Long-term Effectiveness of the Airway Registry at Sydney Helicopter Emergency Medical Service

OBJECTIVE: Prehospital rapid sequence intubation (RSI) is prone to suboptimal documentation. The Greater Sydney Area Helicopter Emergency Medical Service (GSA-HEMS) uses a dedicated Airway Registry (AR) to aid documentation. The AR was only evaluated shortly after its introduction. This first evaluation is followed up to assess the long-term effectiveness of the AR. The secondary objective was to compare the AR with templates in the literature. METHODS: A retrospective review of electronic records was undertaken to compare completeness of documentation between an immediate postintroduction and a long-term postintroduction cohort. Differences between the two cohorts were tested for significance. RESULTS: There was no significant difference in documentation for Cormack-Lehane laryngoscopy grade at the first intubation attempt (P = .552) and confirmation of end-tidal carbon dioxide (P = .258). A significant improvement in the documentation of laryngoscopy grade for the second attempt (P = 0) was found. The documentation of intubator details remained at 100% (165/165). The variables collected by GSA-HEMS corresponded well to the literature, but some definitions differ (eg, desaturation). CONCLUSION: There was no significant change in completeness of documentation for most key intubation variables eight years after the introduction of the AR. GSA-HEMS performs well in registering variables as proposed in the literature; however, variable definitions need to be synchronized

Decision support increases guideline adherence for prescribing postoperative nausea and vomiting prophylaxis

BACKGROUND: Guidelines for postoperative nausea and vomiting (PONV) prevention are implemented widely but their effectiveness may be limited by poor adherence. We hypothesized that the use of an electronic decision support (DS) system would significantly improve guideline adherence. METHODS: Medical information of all patients undergoing elective surgery in our regional teaching hospital is routinely entered in an anesthesia information management system at the preoperative screening clinic. Our departmental PONV prevention guidelines identifies patients as "high-risk" and thus eligible for PONV prophylaxis based on the presence of at least three of the following risk factors: female gender, history of PONV or motion sickness, nonsmoker status, and anticipated use of postoperative opioids. Using automated reminders, we studied the effect of DS on guidelines adherence using an off-on-off design. In these three study periods, we queried for all consecutive patients visiting the preoperative screening clinic who were eligible for PONV prophylaxis and studied how often it was prescribed correctly. RESULTS: Between November 2005 and June 2006,1340,2715, and 1035 patients were included in the control, DS and post-DS periods, respectively. As a result of mandatory data entry of risk factors, the percentage of high-risk PONV patients increased from 28% in the control period to 32% and 31% in the DS and post-DS periods, respectively. During the control period, 38% of all high-risk patients were prescribed PONV prophylaxis. This increased to 73% during the DS period and decreased to 37% in the post-DS period. CONCLUSION: Electronic DS increases guidelines adherence for the prescription of PONV prophylaxis in high-risk PONV patient

Automated reminders increase adherence to guidelines for administration of prophylaxis for postoperative nausea and vomiting

Background and objective Correct identification of patients at high risk for postoperative nausea and vomiting (PONV), prescription of PONV prophylaxis and correct administration of medication are all important for effective PONV prophylaxis. This has been acknowledged by development of guidelines throughout the world. We studied the effect of introducing patient-specific automated reminders on timely administration of PONV prophylaxis medication during general anaesthesia. Methods During the visit to the preoperative screening clinic, patients at high risk for PONV were identified and PONV prophylaxis was prescribed. To study the effect of patient-specific decision support [a pop-up window reminding the (nurse) anaesthetist that PONV prophylaxis had been prescribed for this particular patient] on the timely administration of PONV medication, we queried our database to extract data on all patients for three consecutive periods: 6 weeks before decision support (control), 12 weeks during decision support and 6 weeks after discontinuation of decision support (postdecision support) and studied how often PONV prophylaxis was administered correctly. Results Between November 2005 and May 2006, 1727, 2594 and 1331 patients presented for elective surgery in the control, decision support and postdecision support periods, respectively. In the control period, 236 patients receiving general anaesthesia were scheduled to receive PONV prophylaxis. Of these, 93 (39%) received both dexamethasone and granisetron in the correct timeframe. This increased to 464 (79%) out of 591 patients in the decision support period and decreased back to 99 (41%) out of 243 patients in the postdecision support period (P <0.001). Conclusion Decision support is effective in improving administration and timing of PONV prophylaxis medication. After withdrawal of decision support, adherence decreased to predecision support levels. Eur J Anaesthesiol 27:187 191 (c) 2010 European Society of Anaesthesiolog

A Probabilistic Framework for Joint Pedestrian Head and Body Orientation Estimation

Abstract-We present a probabilistic framework for the joint estimation of pedestrian head and body orientation from a mobile stereo vision platform. For both head and body parts, we convert the responses of a set of orientation-specific detectors into a (continuous) probability density function. The parts are localized by means of a pictorial structure approach, which balances part-based detector responses with spatial constraints. Head and body orientations are estimated jointly to account for anatomical constraints. The joint single-frame orientation estimates are integrated over time by particle filtering. The experiments involved data from a vehicle-mounted stereo vision camera in a realistic traffic setting; 65 pedestrian tracks were supplied by a state-of-the-art pedestrian tracker. We show that the proposed joint probabilistic orientation estimation framework reduces the mean absolute head and body orientation error up to 15 • compared with simpler methods. This results in a mean absolute head/body orientation error of about 21 • /19 • , which remains fairly constant up to a distance of 25 m. Our system currently runs in near real time (8-9 Hz)

A pre-hospital risk score predicts critical illness in non-trauma patients transported by ambulance to a Dutch tertiary referral hospital

Background: Early pre-hospital identification of critically ill patients reduces morbidity and mortality. To identify critically ill non-traumatic and non-cardiac arrest patients, a pre-hospital risk stratification tool was previously developed in the United States. The aim of this study was to investigate the accuracy of this tool in a Dutch Emergency Department. Methods: This retrospective study included all patients of 18 years and older transported by ambulance to the Emergency Department of a tertiary referral hospital between January 1st 2017 and December 31st 2017. Documentation of pre-hospital vital parameters had to be available. The tool included a full set of vital parameters, which were categorized by predetermined thresholds. Study outcome was the accuracy of the tool in predicting critical illness, defined as admittance to the Intensive Care Unit for delivery of vital organ support or death within 28 days. Accuracy of the risk stratification tool was measured with the Area Under the Receiver Operating Characteristics (AUROC) curve. Results: Nearly 3000 patients were included in the study, of whom 356 patients (12.2%) developed critical illness. We observed moderate discrimination of the pre-hospital risk score with an AUROC of 0.74 (95%-CI 0.71–0.77). Using a threshold of 3 to identify critical illness, we observed a sensitivity of 45.0% (95%-CI 44.8–45.2) and a specificity of 86.0% (95%-CI 85.9–86.0). Conclusion: These data show that this pre-hospital risk stratification tool is a moderately effective tool to predict which patients are likely to become critically ill in a Dutch non-trauma and non-cardiac arrest population

A pre-hospital risk score predicts critical illness in non-trauma patients transported by ambulance to a Dutch tertiary referral hospital

Background: Early pre-hospital identification of critically ill patients reduces morbidity and mortality. To identify critically ill non-traumatic and non-cardiac arrest patients, a pre-hospital risk stratification tool was previously developed in the United States. The aim of this study was to investigate the accuracy of this tool in a Dutch Emergency Department. Methods: This retrospective study included all patients of 18 years and older transported by ambulance to the Emergency Department of a tertiary referral hospital between January 1st 2017 and December 31st 2017. Documentation of pre-hospital vital parameters had to be available. The tool included a full set of vital parameters, which were categorized by predetermined thresholds. Study outcome was the accuracy of the tool in predicting critical illness, defined as admittance to the Intensive Care Unit for delivery of vital organ support or death within 28 days. Accuracy of the risk stratification tool was measured with the Area Under the Receiver Operating Characteristics (AUROC) curve. Results: Nearly 3000 patients were included in the study, of whom 356 patients (12.2%) developed critical illness. We observed moderate discrimination of the pre-hospital risk score with an AUROC of 0.74 (95%-CI 0.71–0.77). Using a threshold of 3 to identify critical illness, we observed a sensitivity of 45.0% (95%-CI 44.8–45.2) and a specificity of 86.0% (95%-CI 85.9–86.0). Conclusion: These data show that this pre-hospital risk stratification tool is a moderately effective tool to predict which patients are likely to become critically ill in a Dutch non-trauma and non-cardiac arrest population

A shocking injury: A clinical review of lightning injuries highlighting pitfalls and a treatment protocol

Introduction: Lightning strikes have high morbidity and mortality rates. Thousands of fatalities are estimated to be caused by lightning worldwide, with the number of injuries being 10 times greater. However, evidence of lightning injuries is restricted to case reports and series and nonsystematic reviews. In this clinical review, we systematically select, score, and present evidence regarding lightning injuries. Material and methods: We performed a systematic search for reviews and guidelines in the PubMed, Embase (OvidSP), MEDLINE (OvidSP), and Web of Science databases. All publications were scored according to the Levels of Evidence 2 Table of the Oxford center for Evidence-Based Medicine. The reviews were also scored using the scale for the quality assessment of narrative review articles (SANRA) and guidelines from the Appraisal of Guidelines for Research & Evaluation (AGREE II). Results: The search yielded 536 articles. Eventually, 56 articles were included, which consisted of 50 reviews, five guidelines and one overview. The available reviews and guidelines were graded as low to moderate evidence. Most damage from lightning injuries is cardiovascular and neurological, although an individual can experience complications with any of their vital functions. At the scene, initial treatment and resuscitation should focus on those who appear to be dead, which is called the reverse triage system. We proposed an evidence-based treatment protocol for lightning strike patients. Conclusion: It is vital that every lightning strike patient is treated according to standard trauma guidelines, with a specific focus on the possible sequelae of lighting injuries. All emergency healthcare professionals should acknowledge the risks and particularities of treating lighting strike injuries to optimize the care and outcomes of these patients. Our evidence-based treatment protocol should help prehospital and in-hospital emergency healthcare practitioners to prevent therapeutic mismanagement among these patients

The effect of requesting a reason for non-adherence to a guideline in a long running automated reminder system for PONV prophylaxis

Automated reminders are employed frequently to improve guideline adherence, but limitations of automated reminders are becoming more apparent. We studied the reasons for non-adherence in the setting of automated reminders to test the hypothesis that a separate request for a reason in itself may further improve guideline adherence. In a previously implemented automated reminder system on prophylaxis for postoperative nausea and vomiting (PONV), we included additional automated reminders requesting a reason for non-adherence. We recorded these reasons in the pre-operative screening clinic, the OR and the PACU. We compared adherence to our PONV guideline in two study groups with a historical control group. Guideline adherence on prescribing and administering PONV prophylaxis (dexamethasone and granisetron) all improved compared to the historical control group (89 vs. 82% (p <0.0001), 96 vs 95% (not significant) and 90 vs 82% (p <0.0001)) while decreasing unwarranted prescription for PONV prophylaxis (10 vs. 13 %). In the pre-operative screening clinic, the main reason for not prescribing PONV prophylaxis was disagreement with the risk estimate by the decision support system. In the OR/PACU, the main reasons for not administering PONV prophylaxis were: 'unintended non-adherence' and 'failure to document'. In this study requesting a reason for non-adherence is associated with improved guideline adherence. The effect seems to depend on the underlying reason for non-adherence. It also illustrates the importance of human factors principles in the design of decision support. Some reasons for non-adherence may not be influenced by automated reminder