaths per min) were significantly different (p 0.004), as were the nurse and BT16 measured heart rates (85.4 SD 21.3 vs. 81.2 SD 18.7, p 0.004), and the correlation coefficient between the two methods of breathing and heart rate measurement were low. Nurse measured breathing rate measurements were clustered around rates of 18, 20 and 22 breaths per min. Unlike those obtained by nurses, BT16 measured heart and breathing rates were shown by logistic regression to be independent predictors of in-hospital mortality. CONCLUSION: There is a poor correlation between breathing and heart rates measured by traditional methods and those obtained by the BT16 device. BT16 derived breathing and heart rates, but not those measured manually, were independent predictors of in-hospital mortality.

Identification of food spoilage in the smart home based on neural and fuzzy processing of odour sensor responses.

Adopting the use of real-time odour monitoring in the smart home has the potential to alert the occupant of unsafe or unsanitary conditions. In this paper, we measured (with a commercial metal-oxide sensor-based electronic nose) the odours of five household foods that had been left out at room temperature for a week to spoil. A multilayer perceptron (MLP) neural network was trained to recognize the age of the samples (a quantity related to the degree of spoilage). For four of these foods, median correlation coefficients (between target values and MLP outputs) of R > 0.97 were observed. Fuzzy C-means clustering (FCM) was applied to the evolving odour patterns of spoiling milk, which had been sampled more frequently (4h intervals for 7 days). The FCM results showed that both the freshest and oldest milk samples had a high degree of membership in "fresh" and "spoiled" clusters, respectively. In the future, as advancements in electronic nose development remove the present barriers to acceptance, signal processing methods like those explored in this paper can be incorporated into odour monitoring systems used in the smart home.

An investigation into the suitability of using three electronic nose instruments for the detection and discrimination of bacteria types.

The use of electronic nose (e-nose) technology for detection of food-borne bacteria has several practical advantages over current laboratory procedures, such as lower cost and reduced testing time. In this work, we are interested in using electronic nose systems to detect E. coli and Listeria in a nutrient broth, and discriminate between these bacteria types at various concentrations. To do this, we use instruments based on three different technologies - fingerprint mass spectrometry, metal oxide sensors, and conductive polymer sensors. Our results indicate that separation between groups can be achieved. We describe the relative merits and drawbacks of each technology and discuss how this rich multimodal dataset can be used to build a classification system.

Spatially adaptive wavelet thresholding of rubidium-82 cardiac PET images.

82Rb positron emission tomography (PET) images can be used to diagnose coronary artery disease (CAD), but deterioration of the images due to noise can compromise their quality. This work presents our work on a wavelet-based thresholding method for denoising cardiac /sup 82/Rb PET images. Our approach is based on a three-dimensional (3D) discrete dyadic wavelet transform, with iterative thresholding of coefficients being performed in a spatially adaptive context. A subjective analysis indicates that this technique leads to better discrimination between diagnostically significant features and noise when observing the denoised images and the corresponding cardiac polar maps. Results are presented for cardiac PET images of a normal subject (low risk of CAD) using a figure of merit based on expected image properties for such a patient. The increase in image quality is substantial when the proposed method is compared to conventional denoising protocols.