Prediction of extubation outcome in mechanically ventilated patients: Development and validation of the Extubation Predictive Score (ExPreS)

Comparison of the Recent ExPreS Score, WEANSNOW Score, and the Parsimonious HACOR Score as the Best Predictor of Weaning: An Externally Validated Prospective Observational Study

Background

Since weaning failure is multifactorial, comprehensive weaning scores encompassing not only the respiratory component but also nonrespiratory aspects are quintessential for successful weaning prediction.

Materials and methods

This was a single-center prospective observational study on 128 intensive care unit (ICU) patients undergoing spontaneous breathing trials (SBT). The extubation prediction score (ExPreS), heart rate, acidosis, consciousness, oxygenation, respiratory rate (HACOR), and weaning parameters, endotracheal tube size, arterial blood gas analysis, nutrition, secretions, neuromuscular affecting agents, obstructive airway problems and wakefulness (WEANSNOW) scores were compared for their diagnostic accuracy for successful weaning prediction.

Results

Out of 128 patients, 49 (38.3%) patients had weaning failure, and 79 (61.7%) had weaning success. The patients in the weaning failure group had significantly higher APACHE II scores, WEANSNOW scores, HACOR scores, MV days, and significantly lower ExPreS scores as compared to the successful weaning group. Multivariable regression analysis showed that ExPreS score p = 0.015, adjusted OR 0.960, 95% CI (0.929-0.992) and HACOR score p < 0.001, adjusted OR 1.357, 95% CI (1.176-1.567) were independent predictors of weaning failure. The HACOR score had an AUC of 0.830, cut-off ≥5, p < 0.001, sensitivity 76%, specificity 68%, diagnostic accuracy 70% to predict weaning failure. The ExPreS score had an AUC of 0.735, cut-off ≥69, p < 0.001, sensitivity of 70.9%, specificity of 69.4%, and diagnostic accuracy of 70.3% to predict weaning success. Both the HACOR and ExPreS scores were good models for predicting weaning outcomes (model quality 0.76 and 0.64 respectively).

Conclusion

The parsimonious HACOR score is comparable to the ExPreS score for the prediction of weaning outcomes in critically ill patients.

How to cite this article

Nayak G, Chaudhuri S, Ravindranath S, Todur P. Comparison of the Recent ExPreS Score, WEANSNOW Score, and the Parsimonious HACOR Score as the Best Predictor of Weaning: An Externally Validated Prospective Observational Study. Indian J Crit Care Med 2024;28(3):273-279.

Hail the HACOR as a Customized Indian Weaning Score!

Singh MK. Hail the HACOR as a Customized Indian Weaning Score! Indian J Crit Care Med 2024;28(3):198-199.

Impact of different oxygen therapy strategies on the risk of endotracheal reintubation in mechanically ventilated patients: A systematic review and meta-analysis

BACKGROUND

Mechanical ventilation (MV) is a crucial intervention for the support of patients with acute and severe respiratory failure in modern intensive care medicine. However, the mechanical forces resulting from the interplay between the ventilator and the respiratory system may cause pulmonary injury.

OBJECTIVE

To compare the effects of high-flow nasal cannula (HFNC) therapy and other oxygen therapy modalities on the risk of endotracheal reintubation in mechanically ventilated patients after extubation in the intensive care unit (ICU).

METHODS

An electronic search was carried out across various databases including PubMed, Embase, Ovid, Medline, Cochrane Library, Embase, VIP, and Wanfang. The objective of this search was to locate prospective randomized controlled trials that examined the effects of multiple oxygen therapy approaches on the incidence of reintubation in patients in the ICU after undergoing mechanical ventilation. The meta package in R language was used to analyze parameters adopted by the included studies such as reintubation rate, mortality rate, and length of hospital stay.

RESULTS

This study enrolled 22 articles, involving 4,160 participants, with 2,061 in the study group and 2,099 in the control group. Among these, 20 articles presented data on the reintubation rate of the patients included with an odds ratio (OR) of 0.90 (95% CI: 0.74, 1.09) for HFNC and an OR of 1.77 (95% CI: 0.93, 3.38) for HFNC in the chronic obstructive pulmonary disease (COPD) subgroup. Moreover, 10 articles assessed the incidence of respiratory failure after extubation, revealing an OR for HFNC was 0.68 (95% CI: 0.55, 0.84) using a fixed-effects model. Nine articles addressed ICU mortality, while 13 pieces of literature examined hospital mortality. HFNC showed no significant impact on either ICU mortality or hospital mortality.

CONCLUSION

HFNC therapy markedly reduces the incidence of respiratory failure in mechanically ventilated patients following extubation in the ICU. Furthermore, it specifically reduces the risk of reintubation in patients diagnosed with COPD.

Association between microcirculation in spontaneous breathing trial and extubation success

PURPOSE

This study assessed the association between changes in sublingual microcirculation after a spontaneous breathing trial (SBT) and successful extubation.

MATERIALS AND METHODS

Sublingual microcirculation was assessed using an incident dark-field video microscope before and after each SBT and before extubation. Microcirculatory parameters before the SBT, at the end of the SBT, and before extubation were compared between the successful and failed extubation groups.

RESULTS

Forty-seven patients were enrolled and analysed in this study (34 patients in the successful extubation group and 13 patients in the failed extubation group). At the end of the SBT, the weaning parameters did not differ between the two groups. However, the total small vessel density (21.2 [20.4-23.7] versus 24.9 [22.6-26.5] mm/mm²), perfused small vessel density (20.6 [18.5-21.8] versus 23.1 [20.9-25] mm/mm²), proportion of perfused small vessels (91 [87-96] versus 95 [93-98] %), and microvascular flow index (2.8 [2.7-2.9] versus 2.9 [2.9-3]) were significantly lower in the failed extubation group than in the successful extubation group. The weaning and microcirculatory parameters did not differ significantly between the two groups before the SBT.

CONCLUSIONS

More patients are required to investigate the difference between baseline microcirculation before a successful SBT and the change in microcirculation at the end of the SBT between the successful and failed extubation groups. Better sublingual microcirculatory parameters at the end of SBT and before extubation are associated with successful extubation.

Extubation failure and the use of noninvasive ventilation during the weaning process in critically ill COVID-19 patients

OBJECTIVE

To assess the outcome of extubation in COVID-19 patients and the use of noninvasive ventilation in the weaning process.

METHODS

This retrospective, observational, single-center study was conducted in COVID-19 patients aged 18 years or older who were admitted to an intensive care unit between April 2020 and December 2021, placed under mechanical ventilation for more than 48 hours and progressed to weaning. Early extubation was defined as extubation without a spontaneous breathing trial and immediate use of noninvasive ventilation after extubation. In patients who underwent a spontaneous breathing trial, noninvasive ventilation could be used as prophylactic ventilatory assistance when started immediately after extubation (prophylactic noninvasive ventilation) or as rescue therapy in cases of postextubation respiratory failure (therapeutic noninvasive ventilation). The primary outcome was extubation failure during the intensive care unit stay.

RESULTS

Three hundred eighty-four extubated patients were included. Extubation failure was observed in 107 (27.9%) patients. Forty-seven (12.2%) patients received prophylactic noninvasive ventilation. In 26 (6.8%) patients, early extubation was performed with immediate use of noninvasive ventilation. Noninvasive ventilation for the management of postextubation respiratory failure was administered to 64 (16.7%) patients.

CONCLUSION

We found that COVID-19 patients had a high rate of extubation failure. Despite the high risk of extubation failure, we observed low use of prophylactic noninvasive ventilation in these patients.

Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence

The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.

Fitness checklist model for spontaneous breathing tests in pediatrics

OBJECTIVE

To evaluate whether a model of a daily fitness checklist for spontaneous breathing tests is able to identify predictive variables of extubation failure in pediatric patients admitted to a Brazilian intensive care unit.

METHODS

This was a single-center, cross-sectional study with prospective data collection. The checklist model comprised 20 items and was applied to assess the ability to perform spontaneous breathing tests.

RESULTS

The sample consisted of 126 pediatric patients (85 males (67.5%)) on invasive mechanical ventilation, for whom 1,217 daily assessments were applied at the bedside. The weighted total score of the prediction model showed the highest discriminatory power for the spontaneous breathing test, with sensitivity and specificity indices for fitness failure of 89.7% or success of 84.6%. The cutoff point suggested by the checklist was 8, with a probability of extubation failure less than 5%. Failure increased progressively with increasing score, with a maximum probability of predicting extubation failure of 85%.

CONCLUSION

The extubation failure rate with the use of this model was within what is acceptable in the literature. The daily checklist model for the spontaneous breathing test was able to identify predictive variables of failure in the extubation process in pediatric patients.

Liberation from Mechanical Ventilation and Tracheostomy Practice in Traumatic Brain Injury

Liberating patients with severe traumatic brain injury (TBI) from mechanical ventilation is often a challenging task. These patients frequently require prolonged ventilation and have persistent alterations in the level and content of consciousness. Questions about their ability to protect their airway are common. Pulmonary complications and copious respiratory secretions are also very prevalent. Thus, it is hardly surprising that rates of extubation failure are high. This is a major problem because extubation failure is associated with a host of poor outcome measures. When the safety of an extubation attempt is uncertain, direct tracheostomy is favored by some, but there is no evidence that this practice leads to better outcomes. Current knowledge is insufficient to reliably predict extubation outcomes in TBI, and practices vary substantially across trauma centers. Yet observational studies provide relevant information that must be weighted when considering the decision to attempt extubation in patients with head injury. This review discusses available evidence on liberation from mechanical ventilation in TBI, proposes priorities for future research, and offers practical advice to guide decisions at the bedside.

Diaphragmatic Dynamics and Thickness Parameters Assessed by Ultrasonography Predict Extubation Success in Critically Ill Patients

INTRODUCTION

A frequent cause of weaning and extubation failure in critically ill mechanically ventilated patients is diaphragm muscle dysfunction. Ultrasound (US) evaluation of the diaphragm yields important data regarding its thickness (diaphragm thickening fraction [TFdi]) and its movement or excursion (diaphragmatic dynamics) that reveal the presence of diaphragmatic dysfunction.

METHODS

Cross-sectional study, which included patients older than 18 years with invasive mechanical ventilation with an expected duration of more than 48 h, in a tertiary referral center in Colombia. The excursion of the diaphragm, inspiratory and expiratory thickness, and TFdi were evaluated by US. Prevalence and use of medications were evaluated, and the association with failure in ventilatory weaning and extubation was analyzed.

RESULTS

Sixty-one patients were included. The median age and APACHE IV score were 62.42 years and 78.23, respectively. The prevalence of diaphragmatic dysfunction (assessed by excursion and TFdi) was 40.98%. The sensibility, specificity, positive predictive value, and negative predictive value for TFdi < 20% was 86%, 24%, 75%, and 40%, respectively, with an area under the receiver operating characteristic (ROC) curve of 0.6. The ultrasonographic analysis of excursion of the diaphragm, inspiratory and expiratory thickness, and TFdi (>20%) allow in its set and with normal values, predict success or failure for the extubation with an area under the ROC curve of 0.87.

CONCLUSION

Diaphragmatic dynamics and thickness parameters together assessed by ultrasonography could predict the success of extubation in critically ill patients in Colombia, based on the finding of diaphragmatic dysfunction.

Predictive parameters and model for extubation outcome in pediatric patients

Background

Prolonged mechanical ventilation is associated with significant morbidity in critically ill pediatric patients. In addition, extubation failure and deteriorating respiratory status after extubation contribute to increased morbidity. Well-prepared weaning procedures and accurate identification of at-risk patients using multimodal ventilator parameters are warranted to improve patient outcomes. This study aimed to identify and assess the diagnostic accuracy of single parameters and to develop a model that can help predict extubation outcomes.

Materials and methods

This prospective observational study was conducted at a university hospital between January 2021 and April 2022. Patients aged 1 month to 15 years who were intubated for more than 12 h and deemed clinically ready for extubation were enrolled. A weaning process with a spontaneous breathing trial (SBT), with or without minimal setting, was employed. The ventilator and patient parameters during the weaning period at 0, 30, and 120 min and right before extubation were recorded and analyzed.

Results

A total of 188 eligible patients were extubated during the study. Of them, 45 (23.9%) patients required respiratory support escalation within 48 h. Of 45, 13 (6.9%) were reintubated. The predictors of respiratory support escalation consisted of a nonminimal-setting SBT [odds ratio (OR) 2.2 (1.1, 4.6), P = 0.03], >3 ventilator days [OR 2.4 (1.2, 4.9), P = 0.02], occlusion pressure (P0.1) at 30 min ≥0.9 cmH₂O [OR 2.3 (1.1, 4.9), P = 0.03], and exhaled tidal volume per kg at 120 min ≤8 ml/kg [OR 2.2 (1.1, 4.6), P = 0.03]; all of these predictors had an area under the curve (AUC) of 0.72. A predictive scoring system to determine the probability of respiratory support escalation was developed using a nomogram.

Conclusion

The proposed predictive model, which integrated both patient and ventilator parameters, showed a modest performance level (AUC 0.72); however, it could facilitate the process of patient care.

Development and validation of a mechanical power-oriented prediction model of weaning failure in mechanically ventilated patients: a retrospective cohort study

OBJECTIVE

To develop and validate a mechanical power (MP)-oriented prediction model of weaning failure in mechanically ventilated patients.

DESIGN

A retrospective cohort study.

SETTING

Data were collected from the large US Medical Information Mart for Intensive Care-IV (MIMIC-IV) V.1.0, which integrates comprehensive clinical data from 76 540 intensive care unit (ICU) admissions from 2008 to 2019.

PARTICIPANTS

A total of 3695 patients with invasive mechanical ventilation for more than 24 hours and weaned with T-tube ventilation strategies were enrolled from the MIMIC-IV database.

PRIMARY AND SECONDARY OUTCOME

Weaning failure.

RESULTS

All eligible patients were randomised into development cohorts (n=2586, 70%) and validation cohorts (n=1109, 30%). Multivariate logistic regression analysis of the development cohort showed that positive end-expiratory pressure, dynamic lung compliance, MP, inspired oxygen concentration, length of ICU stay and invasive mechanical ventilation duration were independent predictors of weaning failure. Calibration curves showed good correlation between predicted and observed outcomes. The prediction model showed accurate discrimination in the development and validation cohorts, with area under the receiver operating characteristic curve values of 0.828 (95% CI: 0.812 to 0.844) and 0.833 (95% CI: 0.809 to 0.857), respectively. Decision curve analysis indicated that the predictive model was clinically beneficial.

CONCLUSION

The MP-oriented model of weaning failure accurately predicts the risk of weaning failure in mechanical ventilation patients and provides valuable information for clinicians making decisions on weaning.

An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach

Background

For the intensivists, accurate assessment of the ideal timing for successful weaning from the mechanical ventilation (MV) in the intensive care unit (ICU) is very challenging.

Purpose

Using artificial intelligence (AI) approach to build two-stage predictive models, namely, the try-weaning stage and weaning MV stage to determine the optimal timing of weaning from MV for ICU intubated patients, and implement into practice for assisting clinical decision making.

Methods

AI and machine learning (ML) technologies were used to establish the predictive models in the stages. Each stage comprised 11 prediction time points with 11 prediction models. Twenty-five features were used for the first-stage models while 20 features were used for the second-stage models. The optimal models for each time point were selected for further practical implementation in a digital dashboard style. Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. The electronic medical records of the intubated ICU patients of Chi Mei Medical Center (CMMC) from 2016 to 2019 were included for modeling. Models with the highest area under the receiver operating characteristic curve (AUC) were regarded as optimal models and used to develop the prediction system accordingly.

Results

A total of 5,873 cases were included in machine learning modeling for Stage 1 with the AUCs of optimal models ranging from 0.843 to 0.953. Further, 4,172 cases were included for Stage 2 with the AUCs of optimal models ranging from 0.889 to 0.944. A prediction system (dashboard) with the optimal models of the two stages was developed and deployed in the ICU setting. Respiratory care members expressed high recognition of the AI dashboard assisting ventilator weaning decisions. Also, the impact analysis of with- and without-AI assistance revealed that our AI models could shorten the patients' intubation time by 21 hours, besides gaining the benefit of substantial consistency between these two decision-making strategies.

Conclusion

We noticed that the two-stage AI prediction models could effectively and precisely predict the optimal timing to wean intubated patients in the ICU from ventilator use. This could reduce patient discomfort, improve medical quality, and lower medical costs. This AI-assisted prediction system is beneficial for clinicians to cope with a high demand for ventilators during the COVID-19 pandemic.

Incidence of extubation failure and its predictors among adult patients in intensive care unit of low-resource setting: A prospective observational study

BACKGROUND

Previous studies have found an association between various predictors and extubation failure (EF) in intensive care units (ICUs). However, this problem remains unexplored in low-resource settings, where predicting the extubation outcomes are more challenging. This study investigates the incidence of EF and its predictors among patients who received mechanical ventilation (MV).

METHODS

This is a prospective observational study of 123 patients' ≥ 18 years of age receiving MV for ≥ 48 hours and tolerated spontaneous breathing trials (SBTs) in the ICU of a low-resource setting. We collected data on the baseline characteristics and clinical profiles before and after SBTs. Patients were categorized into extubation failure (EF) and extubation success (ES) groups. Multivariate logistic regression analyses were performed to identify independent predictors for EF. A p-value < 0.05 is considered statistically significant.

RESULTS

We included 123 patients, and 42 (34.15%) had developed EF. The identified predictors for EF: Moderate to copious secretions (adjusted odds ratio [AOR]: 3.483 [95% confidence interval [CI] 1.10-11.4]), age > 60 years of age ([AOR]: 4.157 [95% CI 1.38-12.48]), and prolonged duration of MV ≥ 10 days ([AOR]: 4.77 [95% CI 1.55-14.66]).

CONCLUSION

Moderate to copious secretions, patients > 60 years of age, and prolonged duration of MV ≥ 10 days were the best predictors of EF. Based on our findings, we recommend that the identified predictors could help in the decision-making process of extubation from MV.

Mechanical power is associated with weaning outcome in critically ill mechanically ventilated patients

Several single-center studies have evaluated the predictive performance of mechanical power (MP) on weaning outcomes in prolonged invasive mechanical ventilation (IMV) patients. The relationship between MP and weaning outcomes in all IMV patients has rarely been studied. A retrospective study was conducted on MIMIC-IV patients with IMV for more than 24 h to investigate the correlation between MP and weaning outcome using logistic regression model and subgroup analysis. The discriminative ability of MP, MP normalized to dynamic lung compliance (C_dyn-MP) and MP normalized to predicted body weight (PBW-MP) on weaning outcome were evaluated by analyzing the area under the receiver-operating characteristic (AUROC). Following adjustment for confounding factors, compared with the reference group, the Odds Ratio of weaning failure in the maximum MP, C_dyn-MP, and PBW-MP groups increased to 3.33 [95%CI (2.04-4.53), P < 0.001], 3.58 [95%CI (2.27-5.56), P < 0.001] and 5.15 [95%CI (3.58-7.41), P < 0.001], respectively. The discriminative abilities of C_dyn-MP (AUROC 0.760 [95%CI 0.745-0.776]) and PBW-MP (AUROC 0.761 [95%CI 0.744-0.779]) were higher than MP (AUROC 0.745 [95%CI 0.730-0.761]) (P < 0.05). MP is associated with weaning outcomes in IMV patients and is an independent predictor of the risk of weaning failure. C_dyn-MP and PBW-MP showed higher ability in weaning failure prediction than MP.

Factores clínicos asociados a extubación fallida y a estridor laríngeo post-extubación en pacientes adultos con ventilación mecánica invasiva

Introducción. La intubación orotraqueal es un procedimiento que conlleva riesgos como la extubación fallida y el estridor laríngeo, los cuales aumentan el riesgo de morbilidad. Objetivo. Identificar los factores asociados a extubación fallida temprana (≤24 horas) y al desarrollo de estridor laríngeo post-extubación en pacientes adultos con ventilación mecánica invasiva (VMI). Materiales y métodos. Estudio de casos y controles con recolección prospectiva de información realizado en la unidad de cuidados intensivos (UCI) de un hospital de IV nivel de Bogotá, Colombia, entre abril de 2019 y febrero de 2020. Se incluyeron 180 pacientes con VMI ≥24 horas y al menos ≥1 intento de extubación: 30 casos, definidos como pacientes con extubación fallida temprana (≤24 horas a la extubación) y 150 controles. Las diferencias en las variables consideradas entre casos y controles se determinaron mediante las pruebas t de Student y Chi 2 o exacta de Fisher. Además, se realizó un análisis multivariado (modelo de regresión logística no condicional) para determinar los factores asociados con extubación fallida y estridor laríngeo post-extubación, calculando los Odds ratio (OR) con sus respectivos intervalos de confianza al 95% (IC95%). Se consideró un nivel de significancia de p<0.05. Resultados. La extubación fallida temprana y el estridor laríngeo tuvieron una prevalencia de 16.66% (n=30) y 3.89% (n=7), respectivamente. En el análisis multivariado, el antecedente de intubación (OR=4.27, IC95%=1.44-12.66), la presencia de cáncer activo (OR= 2.92, IC95%=1.08-7.90) y ser diagnosticado con neumonía (OR=2.84, IC95%=1.15-6.99) se asociaron significativamente con extubación fallida, mientras que la duración de la VMI (OR=1.53, IC95%=1.18-1.99) y el antecedente de intubación (OR=37.9, IC95%=2.22-650.8), con estridor laríngeo post-extubación. Conclusiones. Con base en los resultados aquí obtenidos, se sugiere considerar factores como antecedente de intubación previa, comorbilidad con cáncer y diagnóstico de neumonía en la estratificación de estos pacientes críticos para aumentar la probabilidad de una extubación exitosa.

Prediction Model of Extubation Outcomes in Critically Ill Patients: A Multicenter Prospective Cohort Study

Liberation from mechanical ventilation is of great importance owing to related complications from extended ventilation time. In this prospective multicenter study, we aimed to construct a versatile model for predicting extubation outcomes in critical care settings using obtainable physiological predictors. The study included patients who had been extubated after a successful 30 min spontaneous breathing trial (SBT). A multivariable logistic regression model was constructed to predict extubation outcomes (successful extubation without reintubation and uneventful extubation without reintubation or noninvasive respiratory support) using eight parameters: age, heart failure, respiratory disease, rapid shallow breathing index (RSBI), PaO2/FIO2, Glasgow Coma Scale score, fluid balance, and endotracheal suctioning episodes. Of 499 patients, 453 (90.8%) and 328 (65.7%) achieved successful and uneventful extubation, respectively. The areas under the curve for successful and uneventful extubation in the novel prediction model were 0.69 (95% confidence interval (CI), 0.62−0.77) and 0.70 (95% CI, 0.65−0.74), respectively, which were significantly higher than those in the conventional model solely using RSBI (0.58 (95% CI, 0.50−0.66) and 0.54 (95% CI, 0.49−0.60), p = 0.004 and <0.001, respectively). The model was validated using a bootstrap method, and an online application was developed for automatic calculation. Our model, which is based on a combination of generally obtainable parameters, established an accessible method for predicting extubation outcomes after a successful SBT.

Predictive Factors of Extubation Failure in COVID-19 Mechanically Ventilated Patients

Purpose: We investigated whether COVID-19 patients on mechanical ventilation (MV) had a different extubation outcome compared to non-COVID-19 patients while identifying predictive factors of extubation failure in the former. Methods: A retrospective, single-center, and observational study was done on 216 COVID-19 patients admitted to an intensive care unit (ICU) between March 2020 and March 2021, aged ≥ 18 years, in use of invasive MV for more than 24 h, which progressed to weaning. The primary outcome that was evaluated was extubation failure during ICU stay. A statistical analysis was performed to evaluate the association of patient characteristics with extubation outcome, and a Poisson regression model determined the predictive value. Results: Seventy-seven patients were extubated; the mean age was 57.2 years, 52.5% were male, and their mean APACHE II score at admission was 17.8. On average, MV duration until extubation was 8.7 ± 3.7 days, with 14.9 ± 10.1 days of ICU stay and 24.6 ± 14.0 days with COVID-19 symptoms. The rate of extubation failure (ie, the patient had to be reintubated during their ICU stay) was 22.1% (n = 17), while extubation was successful in 77.9% (n = 60) of cases. Failure was observed in only 7.8% of cases when evaluated 48 hours after extubation. The mean reintubation time was 4.28 days. After adjusting the analysis for age, sex, during of symptoms, days under MV, dialysis, and PaO2/FiO2 ratio, some parameters independently predicted extubation failure: age ≥ 66 years (APR = 5.12 [1.35-19.46]; p = 0.016), ≥ 31 days of symptoms (APR = 5.45 [0.48-62.19]; p = 0.016), and need for dialysis (APR = 5.10 [2.00-13.00]; p = 0.001), while a PaO2/FiO2 ratio >300 decreased the probability of extubation failure (APR = 0.14 [0.04-0.55]; p = 0.005). The presence of three predictors (ie, age ≥ 66 years, time of symptoms ≥ 31 days, need of dialysis, and PaO2/FiO2 ratio < 200) increased the risk of extubation failure by a factor of 23.0 (95% CI, 3.34-158.5). Conclusion: COVID-19 patients had an extubation failure risk that was almost three times higher than non-COVID-19 patients, with the extubation of the former being delayed compared to the latter. Furthermore, an age ≥ 66 years, time of symptoms ≥ 31 days, need of dialysis, and PaO2/FiO2 ratio > 200 were independent predictors for extubation failure, and the presence of three of these characteristics increased the risk of failure by a factor of 23.0.

Predictors of post-extubation stridor in patients on mechanical ventilation: a prospective observational study

The cuff leak test (CLT) has been widely accepted as a simple and noninvasive method for predicting post-extubation stridor (PES). However, its accuracy and clinical impact remain uncertain. We aimed to evaluate the reliability of CLT and to assess the impact of pre-extubation variables on the incidence of PES. A prospective observational study was performed on adult critically ill patients who required mechanical ventilation for more than 24 h. Patients were extubated after the successful spontaneous breathing trial, and CLT was conducted before extubation. Of the 191 patients studied, 26 (13.6%) were deemed positive through CLT. PES developed in 19 patients (9.9%) and resulted in a higher reintubation rate (8.1% vs. 52.6%, p < 0.001) and longer intensive care unit stay (8 [4.5-14] vs. 12 [8-30.5] days, p = 0.01) than patients without PES. The incidence of PES and post-extubation outcomes were similar in patients with both positive and negative CLT results. Compared with patients without PES, patients with PES had longer durations of endotracheal intubation and required endotracheal suctioning more frequently during the 24-h period prior to extubation. After adjusting for confounding factors, frequent endotracheal suctioning more than 15 times per day was associated with an adjusted odds ratio of 2.97 (95% confidence interval, 1.01-8.77) for PES. In conclusion, frequent endotracheal suctioning before extubation was a significant PES predictor in critically ill patients. Further investigations of its impact on the incidence of PES and patient outcomes are warranted.

Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data's variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70-0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.