Adverse events during anaesthesia at an Ethiopian referral hospital : a prospective observational study

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Study of brain injury and neuroregenerative effects of D-cycloserine using an asphyxial cardiac arrest rat model

Thesis (M.S.) University of Alaska Fairbanks, 2013Cardiac arrest (CA) affects over 300,000 Americans annually and results in severe brain injury (impaired motility, memory loss, or death) due to poor recovery. Over stimulation of glutamatergic N-methyl-D-aspartate receptor (NMDAR) channel activation, allows Ca² ions to enter cells, triggers a cascade of excitotoxic events and eventual neuronal death, but down-regulation of NMDAR post arrest may contribute to progressive injury. Previous studies have indicated that NMDARs are down-regulated within hours to days after resuscitation, therefore re-stimulation of NMDARs after CA should improve neurological outcome. The purpose of this thesis was to: 1.) Implement an in vivo asphyxial CA (ACA) rat model at UAF to reproduce CA seen clinically in prenatal/pediatric populations, and 2.) Test the hypothesis that partial NMDAR agonist (D-cycloserine, DCS) would improve recovery from neuronal injury. Male Sprague Dawley rats (250-330g) were administered a low dose of DCS (10mg/kg, IP) 24 and 48hr after resuscitation from either 6 or 8-min ACA. Behavioral Neurological Deficit Scores (NDS) were taken at 2hr and daily for 7 days after ACA to assess injury. Histopathology assessed CA1 hippocampal neuronal injury. DCS had no effect on neurological improvement but the ACA model produced significant brain injury in rats regardless of CA duration.Chapter 1. General introduction to excitotoxic effects resulting from cerebral ischemia -- Chapter 2. Health implications and technique assessment of an asphyxial cardiac arrest (ACA) rat model transferred to the University of Alaska, Fairbanks (UAF) from the University of Miami School of Medicine -- Chapter 3. D-Cycloserine (DCS) activiation of N-methyl-D-aspartate receptors (NMDAR) 24 & 48hr after asphyxial cardiac arrest (ACA) has no effect on hippocampal CA1 neurological deficits -- Chapter 4. General conclusion -- Appendices

Airway Management in ICU Settings

Maintenance of patent airway, adequate ventilation, and pulmonary gas exchange is very important in critically ill patients. Airway management in intensive care patients differs significantly from routine surgical procedures in the operating room. The airway competence in intensive care unit (ICU) should be coping with the rapidly evolving advances in airway management. Therefore, efforts should be focused on the three pillars of airway master: airway providers as intensivists or critical care physicians, equipment, and operational plans. Not all institutions can afford all airway equipment in the market; however, they should make sure that critical care providers have a full access to the available tools and they are comfortable using it. Educational sessions and refresher courses should be tailored to meet the competence level of the ICU providers and equipment availability. Operational plan includes developing institutional airway protocols and implementing difficult airway guidelines. The protocols should consider different staffing models of ICU and make sure all the time at least one member of the team with the highest experience in airway should be always available. The aim of writing this chapter is to enable the intensivist to optimize their use of airway equipment and managing high‐risk patients in ICU

Observational Data to Improve Clinical Decision Making in Acute Care

The premise of Big Data in acute medicine is to make medicine more efficient and effective. However, the translation of large observational data to knowledge is difficult. This thesis explores and discusses the three main types of research questions which can be asked from large observational data:1. What is current clinical practice?2. What is best practice?3. What patients need to be prioritised?This thesis will focus on traumatic brain injury and in-hospital cardiac arrest.<br/

Certified Registered Nurse Anesthetist Performance and Perceptions: Use of a Handheld, Computerized, Decision Making Aid During Critical Events in a High-fidelity Human Simulation Environment

With the increasing focus on patient safety and human error, understanding how practitioners make decisions during critical incidents is important. Despite the move towards evidence-based practice, research shows that much decision making is based on intuition and heuristics (“rules of thumb”). The purpose of this study was to examine and evaluate the methodologic feasibility of a strategy for comparing traditional cognition versus the use of algorithms programmed on a personal digital assistant (FDA) in the management of unanticipated critical events by certified registered nurse anesthetists (CRNAs). A combined qualitative-quantitative methodology was utilized. The quantitative element consists of a pilot study using a cross-over trial design. Two case scenarios were carried out in a full-scale, high fidelity, simulated anesthesia care delivery environment. Four subjects participated in both scenarios, one without and one with a PDA containing a catalog of approximately 30 events with diagnostic and treatment related information in second scenario. Audio—videotaping of the scenarios allowed for definitive descriptive analysis of items of interest, including time to correct diagnosis and definitive intervention. The qualitative approach consisted of a phenomenological investigation of problem solving and perceptions of FDA use and the simulation experience by the participants using “think aloud” and retrospective verbal reports, semi-structured group interviews, and written evaluations. Qualitative results revealed that participants found the PDA algorithms useful despite some minor technical difficulties and the simulated environment and case scenarios realistic, but also described feelings of expectation, anxiety, and pressure. Problem solving occurred in a hypothetico-deductive manner. More hypotheses were considered when using the PDA. Time to correct diagnosis and treatment varied by scenario, taking less time with the PDA for one but taking longer with the PDA for the other, likely due to differences in pace and intensity of the two scenarios. The methodologic investigation revealed several areas for improvement including more precise control of case scenarios. All participants agreed with the value of using high fidelity simulation, particularly for problem solving of critical events, and provided useful information for more effective utilization of this tool for education and research

비침습적 뇌파 신호를 이용한 응급환자의 생체반응 모니터링 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2021. 2. 김희찬.뇌파는 대뇌피질이나 두피의 전극을 통해서 뇌의 전기적 신호를 기록한 것을 의미한다. 뇌 기능 관찰을 위한 진단도구로써 뇌파는 뇌전증이나 치매 진단 등의 목적으로 활용되고 있다. 본 논문에서는 비침습적 뇌파를 이용하여 응급환자의 주요 생리학적 지표를 모니터링하는 기술을 개발하였다. 처음 두 연구에서 심폐소생술의 효과를 평가하기 위한 심정지 돼지실험모델을 고안하였다. 현재의 심폐소생술 지침은 체순환 평가를 위해 기도삽관을 통한 호기말 이산화탄소 분압의 측정을 권고한다. 하지만, 정확한 기도삽관이 특히 병원 밖 상황에서 어려울 수 있다. 따라서, 간편히 측정할 수 있고 소생 환자의 신경학적 예후를 진단하는데 사용되는 뇌파를 이용한 예측 기술이 심폐소생술 품질평가지표의 대안으로 제안되었다. 첫 번째 실험에서는 고품질과 저품질 기본심폐소생술을 10회 반복하면서 측정된 뇌파를 분석하였다. 심폐소생술의 품질에 따른 뇌파의 변화를 이용하여 체순환 평가를 위한 EEG-based Brain Resuscitation Index (EBRI) 모델을 도출하였다. EBRI 모델에서 획득한 호기말 이산화탄소 분압 예측치는 실제 값과 양의 상관관계를 보이며, 병원 밖 상황에서의 활용 가능성을 보였다. 두 번째 실험에서는 두 가지 심폐소생술(기본심폐소생술, 전문심폐소생술)이 수행되었다. 제세동 직전에 수집된 뇌파는 심폐소생술 도중 경동맥혈류의 회복률과 함께 분석되었다. 심폐소생술 도중 경동맥혈류의 회복률을 반영하는 뇌파 변수를 규명한 후, 이를 이용하여 높은 회복률(30% 이상)과 낮은 회복률(30% 미만)을 구분하는 기계학습 기반 이진분류모델을 도출하였다. 서포트 벡터 머신 기반의 예측모델이 0.853의 정확도와 0.909의 곡선하면적을 보이며 가장 우수한 성능을 보였다. 이러한 예측모델은 심정지 환자의 뇌 소생을 향상시켜 빠른 뇌 기능 회복을 가능하게 할 것으로 기대된다. 세 번째 연구에서 비침습적 뇌파를 이용하여 두개내압을 예측하는 모델을 개발하기 위한 외상성 뇌손상 돼지실험모델이 고안되었다. 외상성 뇌손상은 물리적 충격에 의해 정상적인 뇌 기능이 중단된 상태를 의미하며, 이 때의 두개내압 상승과 관류저하가 뇌파에 영향을 끼칠 수 있다. 따라서, 우리는 뇌파 기반 두개내압 예측모델을 개발하였다. 폴리카테터로 실험동물의 두개내압을 변경하면서 뇌파를 획득하였다. 두개내압의 정상구간(25 mmHg 미만)과 위험구간(25 mmHg 이상)을 유의미하게 구분하는 뇌파 변수를 규명한 후 기계학습 기반 이진분류모델을 도출하였다. 다층 퍼셉트론 기반의 예측모델이 0.686의 정확도와 0.754의 곡선하면적을 보이며 가장 우수한 성능을 보였다. 또다른 비침습 데이터인 심박수 정보와 함께 사용하였을 때 정확도와 곡선하면적은 각각 0.760과 0.834로 향상되었다. 제안된 예측모델은 응급상황에서 비침습적으로 두개내압을 관찰하여 정상 수준의 두개내압을 유지하는데 도움을 줄 것으로 기대된다. 본 논문은 응급환자의 주요 생리학적 지표를 비침습적 뇌파를 이용하여 관찰하는 예측모델을 제안하고 성능을 검증하였다. 본 연구에서는 뇌파를 이용하여 즉각적인 호기말 이산화탄소 분압, 경동맥혈류, 두개내압을 추정하기 위한 예측모델을 수립하였다. 하지만, 뇌파 데이터는 장기간의 신경학적, 기능적 회복과 함께 평가되어야 한다. 본 논문에서 개발한 예측모델의 성능과 적용 가능성은 향후 다양한 임상연구를 통해 cerebral performance category와 modified Rankin scale 등의 신경학적 평가지표와 함께 분석, 개선되어야 할 것이다.Electroencephalogram (EEG) is a recording of the electrical activity of the brain, measured using electrodes attached to the cerebrum cortex or the scalp. As a diagnostic tool for brain disorders, EEG has been widely used for clinical purposes such as epilepsy- and dementia diagnosis. This study develops an EEG-based noninvasive critical care monitoring method for emergency patients. In the first two studies, ventricular fibrillation swine models were designed to develop EEG-based monitoring methods for evaluating the effectiveness of cardiopulmonary resuscitation (CPR). The CPR guidelines recommend measuring end-tidal carbon dioxide (ETCO2) via endotracheal intubation to assess systemic circulation. However, accurate insertion of the endotracheal tube might be difficult in an out-of-hospital setting (OOHS). Therefore, an easily measurable EEG, which has been used to predict resuscitated patients neurologic prognosis, was suggested as a surrogate indicator for CPR feedback. In the first experimental setup, the high- and low quality CPRs were altered 10 times repeatedly, and the EEG parameters were analyzed. Linear regression of an EEG-based brain resuscitation index (EBRI) was used to estimate ETCO2 levels as a novel feedback indicator of systemic circulation during CPR. A positive correlation was found between the EBRI and the real ETCO2, which indicates the feasibility of EBRI in OOHSs. In the second experimental setup, two types of CPR mode were performed: basic life support and advanced cardiovascular life support. EEG signals that were measured between chest compressions and defibrillation shocks were analyzed to monitor the cerebral circulation with respect to the recovery of carotid blood flow (CaBF) during CPR. Significant EEG parameters were identified to represent the CaBF recovery, and machine learning (ML)-based classification models were established to differentiate between the higher (≥ 30%) and lower (< 30%) CaBF recovery. The prediction model based on the support vector machine (SVM) showed the best performance, with an accuracy of 0.853 and an area under the curve (AUC) of 0.909. The proposed models are expected to guide better cerebral resuscitation and enable early recovery of brain function. In the third study, a swine model of traumatic brain injury (TBI) was designed to develop an EEG-based prediction model of an elevated intracranial pressure (ICP). TBI is defined as the disruption of normal brain function due to physical impact. This can increase ICP, and the resulting hypoperfusion can affect the cerebral electrical activity. Thus, we developed EEG-based prediction models to monitor ICP levels. During the experiments, EEG was measured while the ICP was adjusted with the Foley balloon catheter. Significant EEG parameters were determined to differentiate between the normal (< 25 mmHg) and dangerous (≥ 25 mmHg) ICP levels and ML-based binary classifiers were established to distinguish between these two groups. The multilayer perceptron model showed the best performance with an accuracy of 0.686 and an AUC of 0.754, which were improved to 0.760 and 0.834, respectively, when a noninvasive heart rate was also used as an input. The proposed prediction models are expected to instantly treat an elevated ICP (≥ 25 mmHg) in emergency settings. This study presents a new EEG-based noninvasive monitoring method of the physiologic parameters of emergency patients, especially in an OOHS, and evaluates the performance of the proposed models. In this study, EEG was analyzed to predict immediate ETCO2, CaBF, and ICP. The prediction models demonstrate that a noninvasive EEG can yield clinically important predictive outcomes. Eventually, the EEG parameters should be investigated with regard to the long-term neurological and functional outcomes. Further clinical trials are warranted to improve and evaluate the feasibility of the proposed method with respect to the neurological evaluation scores, such as the cerebral performance category and modified Rankin scale.Abstract i Contents iv List of Tables viii List of Figures x List of Abbreviations xii Chapter 1 General Introduction 1 1.1 Electroencephalogram 1 1.2 Clinical use of spontaneous EEG 5 1.3 EEG and cerebral hemodynamics 7 1.4 EEG use in emergency settings 9 1.5 Noninvasive CPR assessment 10 1.6 Noninvasive traumatic brain injury assessment 16 1.7 Thesis objectives 21 Chapter 2 EEG-based Brain Resuscitation Index for Monitoring Systemic Circulation During CPR 23 2.1 Introduction 23 2.2 Methods 25 2.2.1 Ethical statement 25 2.2.2 Study design and setting 25 2.2.3 Experimental animals and housing 27 2.2.4 Surgical preparation and hemodynamic measurements 27 2.2.5 EEG measurement 29 2.2.6 Data analysis 32 2.2.7 EBRI calculation 33 2.2.8 Delta-EBRI calculation 34 2.3 Results 36 2.3.1 Hemodynamic parameters 36 2.3.2 Changes in EEG parameters 37 2.3.3 EBRI calculation 39 2.3.4 Delta-EBRI calculation 41 2.4 Discussion 42 2.4.1 Accomplishment 42 2.4.2 Limitations 45 2.5 Conclusion 46 Chapter 3 EEG-based Prediction Model of the Recovery of Carotid Blood Flow for Monitoring Cerebral Circulation During CPR 47 3.1 Introduction 47 3.2 Methods 50 3.2.1 Ethical statement 50 3.2.2 Study design and setting 50 3.2.3 Experimental animals and housing 52 3.2.4 Surgical preparation and hemodynamic measurements 54 3.2.5 EEG measurement 55 3.2.6 Data processing 57 3.2.7 Data analysis 58 3.2.8 Development of machine-learning based prediction model 59 3.3 Results 63 3.3.1 Results of CPR process 63 3.3.2 EEG changes with the recovery of CaBF 66 3.3.3 Changes in EEG parameters depending on four CaBF groups 68 3.3.4 Changes in EEG parameters depending on two CaBF groups 69 3.3.5 EEG parameters for prediction models 70 3.3.6 Performances of prediction models 73 3.4 Discussion 76 3.4.1 Accomplishment 76 3.4.2 Limitations 78 3.5 Conclusion 80 Chapter 4 EEG-based Prediction Model of an Increased Intra-Cranial Pressure for TBI patients 81 4.1 Introduction 81 4.2 Methods 83 4.2.1 Ethical statement 83 4.2.2 Study design and setting 83 4.2.3 Experimental animals and housing 85 4.2.4 Surgical preparation and hemodynamic measurements 86 4.2.5 EEG measurement 88 4.2.6 Data processing 90 4.2.7 Data analysis 90 4.2.8 Development of machine-learning based prediction model 91 4.3 Results 92 4.3.1 Hemodynamic changes during brain injury phase 92 4.3.2 EEG changes with an increase of ICP 93 4.3.3 EEG parameters for prediction models 94 4.3.4 Performances for prediction models 95 4.4 Discussion 100 4.4.1 Accomplishment 100 4.4.2 Limitations 104 4.5 Conclusion 104 Chapter 5 Summary and Future works 105 5.1 Thesis summary and contributions 105 5.2 Future direction 108 Bibilography 113 Abstract in Korean 135Docto

The Role Of Awake Fiberoptic Intubation In The Difficult Airway

Awake fiberoptic intubation (AFOI) is a specialized technique used by anesthesia providers to decrease the risk of airway management during tracheal intubation of a predicted difficult airway. The rate of AFOI has decreased, and the advent of the video laryngoscope may be a contributing factor. However, there is still a need for AFOI. This case study reviews the relevance of, current indications for, and best practices for performing AFOI. Is AFOI still relevant in current anesthesia practice when managing airway difficulties such as limited mouth opening, obstructive sleep apnea, limited neck extension, head and neck pathology, morbid obesity, and progressive airway compromise? The Difficult Airway Society’s 2020 guidelines for AFOI in adults is used as an example to evaluate the performance and outcomes of the case study presented in this paper

Airway Management Outside the Operating Room

An anesthesiologist is an expert at airway management in the clinical environment. He or she has mastered knowledge of the anatomy and physiology of the normal and potentially abnormal airway. The environment of the operating room (OR) has been considered to be their most familiar area of work, where they feel most confident. Airway management outside this area is known to put patients at an increased risk of complications. This chapter addresses the important facets of this indispensable skill when used outside the operating room, taking into consideration both anesthesiologists and non-anesthesiologists as operators. Since the intensive care unit (ICU) is a similar environment to the OR, a separate chapter has been written for airway management in the ICU. Therefore, this chapter will concentrate on other areas outside the OR. It will not address resuscitation scenarios