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.

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.

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.