extubation failure
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2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Junpei Haruna ◽  
Hiroomi Tatsumi ◽  
Satoshi Kazuma ◽  
Aki Sasaki ◽  
Yoshiki Masuda

Abstract Background Extubation failure, i.e., reintubation in ventilated patients, is a well-known risk factor for mortality and prolonged stay in the intensive care unit (ICU). Although sputum volume is a risk factor, the frequency of tracheal suctioning has not been validated as a predictor of reintubation. We conducted this study to examine whether frequent tracheal suctioning is a risk factor for reintubation. Patients and methods We included adult patients who were intubated for > 72 h in the ICU and extubated after completion of spontaneous breathing trial (SBT). We compared the characteristics and weaning-related variables, including the frequency of tracheal suctioning between patients who required reintubation within 24 h after extubation and those who did not, and examined the factors responsible for reintubation. Results Of the 400 patients enrolled, reintubation was required in 51 (12.8%). The most common cause of reintubation was difficulty in sputum excretion (66.7%). There were significant differences in sex, proportion of patients with chronic kidney disease, pneumonia, ICU admission type, the length of mechanical ventilation, and ICU stay between patients requiring reintubation and those who did not. Multivariate analysis showed frequent tracheal suction (> once every 2 h) and the length of mechanical ventilation were independent factors for predicting reintubation. Conclusion We should examine the frequency of tracheal suctioning > once every 2 h in addition to the length of mechanical ventilation before deciding to extubate after completion of SBT in patients intubated for > 72 h in the ICU.


2022 ◽  
pp. respcare.09476
Author(s):  
Thibaut Genty ◽  
Florent Laverdure ◽  
Olivier Peyrouset ◽  
Saïda Rezaiguia-Delclaux ◽  
Jacques Thès ◽  
...  

2022 ◽  
Vol 86 (1) ◽  
pp. 398-401
Author(s):  
Mohamed Elsayed Elsetouhi ◽  
Lotfy Mohamed Elsayed ◽  
Ali Abd El-Hameed Abdo ◽  
M. M. Shehab

Children ◽  
2022 ◽  
Vol 9 (1) ◽  
pp. 30
Author(s):  
Eugenio Spaggiari ◽  
Maria Amato ◽  
Ornella Angela Ricca ◽  
Luigi Corradini Zini ◽  
Ilaria Bianchedi ◽  
...  

Background: Prolonged mechanical ventilation in preterm infants may cause complications. We aimed to analyze the variables affecting extubation outcomes in preterm infants at high risk of extubation failure. Methods: This was a single-center, observational, retrospective study. Extubation failure was defined as survival with the need for reintubation within 72 h. Successfully extubated neonates (group 1) were compared to those with failed extubation (group 2). Multivariate logistic regression analysis evaluated factors that predicted extubation outcomes. Results: Eighty infants with a birth weight under 1000 g and/or gestational age (GA) under 28 weeks were included. Extubation failure occurred in 29 (36.2%) and success in 51 (63.8%) neonates. Most failures (75.9%) occurred within 24 h. Pre-extubation inspired oxygen fraction (FiO2) of 27% had a sensitivity of 58.6% and specificity of 64.7% for extubation failure. Post-extubation FiO2 of 32% had a sensitivity of 65.5% and specificity of 62.8% for failure. Prolonged membrane rupture (PROM) and high GA were associated with extubation success in multivariate logistic regression analysis. Conclusions: High GA and PROM were associated with extubation success. Pre- and post-extubation FiO2 values were not significantly predictive of extubation failure. Further studies should evaluate if overall assessment, including ventilatory parameters and clinical factors, can predict extubation success in neonates.


2021 ◽  
pp. 1-8
Author(s):  
Sydney R. Rooney ◽  
Evan L. Reynolds ◽  
Mousumi Banerjee ◽  
Sara K. Pasquali ◽  
John R. Charpie ◽  
...  

Abstract Background: Cardiac intensivists frequently assess patient readiness to wean off mechanical ventilation with an extubation readiness trial despite it being no more effective than clinician judgement alone. We evaluated the utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease. Methods: This was a retrospective analysis of clinical registry data and streamed physiologic extubation readiness trial data from one paediatric cardiac ICU (12/2016-3/2018). We analysed patients’ final extubation readiness trial. Machine learning methods (classification and regression tree, Boosting, Random Forest) were performed using clinical/demographic data, physiologic data, and both datasets. Extubation failure was defined as reintubation within 48 hrs. Classifier performance was assessed on prediction accuracy and area under the receiver operating characteristic curve. Results: Of 178 episodes, 11.2% (N = 20) failed extubation. Using clinical/demographic data, our machine learning methods identified variables such as age, weight, height, and ventilation duration as being important in predicting extubation failure. Best classifier performance with this data was Boosting (prediction accuracy: 0.88; area under the receiver operating characteristic curve: 0.74). Using physiologic data, our machine learning methods found oxygen saturation extremes and descriptors of dynamic compliance, central venous pressure, and heart/respiratory rate to be of importance. The best classifier in this setting was Random Forest (prediction accuracy: 0.89; area under the receiver operating characteristic curve: 0.75). Combining both datasets produced classifiers highlighting the importance of physiologic variables in determining extubation failure, though predictive performance was not improved. Conclusion: Physiologic variables not routinely scrutinised during extubation readiness trials were identified as potential extubation failure predictors. Larger analyses are necessary to investigate whether these markers can improve clinical decision-making.


2021 ◽  
Vol 50 (1) ◽  
pp. 249-249
Author(s):  
Jamie Palumbo ◽  
Gerardo Soto-Campos ◽  
Tom Rice ◽  
Randall Wetzel

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhenyu Liang ◽  
Qiong Meng ◽  
Chuming You ◽  
Bijun Wu ◽  
Xia Li ◽  
...  

Objective: To investigate the predictive value of lung ultrasound score (LUS) in the extubation failure from mechanical ventilation (MV) among premature infants with neonatal respiratory distress syndrome (RDS).Methods: The retrospective cohort study was conducted with a total of 314 RDS newborns who received MV support for over 24 h. After extubation from MV, infants were divided into extubation success and extubation failure groups. Extubation failure was defined as re-intubation within 48 h after extubation. Univariate and multivariate logistic regression analyses were used to identify the predictors of the extubation failure. The predictive effectiveness of the combined model and LUS in the extubation failure was assessed by receiver operating characteristic curve, area under curve (AUC), and internal validation.Results: 106 infants failed extubation from MV. The combined model for predicting the extubation failure was performed according to the predictors of gestational age, body length, birth weight, and LUS. The AUC of this combined model was 0.871 (sensitivity: 86.67%, specificity: 74.31%). The AUC of LUS was 0.858 (sensitivity: 84.00%, specificity: 80.69%), and the cutoff value was 18. There was no statistical difference in the predictive power between the combined model and LUS (Z = 0.880, P = 0.379). The internal validation result showed that the AUC of LUS was 0.855.Conclusions: LUS presented a good ability in predicting the extubation failure among RDS newborns after MV.


Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Lucas M. Fleuren ◽  
Tariq A. Dam ◽  
Michele Tonutti ◽  
Daan P. de Bruin ◽  
Robbert C. A. Lalisang ◽  
...  

Abstract Introduction Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. Methods We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. Results A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. Conclusion The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.


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