CARE MANAGEMENT DURING ADVANCED AIRWAY SUPPORT IN PREHOSPITAL CARE: A DECISION-MAKING ALGORITHMABSTRACT
Objective: to develop a care management algorithm for upper airway management with laryngeal masks by nurses working in prehospital care services.
Method: a multi-method implementation improvement study conducted in prehospital care services administered by the Rio de Janeiro State Health Department. The study was organized in two stages. A literature review was conducted in the pre-implementation stage to identify the best scientific evidence related to advanced airway support. The participants completed an instrument in the implementation stage on care management for upper airways with laryngeal masks. Data were analyzed using SPSS 22.0 software. The distribution of quantitative variables was summarized into proportions of interest: mean, median, minimum, maximum, and standard deviation. Finally, the algorithm was evaluated by expert nurses through a workshop using the brainstorming technique.
Results: the majority of professionals were female (63.9%), with 16.8 years of training and more than 10 years of experience in prehospital care (80.6%). As a result of the study, two mind maps were created presenting the evidence selected for structuring the algorithm’s decision-making inputs, stratifying the data into a finite sequence of care instructions focused on upper airway management.
Conclusion: the proposed improvement implementation considers using the algorithm as a strategy for systematizing differentiated and optimal nursing care, capable of appropriately prioritizing care.
DESCRIPTORS:
Prehospital care; Upper airway management; Laryngeal masks; Management decision making; Decision support techniques; Algorithm
RESUMO
Objetivo: elaborar um algoritmo de gestão do cuidado no manejo de vias aéreas superiores com máscara laríngea por enfermeiros que atuam no serviço de atendimento pré-hospitalar.
Método: estudo de implementação de melhorias, com abordagem multimétodos, realizado em serviços de atendimento pré-hospitalar administrados pela Secretaria Estadual de Saúde do Rio de Janeiro. A pesquisa foi organizada em duas etapas. Na etapa de pré-implementação, foi realizada uma revisão de literatura para identificar as melhores evidências científicas relacionadas ao suporte avançado de vias aéreas. Na etapa de implementação, os participantes responderam a um instrumento sobre a gestão do cuidado no manejo de vias aéreas com o uso da máscara laríngea. Os dados foram analisados pelo software SPSS 22.0. A distribuição de variáveis quantitativas foi sintetizada em proporções de interesse média, mediana, mínimo, máximo e desvio padrão. Por fim, o algoritmo foi avaliado por enfermeiros experts através de um workshop utilizando a técnica de brainstorming.
Resultados: a maioria dos profissionais é do sexo feminino (63,9%), com tempo de formação 16,8 anos e experiência em atendimento pré-hospitalar maior que 10 anos (80,6%). Como resultados do estudo, foram confeccionados 2 mapas mentais apresentando as evidências selecionadas para a estruturação dos inputs de tomada de decisão do algoritmo, estratificando os dados em uma sequência finita de instruções de cuidados, direcionados para a abordagem de vias aéreas.
Conclusão: a proposta de implementação de melhoria considera utilizar o algoritmo como estratégia de sistematização de uma assistência de enfermagem diferenciada e otimizadora, capaz de estabelecer adequadamente prioridades no cuidado.
DESCRITORES:
Assistência pré-hospitalar; Manejo das vias aéreas; Máscaras laríngeas; Tomada de decisões gerenciais; Técnicas de apoio para a decisão; Algoritmo
RESUMEN
Objetivo: desarrollar un algoritmo de gestión de cuidados para el manejo de la vía aérea superior con mascarilla laríngea por parte de enfermeras que trabajan en servicios de atención prehospitalaria.
Método: estudio multimétodo de mejora de la implementación, realizado en servicios de atención prehospitalaria de la Secretaría de Salud del Estado de Río de Janeiro. La investigación se organizó en dos etapas. En la etapa de preimplementación, se realizó una revisión bibliográfica para identificar la mejor evidencia científica relacionada con el soporte avanzado de la vía aérea. En la etapa de implementación, los participantes completaron un instrumento sobre gestión de cuidados para el manejo de la vía aérea con mascarilla laríngea. Los datos se analizaron con el programa SPSS 22.0. La distribución de las variables cuantitativas se resumió en proporciones de interés: media, mediana, mínimo, máximo y desviación estándar. Finalmente, el algoritmo fue evaluado por enfermeras expertas mediante un taller utilizando la técnica de lluvia de ideas.
Resultados: la mayoría de los profesionales eran mujeres (63,9%), con 16,8 años de formación y más de 10 años de experiencia en atención prehospitalaria (80,6%). Como resultado del estudio, se crearon dos mapas mentales que presentan la evidencia seleccionada para estructurar los datos de entrada del algoritmo para la toma de decisiones, estratificando los datos en una secuencia finita de instrucciones de cuidado, centradas en el manejo de la vía aérea.
Conclusión: la implementación de la mejora propuesta considera el uso del algoritmo como estrategia para sistematizar una atención de enfermería diferenciada y óptima, capaz de priorizar adecuadamente la atención.
DESCRIPTORES:
Atención prehospitalaria; Manejo de la vía aérea; Vías aéreas con mascarilla laríngea; Toma de decisiones de manejo; Técnicas de apoyo a la toma de decisiones; Algoritmo
INTRODUCTION
Pre-hospital care (PHC) is an important field of nursing practice1, and constitutes the service which seeks to reach victims early in the out-of-hospital setting after a health problem that may lead to physical disability or risk of death2. Nurses who work in intermediate life support in PHC are responsible for managing direct and indirect healthcare, and it is necessary to plan the entire care process2 so that this management process is not empirical.
Among the various clinical conditions in which PHC nurses work, Acute Respiratory Failure (ARF) is characterized as the inability to maintain an efficient state of gas exchange between the body and the atmosphere. This failure presents dyspnea as its most prominent symptom, and most patients require oxygen, either invasively or non-invasively3. The invasive device most recommended for use by nurses during ARF in Upper Airway (UA) management is the Laryngeal Mask (LM), which consists of a tube similar to the endotracheal tube, with an inflatable mask at the distal end suitable for adaptation to the posterior pharynx, sealing the region of the base of the tongue and the laryngeal opening4.
This study addresses care management in advanced practice of upper airway (UA) management through intubation with a supraglottic airway device (LM) in patients with ARF with clinical signs of a Glasgow Coma Scale <8*1 in the out-of-hospital setting5. From this perspective, we envision implementing improved care management in upper airway (UA) management with LM by nurses working in the PHC service, aligning with the Science of Healthcare Improvement. The science of improvement assumes that all improvement comes from the development, testing, and implementation of changes, and that structuring processes based on science enhances achievement of more effective results6. It is understood that the proposal of a decision-making algorithm becomes necessary as a supervisory, optimizing, and clinically applicable tool, capable of guiding and standardizing care management actions, especially regarding patient safety. Algorithms consist of a finite sequence of instructions carried out systematically, evaluating several variables in a short period of time. They are used as simple, objective and easily accessible instruments, which provide a complete overview of the care process, in addition to being a tool for standardizing techniques and management, serving as a guide for decision-making7.
In this context, the following question emerged: What care management actions do nurses consider relevant to develop an algorithm for managing upper airways with laryngeal mask (LM) intubation of patients with ARF? Therefore, the objective of this study was to develop a care management algorithm for upper airway management with a laryngeal mask (LM) by nurses working in prehospital care. The intent of this algorithm is to produce a viable and feasible tool which makes care for patients with ARF a more agile, accurate, and effective process.
METHOD
This is an improvement implementation study using a multi-method approach, conducted from March 2021 to April 2025. The study was organized into two stages: pre-implementation and implementation, each divided into two distinct phases, totaling four. A scoping review was conducted in the pre-implementation stage to identify the best available evidence, in addition to applying a data collection instrument to prepare an initial diagnosis. Then, a prototype of the decision-making algorithm was developed in the implementation stage, and evaluated by expert professionals. This evaluation enabled finalizing and validating the final product8.
The following descriptors in Portuguese were used for the literature search: Laryngeal Mask, Respiratory Failure, Health Management, Algorithm, Care Management, Care Management, Pre-Hospital Care, Pre-Hospital Emergency Care, and Pre-Hospital Services (Máscara Laríngea, Insuficiência Respiratória, Gestão em Saúde, Algoritmo, Gestão do Cuidado, Gerenciamento do Cuidado, Assistência Pré-Hospitalar, Atendimento de Emergência Pré-Hospitalar e Serviços Pré-Hospitalares). From these elements, the terms were then mapped into the controlled vocabularies: Health Sciences Descriptors (DECS), Medical Subject Healing (MESH), and Emtree (Embase subject headings). No language filters were applied. The time frame was 2015 to 2025. The protocol for this scoping review was registered with the Open Science Framework (OSF) under DOI 10.17605 (available at: https://doi.org/10.17605/OSF.IO/Z8CP6).
The search strategy was updated to ensure that the scoping review reflected the current state of scientific evidence on the use of LM in primary care. This update maintained the same descriptors and criteria of the PCC model (Population, Concept, Context) and the inclusion criteria used in the initial phase of the study. This expanded data collection provided the review with greater methodological robustness and scientific relevance.
As the study progressed, an instrument was created and applied to guide data collection in the research field, identifying how nurses manage direct and indirect care in UA management with LM in primary care.
The study setting is prehospital emergency care. The selected primary care unit is administered by the State Department of Health. This service operates on public roads and thoroughfares throughout the state of Rio de Janeiro. Ambulances classified as Intermediate Life Support (ILS) were selected, with a team consisting of a nurse, a nursing technician, and a driver. The sample consisted of 36 participants. Eligibility criteria were: being a nurse officer in the Rio de Janeiro State Fire Department; having at least 10 years of experience as a nurse; having been a team leader nurse in an intermediate primary care setting for at least 5 years; having completed a course in UA management with a LM; and having developed management activities for the use of LM for UA management in nursing care for patients with ARF and/or CPA. Data collected in Google Forms were exported to a Microsoft Excel 2007 spreadsheet and analyzed using SPSS 22.0.
The algorithm was developed based on scientific evidence synthesized in two mind maps. The first presents the content provided by the literature review, which selected best practices for direct and indirect care in managing UA with LM. The second mind map articulated the findings of the field research, which sought to identify how nurses manage this care in PHC. Lucidchart, an intelligent diagramming application with an intuitive interface, was used to begin structuring the future algorithm. This enabled creating the algorithm’s flowchart and its decision-making inputs. The algorithm then underwent the final stage, which includes content evaluation and its final design.
The algorithm evaluation process was conducted via videoconferencing on the Google Meet platform, which optimized participant attendance, enhanced information exchange, and recorded the meeting, allowing for review of all contributions to improve the algorithm. Next, five PHC nurses (team leaders) who had worked in pre-hospital emergency care for at least 10 years and had postgraduate degrees in their field participated in the workshop, being recruited through intentional selection by the researcher. The workshop was held in a single meeting, meeting the expectations for evaluating the algorithm. The following steps were used to develop and standardize the information obtained in the workshop: presentation of the improvement opportunity (algorithm), evaluation of the algorithm’s content as a proposal for implementation, brainstorming, selection of the best proposals for the final synthesis of the algorithm, and standardization of the product for possible practical use.
The study was approved by the Research Ethics Committee of the Aurora de Afonso Costa School of Nursing. The participants signed the Informed Consent Form, and the study followed all relevant regulations.
RESULTS
The records included in the study after a literature review are from 2015 to 2025, with 15 previously included studies and seven new studies (extended review) published from 2023 to 2025.
The analysis of the 22 selected studies enabled mapping updated evidence on the use of LM in emergency care, with an emphasis on the performance of nursing professionals, technical training, and clinical outcomes favorable to inserting the device in prehospital protocols. This evidence reinforces the importance of ongoing training, standardized protocols, and the articulation between science and care practice. The 22 studies included in this review present recommendations for direct and indirect care management in UA management (Chart 1).
Direct and indirect care recommendations from studies included in the review, Niterói, RJ, Brazil, 2025.
A mind map was subsequently generated (Figure 1) as a strategy for building knowledge and synthesizing the review findings, stratifying the scientific evidence mapped in the literature and allowing an overview; in other words, to be able to visualize all the content generated in conducting the research process and connect the best indirect and direct care practices for managing UA with LM selected in the literature.
Regarding the field research, a descriptive analysis was conducted on the responses of 36 experienced nurses providing PHC services at CBMERJ regarding emergency care requiring advanced airway support using an LM.
The field research initially profiled the participants, presenting the main quantitative variables of the study (table 1). The nurses were on average 40.4 years old, had 16.8 years of training, rated the importance of materials and equipment for safety in care as 9.3, rated their theoretical preparation for handling the LM as 8.4, rated their practical experience in handling the LM as 7.8, rated their ease of handling the LM as 8.6, rated their confidence in placing the LM as 8.4, and had an 86.5% success rate in laryngeal mask intubation attempts.
The field research also provided information on: nurses’ strategies to avoid danger to patients and PHC staff; which resources and materials in the PHC ambulance provide efficient care for patients with ARF/CPA and which resources were lacking for handling LMs in the PHC; actions taken to improve patient saturation in the PHC setting; factors which cause nurses’ insecurity when handling LMs; difficulties cited by nurses in handling and installing LMs; proposals for improvements in the quality of care related to advanced airway support; and guidance nurses would give to an inexperienced professional handling LMs in the PHC. In turn, of the 50 guidelines nurses would give to an inexperienced professional handling LMs in the PHC, 39 provided data to generate inputs for the proposed algorithm.
A second mind map (Figure 2) was then constructed which presents the content of the field research for visual structuring and knowledge storage.
Mind map of field research on how primary care nurses manage UA. Rio de Janeiro, RJ, Brazil, 2025.
Finally, from the theoretical and practical perspectives of nurses specializing in primary care services, the algorithm was evaluated from the perspective of these professionals in their field. This was done through a workshop where the algorithm was presented as an opportunity to implement improvements in care. Following a brainstorming session, participants analyzed the proposed algorithm and presented recommendations for improving the inputs. These were then evaluated for feasibility and usability, with the best proposals being selected for the final algorithm synthesis.
Among the improvements recommended for the final product were: changing the title of step 1 of the algorithm from “Manage Airway” to “Airway Management Planning”; including the action command “promote biosafety” in the rectangular process management figure; changing the decision-making input (second diamond figure) from “is patient breathing spontaneously?” to “is patient struggling to breathe?”; Inclusion of a rectangular figure with the ARF signals “dyspnea, tachypnea, bradypnea, and apnea”; inclusion of the third decision-making input “Glasgow Coma Scale <8?”.
Then, the Super QR Code Reader Generator app was used to develop a QR Code which allows anyone to easily access the decision-making algorithm using a cell phone camera to facilitate product replication. Figure 3 shows the final version of the algorithm for managing laryngeal mask insertion by primary care nurses.
Algorithm for managing laryngeal mask airway management by primary care nurses. Rio de Janeiro, RJ, Brazil, 2025.
The proposed algorithm consists of a finite sequence of well-defined instructions, implemented systematically, evaluating the various variables for managing upper airway (UA) pressure in patients with ARF, providing a comprehensive overview of the care process, standardizing LM insertion techniques, and serving as a guide for decision-making. The algorithm content was structured and systematized by articulating three research axes: mapping the literature to select content related to best care practices in managing upper airway (UA) pressure with LM, represented in Chart 1 and Mind Map 1; the findings of the field research, which identified how nurses manage this care in PHC (represented by Mind Map 2); and the contributions of the algorithm evaluation stage carried out in the workshop with PHC nurses.
From a structuring perspective, the algorithm was developed in four steps: the first step surveys indirect care management, presenting structural management items and the management of the care process to be developed, as well as the initial oval that constitutes the problem to be addressed (Acute Respiratory Failure); the second step focuses on airway assessment, presenting four diamonds with intervention proposals and complementary nursing assessments, which independently discuss the paths for: face mask use; BVM use; airway opening; whether invasive airway measures are truly necessary, based on the assessment of the patient’s level of consciousness and items to assess the chances of successful intubation with the LM; the third step presents the steps to be followed for LM insertion, with a maximum of two attempts recommended; the fourth and final step discusses care management after LM insertion.
DISCUSSION
Care management in advanced airway support in primary care settings is essential for patient safety, especially in emergencies, where rapid and informed decision-making directly impacts clinical outcomes. The data obtained corroborate the relevance of using algorithms as a tool for standardizing and optimizing the care process, promoting greater safety and efficiency in the work of primary care nurses.
Studies have shown that airway management is essential, as small changes in performance can have a significant impact on clinical outcomes. This management involves the use of appropriate techniques and skills, careful planning, and effective response to difficulties. A coordinated process, optimization of environmental conditions, and efficient communication are crucial for emergency response9-11,13-15,17-30.
The literature indicates that the use of supraglottic airways is a recommended strategy for airway management in emergencies, especially when intubation is not feasible. However, airway management in trauma patients presents additional challenges due to suboptimal intubation conditions and the increased risk of hypoxia, obstruction, hypoventilation, hypotension, and aspiration. Adapting the airway algorithm for these cases can improve procedural safety9-30.
Provision and regular use of management tools, such as algorithms, are highly recommended. A study evaluating the application of an institutional difficult airway algorithm concluded that this tool improves processes, standardizes, and optimizes management, making decision-making more understandable and effective9,13,16-18,23-24,27,29-30.
Familiarity and skill in using the LM increase with continued practice20-21. Implementing regular training and an algorithm adapted to the clinical environment are essential for successful difficult airway management. Studies indicate that continuous training not only teaches new skills, but also enhances those already practiced by PHC nurses9-13,19-21,23-24,29-30.
Furthermore, risk assessment and management when using LMs involve optimizing environmental conditions, including internal ambulance space constraints, inadequate lighting, and adverse weather conditions9-11,29. Strategies such as checking ambulance conditions and maintaining a dynamic and stable support base were incorporated into the proposed algorithm to ensure team and patient safety.
The literature also recommends adapting the infrastructure for LM management, including the provision of all LM sizes, the use of personal protective equipment (PPE), and standardization of necessary instruments11,13. LM management should be coordinated through clear communication and detailed care records for future improvements in care practice9-11,19-20,25.
Advanced Trauma Life Support (ATLS) recommends that execution within the XABCDE protocol for primary assessment of imminent risk of death should follow a sequence of priorities: X - exsanguinating hemorrhages; A - airway and cervical spine control; B - respiration and ventilation; C - circulation with hemorrhage control; D - neurological status; E - exposure and temperature control. Each step aims to quickly identify and treat life-threatening conditions, ensuring a systematic and effective approach to trauma management31. Upon completion of steps A and B, oxygen support should be established immediately and maintained throughout the trauma treatment phase10,12,15-1 7,24,26,29.
Analysis of the participating nurses’ responses revealed that most recognize the importance of structured planning in upper airway management, but point to challenges regarding clinical decisions and LM management in the prehospital setting. The lack of standardized protocols for selecting and inserting the supraglottic airway device was identified as a factor in safety, highlighting the need for evidence-based guidelines.
The data also indicate that decision-making in primary care is directly influenced by response time and environmental dynamics. The ability to quickly identify the need for invasive intervention and perform appropriate upper airway management in acute respiratory distress syndrome (ARF) situations can be crucial to patient survival. The proposed algorithm seeks to meet this need by offering a systematized guide to support nurses’ performance in complex scenarios.
The field research data reinforce the need for ongoing training for nurses to develop confidence in the decision to use the LM. Only 44.4% of nurses stated that the internal structure of the ambulance allows for adequate team movement to assist patients with acute respiratory distress syndrome (ARF). This limitation led to including the recommendation in the algorithm to remain close to the patient to ensure a dynamic and stable support base.
The participating nurses identified a need for systematization of the LM management technique. Furthermore, strategies to minimize risks in PHC were identified, including scene assessment, correct use of PPE, efficient communication among the team, and pre-care equipment checks. These elements were incorporated into the algorithm to standardize and optimize care.
Therefore, the analysis of the results demonstrates that implementing the decision-making algorithm has the potential to positively impact care practice, promoting greater patient safety and granting greater autonomy to nurses in emergency care management. Standardizing actions through the algorithm reduces variability in interventions, favoring uniformity of care and contributing to improve the quality of pre-hospital care.
The limitations of this study lie in the in-depth validation process, as it included only an initial evaluation of the algorithm by PHC nurses. Direct validation in direct care settings is considered, with the use of the algorithm by nurses in direct care of patients with ARF who may use LM.
Studies such as this with an interventionist approach point to a consistent perspective for thinking and discussing the advancement of Evidence-Based Practice and the evolution of Advanced Practice Nursing, as well as the possibility of successful paths for implementing care improvements.
CONCLUSION
The algorithm proposes a real impact on airway management, patient safety, and the healthcare team involved in the advanced airway support care process. The developed product is easily replicable in physical or digital form and offers moderate complexity of interpretation.
The proposed implementation of a care management algorithm for advanced airway support in pre-hospital care represents a significant advance in optimizing care practices, offering a systematic, evidence-based approach to laryngeal mask management. The findings of this study reinforce the need for standardization of procedures and ongoing training of nurses to improve the quality and safety of care provided.
The algorithm proposes a positive impact on clinical decision-making, enabling faster and more precise interventions, essential for stabilizing patients with acute respiratory failure. Furthermore, its application contributes to standardize care protocols, reducing variability in approaches and promoting more efficient and safer care. Given the challenges identified, we emphasize the importance of investing in professional training and adequately structuring primary care services, ensuring that nurses have access to tools and training that enable them to work safely and effectively. Thus, adopting the algorithm not only strengthens nurses’ role as key players in care management, but also raises standards of care, promoting better clinical outcomes and greater safety for patients and the healthcare team involved in the care process.
Lastly, the proposed product aims to achieve a local impact, but one that could reach international proportions. A comprehensive scoping review of 11 databases revealed no algorithms or similar approaches specifically aimed at managing airway obstructions with LM for nurses, demonstrating a high degree of innovation. The closest approach found were algorithms that recommended the use of a supraglottic airway device for difficult airways, in situations of “intubation fails” or failure after a second attempt at endotracheal or orotracheal intubation.
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NOTES
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ORIGIN OF THE ARTICLE
Article extracted from the dissertation - Gestão do cuidado durante o suporte avançado de vias aéreas no atendimento pré-hospitalar: algoritmo para tomada de decisão, presented to the Professional Master’s Program in Nursing Assistance, of Universidade Federal Fluminense, in 2022.
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FUNDING INFORMATION
PDPG-COFEN/Programa de Desenvolvimento da Pós-Graduação - Área de Enfermagem - Edital nº 28/2019. Número 88887.477308/2020-00 - Formação Profissional Especializada em Gestão e Processo de Enfermagem.
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APPROVAL OF ETHICS COMMITTEE IN RESEARCH
Approved by the Ethics Committee in Research of the Aurora de Afonso Costa School of Nursing under opinion number: 5,155,288 and registration: CAAE 52835821.8.0000.5243.
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TRANSLATED BY
Christopher J. Quinn.
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DATA AVAILABILITY
The dataset supporting the findings of this study is available upon reasonable request from the corresponding author Welliton Pestana Faria. The dataset is not publicly available due to its extensive volume, which exceeds the editorial space allowed for scientific articles. Full access to the material can be provided upon request, ensuring transparency and reproducibility of the results.
Edited by
The dataset supporting the findings of this study is available upon reasonable request from the corresponding author Welliton Pestana Faria. The dataset is not publicly available due to its extensive volume, which exceeds the editorial space allowed for scientific articles. Full access to the material can be provided upon request, ensuring transparency and reproducibility of the results.
