Text Version-ABCDEFG Algorithm A Airway

The Open Anesthesiology Journal, 2012

The difficult airway has been defined as a “clinical situation in which a conventionally trained anesthesiologist experiences difficulty with mask ventilation of the upper airway, tracheal intubation, or both.” Given the potentially lifethreatening consequences, the American Society of Anesthesiology has developed an airway algorithm that focuses on establishing an airway, generally for the induction of anesthesia. However, there is no algorithm on how to safely transition from an established airway back to the normal, natural airway. Up to 0.19 percent of patients can require reintubation in the post anesthesia recovery unit, with the known difficult airway at greater risk in these settings for failed reintubation. Because of this, there has been recognition of the need for guidelines in the form of an algorithm to deal with extubation in these patients. To fill this current need, we propose the following difficult to intubate patient extubation algorithm for use in the operating r...

Best Practice & Research Clinical Anaesthesiology, 2005

Management of the difficult airway is the most important patient safety issue in the practice of anaesthesia. Many national societies have developed algorithms and guidelines for management of the difficult airway. The key issues of this chapter are definition of terms, the advantages and disadvantages of the use of guidelines, and a comparison of different algorithms and guidelines for management of the most important clinical airway scenarios. Although there is no strong evidence of benefit for any specific strategy or algorithm for management of the difficult airway, there is strong agreement that a pre-planned strategy may lead to improved outcome.

Revista española de anestesiología y reanimación, 2018

Airway management is an essential area in anaesthesia, and anaesthesiologists are considered the most expert professionals to manage airway tasks. However, complications related to inadequate airway management remain the most frequent cause of morbidity and mortality. Algorithmic strategy to solve difficulties fails, due to several factors related to its structure and clinical application. The Vortex Approach has emerged as a response to the limitations found in the algorithmic strategy of managing the difficult airway, by using a cognitive aid strategy to reduce cognitive load and fixation error. This new strategy may represent a solution to the elusive problem of the challenging airway and reduce the complications rate.

Respiratory Medicine, 2011

Question: The objective of the study was to prospectively evaluate an algorithmic approach to the cause(s) of chronic dyspnea. Materials/patients/methods: Prospective observational study. The study group consisted of 123 patients with a chief complaint of dyspnea of unknown cause present for >8 weeks. Dyspnea severity scores were documented at entry and after therapy. Patients underwent an algorithmic approach to dyspnea. Therapy could be instituted at any time that data supported a treatable diagnosis. Whenever possible, accuracy of diagnosis was confirmed with an improvement in dyspnea after therapy. Tests required, spectrum and frequency of diagnoses, and the values of individual tests were determined. Results: Cause(s) was(were) diagnosed in 122/123 patients (99%); 97 patients had one diagnosis and 25 two diagnoses. Fifty-three percent of diagnoses were respiratory and 47% were non-respiratory. Following therapy, dyspnea improved in 63% of patients. Conclusions: The prospective algorithmic approach led to diagnoses in 99% of cases. A third of patients were diagnosed with each tier of the algorithm, thus minimizing the need for invasive testing. Specific diagnoses led to improvement in dyspnea in the majority of cases. Based on the results of this study, the algorithm can be revised to further minimize unnecessary tests without loss of diagnostic accuracy. ª a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / r m e d Respiratory Medicine (2011) 105, 1014e1021 0954-6111/$ -see front matter ª

Minerva anestesiologica, 2009

Difficult airway management and maintenance of oxygenation remain the two most challenging tasks for anesthetists, while also being controversial items in terms of clinically based-evidence to support relevant guidelines in the literature. Nevertheless, different expert groups and scientific societies from several countries have published guidelines dedicated to the management of difficult airways. These documents have been demonstrated to be useful in reducing airway management related critical accidents, despite their limited use in litigations and legal issues. The aim of this review is to compare different airway management guidelines published by the United States, United Kingdom, France, Italy, Germany, and Canada while trying to elucidate the main differences, weaknesses, and strengths for identifying critical concepts in the management of difficult airways.

Canadian Journal of Anaesthesia-journal Canadien D Anesthesie, 2010

Purpose Although guidelines for difficult airway management have been published, the extent to which consultant anesthesiologists follow these guidelines has not been determined. The purpose of this study is to observe how consultant anesthesiologists manage a “cannot intubate, cannot ventilate” (CICV) scenario in a high-fidelity simulator and to evaluate whether a simulation teaching session improves their adherence to the American Society of Anesthesiologists (ASA) difficult airway algorithm. Methods With Ethics Board approval and informed consent, all staff anesthesiologists in a single tertiary care institution were invited to enrol in this study where they managed a simulated unanticipated CICV scenario in a high-fidelity simulator. The scenario involved a patient with a difficult airway whose trachea could not be intubated and where it was impossible to ventilate the patient’s lungs. Airway management options, including laryngeal mask airway, a fibreoptic bronchoscope, and a Glidescope® were available for use but scripted to fail. A percutaneous cricothyroidotomy was required to re-establish adequate ventilation. Following the scenario, there was a personalized one-hour video-assisted expert debriefing focusing on the ASA difficult airway guidelines and “hands-on” cricothyroidotomy teaching. The second scenario followed immediately with an identical CICV scenario. The content to either scenario was not revealed beforehand. Outcome measures included: 1) major deviations from the ASA difficult airway guidelines; 2) time to start cricothyroidotomy; and 3) time to achieve ventilation. Results Thirty-eight anesthesiologists agreed to participate. The number of major deviations from the ASA algorithm was similar in the first and second sessions. These deviations included: multiple laryngoscopies (0 vs 2 pre-post; P = 0.49), use of fibreoptic bronchoscope (8 vs 7 pre-post; P = 1.0), bypass of laryngeal mask airway attempt (7 vs 13 pre-post; P = 0.19), and failure to call for anesthetic help (12 vs 8 pre-post; P = 0.43). However, more participants failed to call for surgical help in the second session (7 vs 16; P = 0.04). The times to start cricothyroidotomy and the times to achieve ventilation were significantly shorter in the second session (205.5 ± 61.3 sec vs 179.7 ± 65.1 sec; P = 0.01 and 356.9 ± 117.2 sec vs 269.4 ± 77.43 sec; P = 0.0002, respectively). Conclusion No substantial changes in airway management in a CICV scenario were observed after an intense one-hour personalized video-assisted airway-focused simulation debriefing session with an expert. It appears that multiple factors other than airway algorithms come into play in emergency airway decision-making processes, including one’s personal clinical experience with the many available airway devices. Objectif Bien que les lignes directrices relatives à la prise en charge de l’intubation difficile aient été publiées, on ne sait pas dans quelle mesure les anesthésiologistes consultants s’y conforment. L’objectif de cette étude consiste à observer la manière dont les anesthésiologistes consultants gèrent une situation lors de laquelle l’intubation et la ventilation sont impossibles (« cannot intubate, cannot ventilate » - CICV) dans le cadre d’une simulation haute fidélité, ainsi que d’évaluer si la tenue d’une séance de formation par simulation accroît l’observance de l’algorithme de prise en charge de l’intubation difficile de l’American Society of Anesthesiologists (ASA). Méthodes Avec l’approbation du comité d’éthique et un consentement éclairé, tous les anesthésiologistes d’une seule institution de soins tertiaires ont été invités à participer à cette étude et à gérer un scénario CICV imprévu dans le cadre d’une simulation haute fidélité. Le scénario impliquait un cas d’intubation difficile chez un patient dont la trachée ne pouvait être intubée et dont les poumons ne pouvaient être ventilés. Des options de prise en charge des voies aériennes, qui incluaient un masque laryngé, un bronchoscope à fibres optiques et un Glidescope®, étaient disponibles, mais le scénario prévoyait leur échec. Une cricothyroïdotomie percutanée était nécessaire pour rétablir une ventilation adéquate. Au terme du scénario, les participants ont assisté à une séance d’évaluation dirigée par un expert, personnalisée et assistée par une vidéo d’une heure portant sur les lignes directrices de l’ASA pour la prise en charge de l’intubation difficile et la formation « pratique » en cricothyroïdotomie. Cette étape était immédiatement suivie d’un deuxième scénario CICV identique. Le contenu des deux scénarios n’était pas révélé à l’avance. Les indicateurs de résultat incluaient : 1) les déviations majeures aux lignes directrices de l’ASA pour la prise en charge de l’intubation difficile; 2) le délai nécessaire à la mise en œuvre de la cricothyroïdotomie; et 3) le délai nécessaire pour assurer la ventilation. Résultats Trente-huit anesthésiologistes ont accepté de participer à l’étude. Le nombre de déviations majeures à l’algorithme de l’ASA était identique lors de la première et de la deuxième séance. Ces déviations comprenaient: les laryngoscopies multiples (0 contre 3 avant-après; P = 0,49), l’utilisation d’un bronchoscope à fibres optiques (8 contre 7 avant-après; P = 1,0), l’omission de la tentative de pose du masque laryngé (7 contre 13 avant-après; P = 0,19) et le défaut de demander de l’aide sur le plan anesthésique (12 contre 8 avant-après; P = 0,43). Cependant, pendant la deuxième séance, un plus grand nombre de participants ont omis de demander de l’aide sur le plan chirurgical (7 contre 16; P = 0,04). Les délais nécessaires pour la mise en œuvre de la cricothyroïdotomie et pour assurer la ventilation étaient beaucoup plus courts pendant la deuxième séance (205,5 ± 61,3 sec. contre 179,7 ± 65,1 sec.; P = 0,01, et 356,9 ± 117,2 sec. contre 269,4 ± 77,43 sec.; P = 0,0002, respectivement). Conclusion Dans le cadre d’un scénario CICV, aucun changement important de la prise en charge des voies aériennes n’a été observé après une séance d’évaluation personnalisée assistée par vidéo, dirigée par un expert et portant sur les voies aériennes. Il semble que de nombreux facteurs, outre les algorithmes de prise en charge de l’intubation, entrent en jeu lors des processus de prise de décisions liées à l’intubation d’urgence, y compris l’expérience clinique personnelle avec les nombreux dispositifs disponibles pour les voies aériennes.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine, 2012

Numerous models of human lungs with various levels of idealization have been reported in the literature; consequently, results acquired using these models are difficult to compare to in vivo measurements. We have developed a set of model components based on realistic geometries, which permits the analysis of the effects of subsequent model simplification. A realistic digital upper airway geometry except for the lack of an oral cavity has been created which proved suitable both for computational fluid dynamics (CFD) simulations and for the fabrication of physical models. Subsequently, an oral cavity was added to the tracheobronchial geometry. The airway geometry including the oral cavity was adjusted to enable fabrication of a semi-realistic model. Five physical models were created based on these three digital geometries. Two optically transparent models, one with and one without the oral cavity, were constructed for flow velocity measurements, two realistic segmented models, one wit...

 Nasal Prongs  Low flow delivery system  Flow rate: 1 -6 L/min  FiO2 starts at 24% for 1L/min and increases 4% for each L/min up to 44% for 6 Lmin  Well tolerated  Must be low flow rate: > 6 L/min cause nasal mucosal drying  Use: minimal or no respiratory distress or oxygenation problem  Simple (Hudson) Face Mask (Rebreather)  Low flow delivery system  Flow rate b/w 5 -8 L/min  FiO2: 5 -6 is 40%, 6 -7 is 50%, 7 -8 is 60%  Mask doesn't need tight seal  Use: as per nasal prongs but require higher concentrations  Venturi Mask  High flow delivery system  Flow rate b/w 4 -12 L/min  FiO2 can be set specifically with different flow rate and air ports  Fi02 can be 24, 28, 31, 35, and 40%  Use: COPD patient that requires specific oxygen concentrations to administer high Fi02 but not too high such that the hypoxic drive to breath is blunted; titrate to keep sats about 88%  Non-Rebreather with Resevoir Bag  High flow delivery system  Flow rate b/w 6 -10 L/min  Proper use at 10 L/min supplies 70% Fi02  Requires tight fitting mask  Should be used when 50% oxygen required  Use: any spontaneously breathing patient who requires highest [02] possible (carbon monoxide poisoning, trauma, etc)  Puritan Face Mask  High flow delivery system  Flow rate > 10 L/min  Fi02 with single setup are 35 -50%, double setup are 50 -80+%  Provides highest level of humidified oxygen  Flow rates of up to 30L/min can be achieved w/ double set up  Should be used when > 50% oxygen required  Bag Valve Mask  FiO2 depends on the type of bag  Good seal, bag with exhalation portion in proper position, you can get Fi02 to 90%

Journal of Fluids Engineering, 2014

Computational fluid dynamics (CFD) simulations were performed using large-scale models of the human lung airway and unsteady periodic breathing conditions. The computational domain included fully coupled representations of the orotracheal region and large conducting zone up to generation four (G4) obtained from patient-specific CT data, and the small conducting zone (to the 16th generation) obtained from a stochastically generated airway tree with statistically realistic morphological characteristics. A reducedgeometry airway model was used, in which several airway branches in each generation were truncated, and only select flow paths were retained to the 16th generation. The inlet and outlet flow boundaries corresponded to the oral opening, the physical inlet/outlet boundaries at the terminal bronchioles, and the unresolved airway boundaries created from the truncation procedure. The total flow rate was specified according to the expected ventilation pattern for a healthy adult male, which was supplied by the wholebody modeling software HumMod. The unsteady mass flow distribution at the distal boundaries was prescribed based on a preliminary steady-state simulation with an applied flow rate equal to the average flow rate during the inhalation phase of the breathing cycle. In contrast to existing studies, this approach allows fully coupled simulation of the entire conducting zone, with no need to specify distal mass flow or pressure boundary conditions a priori, and without the use of impedance or one-dimensional (1D) flow models downstream of the truncated boundaries. The results show that: (1) physiologically realistic flow is obtained in the model, in terms of cyclic mass conservation and approximately uniform pressure distribution in the distal airways; (2) the predicted alveolar pressure is in good agreement with correlated experimental data; and (3) the use of reduced-order geometry modeling allows accurate and efficient simulation of large-scale breathing lung flow, provided care is taken to use a physiologically realistic geometry and to properly address the unsteady boundary conditions.