Critical incidents related to opioid infusions in children: a five-year review and analysis.

PURPOSE: Opioids have a narrow therapeutic index and have the potential to cause significant harm. Developmental and pharmacogenetic factors put children, and especially infants, at increased risk of complications. We performed a retrospective root cause analysis to identify the factors associated with critical incidents in children receiving opioid infusions in a tertiary care children's hospital. METHODS: Following institutional ethical approval, we identified potential critical incidents during 2004 to 2009 from patient safety and pharmacy data. Patients' medical charts were reviewed and a timeline of events that occurred before, during, and following each incident was generated. A safety assessment code score was assigned to each incident according to its severity and probability of recurrence, and incidents with a score ≥ 8 were selected for root cause analysis. Root causes were identified and classified, formal causal statements were written, and action plans were recommended. RESULTS: One hundred and sixty-six medical charts were reviewed, and 58 of these included one (45/58) or more (13/58) relevant critical incidents. The resulting harms were of minor to moderate severity. Fourteen incidents were submitted for detailed analysis, from which 31 root causes were identified. The most frequent and significant root causes involved defects in pre-printed order sheets, lack of a nursing guidelines for infusions (rate, adjustment, weaning), and inadequate guidelines for monitoring and recording pain, vital signs, and arousal scores. DISCUSSION: The root causes of a range of critical incidents have been identified, and these have been used to generate recommendations for improving both patient safety and quality of analgesia for children receiving opioid infusions for acute pain management.

An evaluation of an expert system for detecting critical events during anesthesia in a human patient simulator: a prospective randomized controlled study.

BACKGROUND: Perioperative monitoring systems produce a large amount of uninterpreted data, use threshold alarms prone to artifacts, and rely on the clinician to continuously visually track changes in physiological data. To address these deficiencies, we developed an expert system that provides real-time clinical decisions for the identification of critical events. We evaluated the efficacy of the expert system for enhancing critical event detection in a simulated environment. We hypothesized that anesthesiologists would identify critical ventilatory events more rapidly and accurately with the expert system. METHODS: We used a high-fidelity human patient simulator to simulate an operating room environment. Participants managed 4 scenarios (anesthetic vapor overdose, tension pneumothorax, anaphylaxis, and endotracheal tube cuff leak) in random order. In 2 of their 4 scenarios, participants were randomly assigned to the expert system, which provided trend-based alerts and potential differential diagnoses. Time to detection and time to treatment were measured. Workload questionnaires and structured debriefings were completed after each scenario, and a usability questionnaire at the conclusion of the session. Data were analyzed using a mixed-effects linear regression model; Fisher exact test was used for workload scores. RESULTS: Twenty anesthesiology trainees and 15 staff anesthesiologists with a combined median (range) of 36 (29-66) years of age and 6 (1-38) years of anesthesia experience participated. For the endotracheal tube cuff leak, the expert system caused mean reductions of 128 (99% confidence interval [CI], 54-202) seconds in time to detection and 140 (99% CI, 79-200) seconds in time to treatment. In the other 3 scenarios, a best-case decrease of 97 seconds (lower 99% CI) in time to diagnosis for anaphylaxis and a worst-case increase of 63 seconds (upper 99% CI) in time to treatment for anesthetic vapor overdose were found. Participants were highly satisfied with the expert system (median score, 2 on a scale of 1-7). Based on participant debriefings, we identified avoidance of task fixation, reassurance to initiate invasive treatment, and confirmation of a suspected diagnosis as 3 safety-critical areas. CONCLUSION: When using the expert system, clinically important and statistically significant decreases in time to detection and time to treatment were observed for the endotracheal tube cuff Leak scenario. The observed differences in the other 3 scenarios were much smaller and not statistically significant. Further evaluation is required to confirm the clinical utility of real-time expert systems for anesthesia.

Monitoring nociception during general anesthesia with cardiorespiratory coherence.

A novel wavelet transform cardiorespiratory coherence (WTCRC) algorithm has been developed to measure the autonomic state. WTCRC may be used as a nociception index, ranging from 0 (no nociception, strong coherence) to 100 (strong nociception, low coherence). The aim of this study is to estimate the sensitivity of the algorithm to nociception (dental dam insertions) and antinociception (bolus doses of anesthetic drugs). WTCRC's sensitivity is compared to mean heart rate (HRmean) and mean non-invasive blood pressure (NIBPmean), which are commonly used clinical signs. Data were collected from 48 children receiving general anesthesia during dental surgery. The times of dental dam insertion and anesthetic bolus events were noted in real-time during surgeries. 42 dental dam insertion and 57 anesthetic bolus events were analyzed. The change in average WTCRC, HRmean, and NIBPmean was calculated between a baseline period before each event and a response period after. A Wilcoxon rank-sum test was used to compare changes. Dental dam insertion changed the WTCRC nociception index by an average of 14 (82 %) [95 % CI from 7.4 to 19], HRmean by 7.3 beats/min (8.1 %) [5.6-9.6], and NIBPmean by 8.3 mmHg (12 %) [4.9-13]. A bolus dose of anesthetics changed the WTCRC by -15 (-50 %) [-21 to -9.3], HRmean by -4.8 beats/min (4.6 %) [-6.6 to -2.9], and NIBPmean by -2.6 mmHg (3.4 %) [-4.7 to -0.50]. A nociception index based on cardiorespiratory coherence is more sensitive to nociception and antinociception than are HRmean or NIBPmean. The WTCRC algorithm shows promise for noninvasively monitoring nociception during general anesthesia.

Comparison of optical microscopy and optical coherence tomography as quality assurance methods for evaluating lubricious hydrophilic coatings surrounding catheter shafts.

Cardiac catheters are a vital tool in medicine due to their widespread use in many minimally invasive procedures. To aid in advancing the catheter within the patient's vasculature, many catheters are coated with a lubricious hydrophilic coating (HPC). Although HPCs benefit patients, their delamination during use is a serious safety concern. Adverse health effects associated with HPC delamination include pulmonary and myocardial embolism, embolic stroke, infarction, and death. In order to improve patient outcomes, more consistent manufacturing methods and improved quality assurance techniques are needed to evaluate HPC medical devices. The present work investigates the efficacy of two novel methods to image and evaluate HPCs post-manufacturing, relative to industry-standard scanning electron microscopy (SEM)-based methods. We have shown that novel evaluation approaches based on optical microscopy (OM) and optical coherence tomography (OCT) are capable of imaging HPC layers and quantifying HPC thickness, saving hours of time relative to SEM sample preparation and imaging. Additionally, the nondestructive nature of OCT avoids damage and alteration to the HPC prior to imaging, leading to more reliable HPC thickness measurements. Overall, the work demonstrated the feasibility and advantages of using OM and OCT to image and measure HPC thickness relative to industry-standard SEM methods.

Real-time cardiorespiratory coherence is blind to changes in respiration during general anesthesia.

PURPOSE: A novel real-time cardiorespiratory coherence (CRC) algorithm has been developed to monitor nociception during general anesthesia. CRC uses custom designed filters to track and analyze the respiratory sinus arrhythmia (RSA) as it moves in time and frequency. CRC is a form of sensor fusion between heart rate and respiration, estimating the strength of linear coupling between the two signals. The aim of this study was to estimate the effect of changes in respiration rate (RR) and peak airway pressure (PPaw) on CRC. The response of CRC was compared to a prior offline wavelet-based algorithm (WTCRC) as well as traditional univariate heart rate variability (HRV) measures. A nociception index was created for each algorithm, ranging from 0 (no nociception) to 100 (strong nociception). METHODS: Following ethics approval and informed consent, data were collected from 48 children receiving general anesthesia during dental surgery. The times of change in RR and PPaw events were noted in real-time. A total of 43 RR and 35 PPaw change events were analyzed post hoc in pseudo real-time. The nociception index averages were compared between a baseline period and a response period around each event. A Wilcoxon rank-sum test was used to compare changes. RESULTS: The change in RR changed the CRC nociception index by an average of -2.2 [95% CI from -10 to 4.7] (P > 0.3), and the change in PPaw changed the CRC nociception index by an average of 5.4 [-1.0 to 11] (P > 0.1). The changes were smaller than those of many traditional HRV measures. CONCLUSIONS: Real-time CRC was blind to the changes in respiration, and was less sensitive than many of the traditional HRV measures. A nociception index based on CRC can thus function across a wider range of respiratory conditions than can many traditional univariate HRV measures. The real-time CRC algorithm shows promise for monitoring nociception during general anesthesia.

Real-time cardiorespiratory coherence detects antinociception during general anesthesia.

Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel real-time cardiorespiratory coherence (CRC) algorithm has been developed to analyze the strength of linear coupling between heart rate (HR) and respiration. CRC values range from 0 (low coherence, strong nociception) to 1 (high coherence, no nociception). The algorithm uses specially designed filters to operate in real-time, minimizing computational complexity and time delay. In the standard HRV high frequency band of 0.15 - 0.4 Hz, the real-time delay is only 5.25 - 3.25 s. We have assessed the algorithm's response to 60 anesthetic bolus events (a large dose of anesthetics given over a short time; strongly antinociceptive) recorded in 47 pediatric patients receiving general anesthesia. Real-time CRC responded strongly to bolus events, changing by an average of 30%. For comparison, three traditional measures of HRV (LF/HF ratio, SDNN, and RMSSD) responded on average by only 3.8%, 14%, and 3.9%, respectively. Finally, two traditional clinical measures of nociception (HR and blood pressure) responded on average by only 3.9% and 0.91%, respectively. CRC may thus be used as a real-time nociception monitor during general anesthesia.

Wavelet transform cardiorespiratory coherence detects patient movement during general anesthesia.

Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel wavelet transform cardiorespiratory coherence (WTCRC) algorithm has been developed to calculate estimates of the linear coupling between heart rate and respiration. WTCRC values range from 1 (high coherence, no nociception) to 0 (low coherence, strong nociception). We have assessed the algorithm's ability to detect movement events (indicative of patient response to nociception) in 39 pediatric patients receiving general anesthesia. Sixty movement events were recorded during the 39 surgical procedures. Minimum and average WTCRC were calculated in a 30 second window surrounding each movement event. We used a 95% significance level as the threshold for detecting nociception during patient movement. The 95% significance level was calculated relative to a red noise background, using Monte Carlo simulations. It was calculated to be 0.7. Values below this threshold were treated as successful detection. The algorithm was found to detect movement with sensitivity ranging from 95% (minimum WTCRC) to 65% (average WTCRC). The WTCRC algorithm thus shows promise for noninvasively monitoring nociception during general anesthesia, using only heart rate and respiration.