Características de la ventilación mecánica invasiva en COVID-19 para médicos no especialistas

Rozas, Bastian Abarca; E, Urra; Garzón, Javier García

doi:10.25237/revchilanestv49n04-06

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…It enhances the accuracy of triage processes, optimizes resource allocation, and prepares healthcare systems for efficient mechanical ventilator management, ultimately improving patient outcomes and healthcare efficiency during critical respiratory seasons. This is important considering that a shortage of ventilators may represent a high risk for patient’s health as the ventilators replace the respiratory function during hypoxemic and hypercapnic respiratory failure [ 26 ]. This study signifies a substantial step forward in the application of AI and simulation technologies in emergency care.…”

Section: Discussionmentioning

confidence: 99%

An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study

Ortiz-Barrios,

Petrillo,

Arias-Fonseca

et al. 2024

Int J Emerg Med

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Background Shortages of mechanical ventilation have become a constant problem in Emergency Departments (EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES) to design effective interventions ensuring the high availability of ventilators for patients needing these devices. Methods First, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European hospital group was used to validate the proposed methodology. Results The number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity of the AI model was 93.08% (95% confidence interval, [88.46 − 96.26%]), whilst the specificity was 85.45% [77.45 − 91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 − 95.13%) and 87.85% (80.12 − 93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 − 100%). Finally, the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource capacity strategy. Conclusions Combining AI and DES helps healthcare decision-makers to elucidate interventions shortening the waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics.

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Section: Discussionmentioning

confidence: 99%

An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study

Ortiz-Barrios,

Petrillo,

Arias-Fonseca

et al. 2024

Int J Emerg Med

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“…The design should provide appropriate ventilation for patients prior to releasing a specific ventilator [12], and thus it was considered the tidal volume and the respiratory rate that would maintain patients stable [13]. The tidal volume was determined based on a ratio with the ideal weight of patients and it is calculated with reference to the human weight: IBW (ideal body weight, kilograms) men [14]: Figure 1 shows the I/E ratio [15], inspiration [16] (insufflation) and expiration (exhalation), which may be adjusted to 1:3. But if the patient condition is preexisting asthma [17] or exacerbation of chronic obstructive pulmonary disease, it may be adjusted to 1:4.…”

Section: Methodsmentioning

confidence: 99%

Design and construction of automated mechanical ventilation equipment to assist respiratory failure

Carrillo¹,

Gómez²,

Rodas³

et al. 2023

AMEIR

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This document presents the requirements met for the design, construction and first validation of a mechanical ventilation system to be used in patients with respiratory failure, which in the initial context was due to the COVID-19 pandemic. The design required the use of computer aided drawing software (Computer Aided Design) CAD and the construction required the use of installed capabilities in mechanical, electropneumatic, electronic, biomedical and automation manufacturing of Don Bosco University (El Salvador) institutes and centers. The adjustment, configuration and programming works were in charge of research professors specialized in these disciplines. The elements used for its construction were available in the Salvadoran market, considering the closure of the borders as a government measure in face of the expansion of the pandemic. After the design, manufacture and commissioning stage, measurements of the conditions of the supplied air were made with the help of professionals dedicated to the maintenance of medical equipment and with the approval of internist doctors. The results achieved are those obtained with paramedical equipment and with first aid equipment, for which it has been foreseen that the equipment can be tested in a subsequent instance with the certified medical union.

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“…El volumen corriente se determinó bajo una relación con el peso ideal de los pacientes y se calcula tomando como referencia el peso de las personas: PCI (peso corporal ideal, kilogramos) hombres [14]: 50 + 2,3*(talla en pulgadas -60) o 50 + 0,9*(talla en cm -152,4) PCI (peso corporal ideal, kilogramos) mujeres: 45,5 + 2,3*(talla en pulgadas -60) o 45,5 + 0,9*(talla en cm -152,4) En cuanto a la frecuencia respiratoria, el ciclo de inspiración y espiración tiene relación con la edad de los pacientes, siguiendo el comportamiento mostrado por la curva de la Figura 1. La Figura 1 muestra la relación I/E [15], inspiración [16] (insuflación) y espiración (exhalación), el cual se puede ajustar a valores 1:3. Pero si la condición de los pacientes es preexistencia de asma [17] o exacerbaciones de enfermedad pulmonar obstructiva crónica se pueden realizar ajustes de 1:4.…”

Section: Materiales Y Métodosunclassified

Detección de peatones en el día y en la noche usando YOLO-v5

Carrillo

Gómez

Rodas

et al. 2021

ings

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Este documento presenta los requerimientos cumplidos para el diseño, construcción y primera validación de un sistema de ventilación mecánica, para ser utilizado en pacientes con insuficiencia respiratoria, que en el contexto inicial fuera a causa de la pandemia de COVID-19. El diseño requirió el uso de software de dibujo asistido por computadora (Computer Aided Design) CAD y la construcción necesitó el uso de las capacidades instaladas en manufactura mecánica, electroneumática, electrónica, biomédica y automatización de los institutos y centros de la Universidad Don Bosco. Los trabajos de ajuste, configuración y programación estuvieron a cargo de docentes investigadores especialistas en dichas disciplinas. Los elementos empleados para su construcción estaban disponibles en el mercado salvadoreño, considerando el cierre de las fronteras como medida gubernamental ante la expansión de la pandemia. Luego de la etapa de diseño, fabricación y puesta en funcionamiento, se realizaron mediciones de las condiciones del aire suministrado con la ayuda de profesionales dedicados al mantenimiento de equipo médico y con el visto bueno de doctores internistas. Los resultados conseguidos son los que se obtienen con equipos paramédicos y con equipos de primera asistencia, por lo cual se ha previsto que el equipo pueda ser probado en una siguiente instancia con el gremio médico certificado.

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Características de la ventilación mecánica invasiva en COVID-19 para médicos no especialistas

Cited by 4 publications

References 23 publications

An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study

An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study

Design and construction of automated mechanical ventilation equipment to assist respiratory failure

Detección de peatones en el día y en la noche usando YOLO-v5

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