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Available online 26 July 2023
Predictors of mechanical ventilation and mortality in critically ill patients with COVID-19 pneumonia
Predictores de ventilación mecánica y mortalidad en pacientes críticos con neumonía por COVID-19
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Sergio Muñoz Lezcanoa,1,
Corresponding author
smunozle@gmail.com

Corresponding author.
, Miguel Ángel Armengol de la Hozb,1, Alberto Corbic, Fernando Lópezd, Miguel Sánchez Garcíae, Antonio Nuñez Reizf, Tomás Fariña Gonzálezg, Viktor Yordanov Zlatkova
a PhD Student of the Program in Computer Science, Universidad Internacional de La Rioja (UNIR), Avenida de La Paz, 137, 26006 Logroño, La Rioja, Spain
b Big Data Department, PMC-FPS, Consejería de Salud y Consumo, Junta de Andalucía, Spain
c Research Institute for Innovation & Technology in Education (iTED), Universidad Internacional de La Rioja (UNIR), Avenida de La Paz, 137, 26006 Logroño, La Rioja, Spain
d Mathematical Analysis and Applied Mathematics Department, Faculty of Mathematics. Universidad Complutense de Madrid, Spain
e Critical Care Department, Hospital Clínico San Carlos, Martín Lagos s/n, 28040 Madrid, Spain
f Critical Care Department, Hospital Universitario Clínico San Carlos, Martín Lagos s/n, 28040 Madrid, Spain
g Critical Care Department, Hospital Universitario Infanta Sofía, Spain
This item has received
Received 03 March 2023. Accepted 22 June 2023
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Tables (3)
Table 1. Group of predictors for Invasive Mechanical Ventilation regression purposes.
Table 2. IMV Results. Group of predictors used for mortality prediction with GLMM tree algorithm.
Table 3. IMV Results.
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Additional material (1)
Abstract
Objective

To determine if potential predictors for invasive mechanical ventilation (IMV) are also determinants for mortality in COVID-19-associated acute respiratory distress syndrome (C-ARDS).

Design

Single center highly detailed longitudinal observational study.

Setting

Tertiary hospital ICU: two first COVID-19 pandemic waves, Madrid, Spain.

Patients or participants

: 280 patients with C-ARDS, not requiring IMV on admission.

Interventions

None.

Main variables of interest

: Target: endotracheal intubation and IMV, mortality.

Predictors: demographics, hourly evolution of oxygenation, clinical data, and laboratory results.

Results

The time between symptom onset and ICU admission, the APACHE II score, the ROX index, and procalcitonin levels in blood were potential predictors related to both IMV and mortality. The ROX index was the most significant predictor associated with IMV, while APACHE II, LDH, and DaysSympICU were the most with mortality.

Conclusions

According to the results of the analysis, there are significant predictors linked with IMV and mortality in C-ARDS patients, including the time between symptom onset and ICU admission, the severity of the COVID-19 waves, and several clinical and laboratory measures. These findings may help clinicians to better identify patients at risk for IMV and mortality and improve their management.

Keywords:
Acute respiratory distress syndrome
Invasive mechanical ventilation
COVID-19
Machine learning
Artificial intelligence
Predictors
Resumen
Objetivo

Determinar si las variables clínicas independientes que condicionan el inicio de ventilación mecánica invasiva (VMI) son los mismos que condicionan la mortalidad en el síndrome de distrés respiratorio agudo asociado con COVID-19 (C-SDRA).

Diseño

Estudio observacional longitudinal en un solo centro.

Ámbito

UCI, hospital terciario: primeras dos olas de COVID-19 en Madrid, España.

Pacientes o participantes

280 pacientes con C-SDRA que no requieren VMI al ingreso en UCI.

Intervenciones

Ninguna.

Principales variables de interés

Objetivo: VMI y Mortalidad.

Predictores: demográficos, variables clínicas, resultados de laboratorio y evolución de la oxigenación.

Resultados

El tiempo entre el inicio de los síntomas y el ingreso en la UCI, la puntuación APACHE II, el índice ROX y los niveles de procalcitonina en sangre eran posibles predictores relacionados tanto con la IMV como con la mortalidad. El índice ROX fue el predictor más significativo asociada con la IMV, mientras que APACHE II, LDH y DaysSympICU fueron los más influyentes en la mortalidad.

Conclusiones

Según los resultados obtenidos se identifican predictores significativos vinculados con la VMI y mortalidad en pacientes con C-ARDS, incluido el tiempo entre el inicio de los síntomas y el ingreso en la UCI, la gravedad de las olas de COVID-19 y varias medidas clínicas y de laboratorio. Estos hallazgos pueden ayudar a los médicos a identificar mejor a los pacientes en riesgo de IMV y mortalidad y mejorar su manejo.

Palabras clave:
Síndrome de distrés respiratorio agudo
Ventilación mecánica invasiva
COVID-19
Aprendizaje automático
Inteligencia artificilal
Predictores

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