Quantified Ataxic Breathing Can Detect Opioid-Induced Respiratory Depression Earlier in Normal Volunteers Infused with Remifentanil.

BACKGROUND: Ataxic breathing (AB) is a well-known manifestation of opioid effects in animals and humans, but is not routinely included in monitoring for opioid-induced respiratory depression (OIRD). We quantified AB in normal volunteers receiving increasing doses of remifentanil. We used a support vector machine (SVM) learning approach with features derived from a modified Poincaré plot. We tested the hypothesis that AB may be found when bradypnea and reduced mental status are not present. METHODS: Twenty-six healthy volunteers (13 female) received escalating target effect-site concentrations of remifentanil with a low baseline dose of propofol to simulate typical breathing patterns in drowsy patients who had received parenteral opioids. We derived respiratory rate (RR) from respiratory inductance plethysmography, mental alertness from the Modified Observer's Assessment of Alertness/Sedation Scale (MOAA/S), and AB severity on a 0 to 4 scale (categories ranging from none to severe) from the SVM. The primary outcome measure was sensitivity and specificity for AB to detect OIRD. RESULTS: All respiratory measurements were obtained from unperturbed subjects during steady state in 121 assessments with complete data. The sensitivity of AB for detecting OIRD by the conventional method was 92% and specificity was 28%. As expected, 69 (72%) of the instances not diagnosed as OIRD using conventional measures were observed to have at least moderate AB. CONCLUSIONS: AB was frequently present in the absence of traditionally detected OIRD as defined by reduced mental alertness (MOAA/S score of <4) and bradypnea (RR <8 breaths/min). These results justify the need for future trials to explore replicability with other opioids and clinical utility of AB as an add-on measure in recognizing OIRD.

Using a novel in-mask non-invasive ventilator microphone to improve talker intelligibility in healthy and hospitalised adults.

Purpose: Non-invasive ventilation (NIV) provides respiratory support without invasive endotracheal intubation but can hinder patients' ability to communicate effectively. The current study presents preliminary results using a novel in-mask ventilator microphone to enhance talker intelligibility while receiving NIV.Method: A proof-of-concept study assessed sentence intelligibility of five healthy adult talkers using a prototype model of the microphone under continuous positive airway pressure (CPAP; 5/5 cm H2O) and bilevel positive airway pressure (BiPAP; 8/4 cm H2O) ventilator conditions. A pilot study then assessed intelligibility, subjective comprehensibility and naturalness, and patient- and conversation partner-reported communication outcomes for eight patients undergoing therapeutic NIV while being treated in an intensive care unit (ICU).Result: Intelligibility increased significantly with the microphone on in the BiPAP condition for healthy volunteers. For patients undergoing NIV in an ICU, intelligibility, comprehensibility, and patient and conversation partner ratings of conversation satisfaction significantly improved with the microphone on. Patients with lower baselines without the microphone in certain measures (intelligibility, comprehensibility) generally showed a greater microphone benefit than patients with higher baselines.Conclusion: Use of a novel microphone integrated into NIV improved intelligibility during ventilation for both healthy volunteers and patients undergoing therapeutic NIV. Additional clinical studies will define precise benefits and implications of such improved intelligibility.

Tracheal sound-based apnea detection using hidden Markov model in sedated volunteers and post anesthesia care unit patients.

The current method of apnea detection based on tracheal sounds is limited in certain situations. In this work, the Hidden Markov Model (HMM) algorithm based on segmentation is used to classify the respiratory and non-respiratory states of tracheal sounds, to achieve the purpose of apnea detection. Three groups of tracheal sounds were used, including two groups of data collected in the laboratory and a group of patient data in the post anesthesia care unit (PACU). One was used for model training, and the others (laboratory test group and clinical test group) were used for testing and apnea detection. The trained HMMs were used to segment the tracheal sounds in laboratory test data and clinical test data. Apnea was detected according to the segmentation results and respiratory flow rate/pressure which was the reference signal in two test groups. The sensitivity, specificity, and accuracy were calculated. For the laboratory test data, apnea detection sensitivity, specificity, and accuracy were 96.9%, 95.5%, and 95.7%, respectively. For the clinical test data, apnea detection sensitivity, specificity, and accuracy were 83.1%, 99.0% and 98.6%. Apnea detection based on tracheal sound using HMM is accurate and reliable for sedated volunteers and patients in PACU.

Response surface model comparison and combinations for remifentanil and propofol in describing response to esophageal instrumentation and adverse respiratory events.

BACKGROUND: The primary aim of this essay was to explore the best fitting model in remifentanil-propofol combined administrations during esophageal instrumentation (EI) from five distinct response surface models. The secondary aim was to combine the models to give appropriate effect-site drug concentrations (Ces) range with maximal comfort and safety. METHODS: The Greco, reduced Greco, Minto, Scaled C50 Hierarchy and Fixed C50 Hierarchy models were constructed to fit four drug effects: loss of response to esophageal instrumentation (LREI), loss of response to esophageal instrumentation revised (LREIR), intolerable ventilatory depression (IVD) and respiratory compromise (RC). Models were tested by chi-square statistical test and evaluated with Akaike Information Criterion (AIC). Model prediction performance were measured by successful prediction rate (SPR) and three prediction errors. RESULTS: The reduced Greco model was the best fitting model for LREI and RC, and the Minto model was the best fitting model for LREIR and IVD. The SPRs of reduced Greco model for LREI and RC were 81.76% and 79.81%. The SPRs of Minto model for LREIR and IVD were 80.32% and 80.12%. Overlay of the reduced Greco model for LREI and Minto model for IVD offered visual aid for guidance in drug administration. CONCLUSION: Using proper response surface model to fit different drug effects will describe the interactions between anesthetic drugs better. Combining response surface models to select the more reliable effect-site drug concentrations range can be used to guide clinical drug administration with greater safety and provide an improvement of anesthesia precision.

The Analysis of How Apnea Influences the Autonomic Nervous System Using Short-Term Heart Rate Variability Indices.

Objective: Frequent cessations of respiration can greatly increase the prevalence rate of arrhythmia. It has been confirmed that cardiac modulation is regulated by the autonomic nervous system (ANS). And heart rate variability (HRV) is widely used as a method to evaluate the function of the ANS. Therefore, we analyzed whether apnea can affect the balance and normal function of the ANS using short-term HRV indices. Methods: Forty-five healthy subjects were asked to breathe normally and hold their breathing to simulate 10 times apnea. Thirty-six patients from the dataset of a sleep laboratory for the diagnosis of sleep disorders with 10 times apnea were included in analysis. We calculated short-term HRV indices of subjects in normal respiratory and apneic states, respectively. Results: Compared with the normal respiratory state, respiration cease would lead to the values of Mean-RR, nLF, LF/HF, and α1 which significantly increased, whereas the values of rMSSD and nHF significantly decreased. Conclusions: Cessations of respiration would lead to an imbalance in the function of the ANS, as well as an increase in fractal characteristics of the heart. These changes in the physiological state are likely to induce and cause the occurrence of arrhythmia, which is regulated by the ANS.

An Automated Algorithm Incorporating Poincaré Analysis Can Quantify the Severity of Opioid-Induced Ataxic Breathing.

BACKGROUND: Opioid-induced respiratory depression (OIRD) is traditionally recognized by assessment of respiratory rate, arterial oxygen saturation, end-tidal CO2, and mental status. Although an irregular or ataxic breathing pattern is widely recognized as a manifestation of opioid effects, there is no standardized method for assessing ataxic breathing severity. The purpose of this study was to explore using a machine-learning algorithm for quantifying the severity of opioid-induced ataxic breathing. We hypothesized that domain experts would have high interrater agreement with each other and that a machine-learning algorithm would have high interrater agreement with the domain experts for ataxic breathing severity assessment. METHODS: We administered target-controlled infusions of propofol and remifentanil to 26 healthy volunteers to simulate light sleep and OIRD. Respiration data were collected from respiratory inductance plethysmography (RIP) bands and an intranasal pressure transducer. Three domain experts quantified the severity of ataxic breathing in accordance with a visual scoring template. The Krippendorff alpha, which reports the extent of interrater agreement among N raters, was used to assess agreement among the 3 domain experts. A multiclass support vector machine (SVM) was trained on a subset of the domain expert-labeled data and then used to quantify ataxic breathing severity on the remaining data. The Vanbelle kappa was used to assess the interrater agreement of the machine-learning algorithm with the grouped domain experts. The Vanbelle kappa expands on the Krippendorff alpha by isolating a single rater-in this case, the machine-learning algorithm-and comparing it to a group of raters. Acceptance criteria for both statistical measures were set at >0.8. The SVM was trained and tested using 2 sensor inputs for the breath marks: RIP and intranasal pressure. RESULTS: Krippendorff alpha was 0.93 (95% confidence interval [CI], 0.91-0.95) for the 3 domain experts. Vanbelle kappa was 0.98 (95% CI, 0.96-0.99) for the RIP SVM and 0.96 (0.92-0.98) for the intranasal pressure SVM compared to the domain experts. CONCLUSIONS: We concluded it may be feasible for a machine-learning algorithm to quantify ataxic breathing severity in a manner consistent with a panel of domain experts. This methodology may be helpful in conjunction with traditional measures to identify patients experiencing OIRD.

Tracheal sounds accurately detect apnea in patients recovering from anesthesia.

Apnea should be monitored continuously in the post anesthesia care unit (PACU) to avoid serious complications. It has been confirmed that tracheal sounds can be used to detect apnea during sedation in healthy subjects, but the performance of this acoustic method has not been evaluated in patients with frequent apnea events in the PACU. Tracheal sounds were acquired from the patients in the PACU using a microphone encased in a plastic bell. Concurrently, a processed nasal pressure signal was used as a reference standard to identify real respiratory events. The logarithm of the tracheal sound variance (log-var) was used to detect apnea, and the results were compared to the reference method. Sensitivity, specificity, positive likelihood ratios (PLR), and negative likelihood ratios (NLR) were calculated. One hundred and twenty-one patients aged 55.5 ± 13.2 years (mean ± SD) with a body mass index of 24.6 ± 3.7 kg/m2 were included in data analysis. The total monitoring time was 52.6 h. Thirty-four patients experienced 236 events of apnea lasting for a total of 122.2 min. The log-var apnea detection algorithm detected apnea with 92% sensitivity, 98% specificity, 46 PLR and 0.08 NLR. The performance of apnea detection in the PACU using the log-var tracheal sounds method proved to be reliable and accurate. Tracheal sounds could be used to minimize the potential risks from apnea in PACU patients.

Faster clinical response to the onset of adverse events: A wearable metacognitive attention aid for nurse triage of clinical alarms.

OBJECTIVE: This study evaluates the potential for improving patient safety by introducing a metacognitive attention aid that enables clinicians to more easily access and use existing alarm/alert information. It is hypothesized that this introduction will enable clinicians to easily triage alarm/alert events and quickly recognize emergent opportunities to adapt care delivery. The resulting faster response to clinically important alarms/alerts has the potential to prevent adverse events and reduce healthcare costs. MATERIALS AND METHODS: A randomized within-subjects single-factor clinical experiment was conducted in a high-fidelity 20-bed simulated acute care hospital unit. Sixteen registered nurses, four at a time, cared for five simulated patients each. A two-part highly realistic clinical scenario was used that included representative: tasking; information; and alarms/alerts. The treatment condition introduced an integrated wearable attention aid that leveraged metacognition methods from proven military systems. The primary metric was time for nurses to respond to important alarms/alerts. RESULTS: Use of the wearable attention aid resulted in a median relative within-subject improvement for individual nurses of 118% (W = 183, p = 0.006). The top quarter of relative improvement was 3,303% faster (mean; 17.76 minutes reduced to 1.33). For all unit sessions, there was an overall 148% median faster response time to important alarms (8.12 minutes reduced to 3.27; U = 2.401, p = 0.016), with 153% median improvement in consistency across nurses (F = 11.670, p = 0.001). DISCUSSION AND CONCLUSION: Existing device-centric alarm/alert notification solutions can require too much time and effort for nurses to access and understand. As a result, nurses may ignore alarms/alerts as they focus on other important work. There has been extensive research on reducing alarm frequency in healthcare. However, alarm safety remains a top problem. Empirical observations reported here highlight the potential of improving patient safety by supporting the meta-work of checking alarms.

Using the entropy of tracheal sounds to detect apnea during sedation in healthy nonobese volunteers.

BACKGROUND: Undetected apnea can lead to severe hypoxia, bradycardia, and cardiac arrest. Tracheal sounds entropy has been proved to be a robust method for estimating respiratory flow, thus maybe a more reliable way to detect obstructive and central apnea during sedation. METHODS: A secondary analysis of a previous pharmacodynamics study was conducted. Twenty volunteers received propofol and remifentinal until they became unresponsive to the insertion of a bougie into the esophagus. Respiratory flow rate and tracheal sounds were recorded using a pneumotachometer and a microphone. The logarithm of the tracheal sound Shannon entropy (Log-E) was calculated to estimate flow rate. An adaptive Log-E threshold was used to distinguish between the presence of normal breath and apnea. Apnea detected from tracheal sounds was compared to the apnea detected from respiratory flow rate. RESULTS: The volunteers stopped breathing for 15 s or longer (apnea) 322 times during the 12.9-h study. Apnea was correctly detected 310 times from both the tracheal sounds and the respiratory flow. Periods of apnea were not detected by the tracheal sounds 12 times. The absence of tracheal sounds was falsely detected as apnea 89 times. Normal breathing was detected correctly 1,196 times. The acoustic method detected obstructive and central apnea in sedated volunteers with 95% sensitivity and 92% specificity. CONCLUSIONS: We found that the entropy of the acoustic signal from a microphone placed over the trachea may reliably provide an early warning of the onset of obstructive and central apnea in volunteers under sedation.

Anatomic dead space cannot be predicted by body weight.

BACKGROUND: Anatomic dead space (also called airway or tracheal dead space) is the part of the tidal volume that does not participate in gas exchange. Some contemporary ventilation protocols, such as the Acute Respiratory Distress Syndrome Network protocol, call for smaller tidal volumes than were traditionally delivered. With smaller tidal volumes, the percentage of each delivered breath that is wasted in the anatomic dead space is greater than it is with larger tidal volumes. Many respiratory and medical textbooks state that anatomic dead space can be estimated from the patient's weight by assuming there is approximately 1 mL of dead space for every pound of body weight. With a volumetric capnography monitor that measures on-airway flow and CO2, the anatomic dead space can be automatically and directly measured with the Fowler method, in which dead space equals the exhaled volume up to the point when CO2 rises above a threshold. METHODS: We analyzed data from 58 patients (43 male, 15 female) to assess the accuracy of 5 anatomic dead space estimation methods. Anatomic dead space was measured during the first 10 min of monitoring and compared to the estimates. RESULTS: The coefficient of determination (r2) between the anatomic dead space estimate based on body weight and the measured anatomic dead space was r2 = 0.0002. The mean +/- SD error between the body weight estimate and the measured dead space was 60 +/- 54 mL. CONCLUSIONS: It appears that the anatomic dead space estimate methods were sufficient when used (as originally intended) together with other assumptions to identify a starting point in a ventilation algorithm, but the poor agreement between an individual patient's measured and estimated anatomic dead space contradicts the assumption that dead space can be predicted from actual or ideal weight alone.

Measurement of functional residual capacity of the lung by partial CO2 rebreathing method during acute lung injury in animals.

BACKGROUND: Several techniques for measuring the functional residual capacity (FRC) of the lungs in mechanically ventilated patients have been proposed, each of which is based on either nitrogen wash-out or dilution of tracer gases. These methods are expensive, difficult, time-consuming, impractical, or require an intolerably large change in the fraction of inspired oxygen. We propose a CO(2) wash-in method that allows automatic and continual FRC measurement in mechanically ventilated patients. METHODS: We measured FRC with a CO(2) partial rebreathing technique, first in a mechanical lung analog, and then in mechanically ventilated animals, before, during, and subsequent to an acute lung injury induced with oleic acid. We compared FRC measurements from partial CO(2) rebreathing to measurements from a nitrogen wash-out reference method. Using an approved animal protocol, general anesthesia was induced and maintained with propofol in 6 swine (38.8-50.8 kg). A partial CO(2) rebreathing monitor was placed in the breathing circuit between the endotracheal tube and the Y-piece. The partial CO(2) rebreathing signal obtained from the monitor was used to calculate FRC. FRC was also measured with a nitrogen wash-out measurement technique. In the animal studies we collected data from healthy lungs, and then subsequent to a lung injury that simulated the conditions of acute lung injury/acute respiratory distress syndrome. The injury was created by intravenously infusing 0.09 mL/kg of oleic acid over a 15-min period. At each stage of the experiment, the positive end-expiratory pressure (PEEP) was set to 0, 5, 10, or 15 cm H(2)O. At each PEEP level we compared the average of 3 CO(2) rebreathing FRC measurements to the average of 3 nitrogen wash-out reference measurements. We also tested the FRC measurement system with a mechanical test lung in which the true FRC could be directly measured. RESULTS: The squared correlation for the linear regression between CO(2) rebreathing and nitrogen wash-out measurements in the animals was r(2) = 0.89 (n = 50). The average error of the CO(2) wash-out system was -87 mL and the limits of agreement were+/- 263 mL. In the mechanical test lung, the average error of the FRC measured via the CO(2) wash-in system was 37 mL, and the limits of agreement were +/- 103 mL, which was equivalent to 1.7% of the true FRC. The squared correlation was r(2) = 0.96. CONCLUSION: These results indicate that FRC measurement via CO(2) rebreathing can reliably detect an FRC decrease during lung injury and can reflect the response of the FRC to treatment with PEEP.