Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2018
Cardiorespiratory instability in monitored step-down unit patients: using cluster analysis to identify patterns of change.
Cardiorespiratory instability (CRI) in monitored step-down unit (SDU) patients has a variety of etiologies, and likely manifests in patterns of vital signs (VS) changes. We explored use of clustering techniques to identify patterns in the initial CRI epoch (CRI1; first exceedances of VS beyond stability thresholds after SDU admission) of unstable patients, and inter-cluster differences in admission characteristics and outcomes. Continuous noninvasive monitoring of heart rate (HR), respiratory rate (RR), and pulse oximetry (SpO2) were sampled at 1/20 Hz. ⋯ Three different clusters of VS presentations for CRI1 were identified. Clusters varied on age, number of comorbidities and hospital length of stay. Future study is needed to determine if there are common physiologic underpinnings of VS clusters which might inform clinical decision-making when CRI first manifests.
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J Clin Monit Comput · Feb 2018
Comparative StudyIn vivo investigation of ear canal pulse oximetry during hypothermia.
Pulse oximeters rely on the technique of photoplethysmography (PPG) to estimate arterial oxygen saturation (SpO[Formula: see text]). In conditions of poor peripheral perfusion such as hypotension, hypothermia, and vasoconstriction, the PPG signals detected are often weak and noisy, or in some cases unobtainable. Hence, pulse oximeters produce erroneous SpO[Formula: see text] readings in these circumstances. ⋯ The SpO[Formula: see text]s estimated from the finger sensor have dropped below 90% in five volunteers (failure) by the end of the cold exposure. The ear canal sensor, on the other hand, had only failed in one volunteer. These results strongly suggest that the ear canal may be used as a suitable alternative site for monitoring PPGs and arterial blood oxygen saturation at times were peripheral perfusion is compromised.
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J Clin Monit Comput · Feb 2018
Cross-comparisons of trending accuracies of continuous cardiac-output measurements: pulse contour analysis, bioreactance, and pulmonary-artery catheter.
We compared the similarity of cardiac-output (CO) estimates between available bolus thermodilution pulmonary-artery catheters (PAC), arterial pulse-contour analysis (LiDCOplus™, FloTrac™ and PiCCOplus™), and bioreactance (NICOM™). Repetitive simultaneous estimates of CO obtained from the above devices were compared in 21 cardiac-surgery patients during the first 2 h post-surgery. Mean and absolute values for CO across the devices were compared by ANOVA, Bland-Altman, Pearson moment, and linear-regression analyses. ⋯ From Pearson moment analysis, dynamic changes in CO, estimated by each device, showed good cross-correlations. Although all devices studied recorded similar mean CO values, which dynamically changed in similar directions, they have markedly different bias and precision values relative to each other. Thus, results from prior studies that have used one device to estimate CO cannot be used to validate others devices.
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J Clin Monit Comput · Feb 2018
Assessing nitrous oxide effect using electroencephalographically-based depth of anesthesia measures cortical state and cortical input.
Existing electroencephalography (EEG) based depth of anesthesia monitors cannot reliably track sedative or anesthetic states during n-methyl-D-aspartate (NMDA) receptor antagonist based anesthesia with ketamine or nitrous oxide (N2O). Here, a physiologically-motivated depth of anesthesia monitoring algorithm based on autoregressive-moving-average (ARMA) modeling and derivative measures of interest, Cortical State (CS) and Cortical Input (CI), is retrospectively applied in an exploratory manner to the NMDA receptor antagonist N2O, an adjuvant anesthetic gas used in clinical practice. Composite Cortical State (CCS) and Composite Cortical State distance (CCSd), two new modifications of CS, along with CS and CI were evaluated on electroencephalographic (EEG) data of healthy control individuals undergoing N2O inhalation up to equilibrated peak gas concentrations of 20, 40 or 60% N2O/O2. ⋯ These results indicate that, contrary to previous depth of anesthesia monitoring measures, the CS, CCS, and especially CCSd measures derived from frontal EEG are potentially useful for differentiating gas concentration and responsiveness levels in people under N2O. On the other hand, determining the utility of CI in this regard will require larger sample sizes and potentially higher gas concentrations. Future work will assess the sensitivity of CS-based and CI measures to other anesthetics and their utility in a clinical environment.
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J Clin Monit Comput · Feb 2018
ADARRI: a novel method to detect spurious R-peaks in the electrocardiogram for heart rate variability analysis in the intensive care unit.
We developed a simple and fully automated method for detecting artifacts in the R-R interval (RRI) time series of the ECG that is tailored to the intensive care unit (ICU) setting. From ECG recordings of 50 adult ICU-subjects we selected 60 epochs with valid R-peak detections and 60 epochs containing artifacts leading to missed or false positive R-peak detections. Next, we calculated the absolute value of the difference between two adjacent RRIs (adRRI), and obtained the empirical probability distributions of adRRI values for valid R-peaks and artifacts. ⋯ Our method showed superior performance for detecting artifacts with sensitivity 100%, specificity 99%, precision 99%, positive likelihood ratio of 100 and negative likelihood ratio <0.001 compared to Berntson's and Clifford's method with a sensitivity, specificity, precision and positive and negative likelihood ratio of 99%, 78%, 82%, 4.5, 0.013 for Berntson's method and 55%, 98%, 96%, 27.5, 0.460 for Clifford's method, respectively. A novel algorithm using a patient-independent threshold derived from the distribution of adRRI values in ICU ECG data identifies artifacts accurately, and outperforms two other methods in common use. Furthermore, the threshold was calculated based on real data from critically ill patients and the algorithm is easy to implement.