Abnormal Breathing Patterns Predict Extubation Failure in Neurocritically Ill Patients

Predictive value of cough peak flow for successful extubation in mechanically ventilated patients after craniotomy: a single-centre prospective diagnostic study

OBJECTIVES

The purpose of this study was to evaluate the predictive value of the cough peak flow (CPF) for successful extubation in postcraniotomy critically ill patients.

DESIGN

This was a single-centre prospective diagnostic study.

SETTING

The study was conducted in three intensive care units (ICUs) of a teaching hospital.

PARTICIPANTS

Postcraniotomy patients who were 18 years or older, stayed in ICU for more than 24 hours and underwent mechanical ventilation for more than 24 hours were eligible for the study. Patients were excluded if one of the following was present: no extubation attempt during the ICU stay; underwent tracheostomy without extubation attempt; pregnant or lactating women; enrolled in other clinical trials; declined to participate in the study. A total of 4879 patients were screened and 1037 were eligible for the study, among whom 785 were included in the study.

OUTCOME MEASURES

CPF, including involuntary (CPF-invol) and voluntary CPF (CPF-vol), were measured before extubation. The area under the receiver operating characteristic curve (AUC) was calculated to explore the diagnostic accuracy of CPF in predicting successful extubation.

RESULTS

There were 641 successful extubation cases (81.7%). The AUC of CPF-invol for predicting successful extubation was 0.810 (95% CI 0.766 to 0.854), with a cut-off value of 63.2 L/min, a sensitivity of 87.4% and a specificity of 66.7%. For conscious patients, the AUC of CPF-invol for the prediction of successful extubation was 0.849 (95% CI 0.794 to 0.904), with a cut-off value of 63.2 L/min and the AUC of CPF-vol was 0.756 (95% CI 0.696 to 0.817), with a cut-off value of 68.2 L/min.

CONCLUSIONS

The CPF was much higher in patients with successful extubation than that in patients with failed extubation. CPF might be valuable for the prediction of extubation outcomes in postcraniotomy critically ill patients. Multicentre studies could be carried out to further validate the results of this study.

TRIAL REGISTRATION NUMBER

NCT04000997.

Impacts of SARS-CoV-2 on brain renin angiotensin system related signaling and its subsequent complications on brain: A theoretical perspective

Cellular ACE2 (cACE2), a vital component of the renin-angiotensin system (RAS), possesses catalytic activity to maintain AngII and Ang 1-7 balance, which is necessary to prevent harmful effects of AngII/AT2R and promote protective pathways of Ang (1-7)/MasR and Ang (1-7)/AT2R. Hemostasis of the brain-RAS is essential for maintaining normal central nervous system (CNS) function. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a viral disease that causes multi-organ dysfunction. SARS-CoV-2 mainly uses cACE2 to enter the cells and cause its downregulation. This, in turn, prevents the conversion of Ang II to Ang (1-7) and disrupts the normal balance of brain-RAS. Brain-RAS disturbances give rise to one of the pathological pathways in which SARS-CoV-2 suppresses neuroprotective pathways and induces inflammatory cytokines and reactive oxygen species. Finally, these impairments lead to neuroinflammation, neuronal injury, and neurological complications. In conclusion, the influence of RAS on various processes within the brain has significant implications for the neurological manifestations associated with COVID-19. These effects include sensory disturbances, such as olfactory and gustatory dysfunctions, as well as cerebrovascular and brain stem-related disorders, all of which are intertwined with disruptions in the RAS homeostasis of the brain.

Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning

Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing analysis system that automatically detects different types of breathing patterns of clinical significance. Accelerometer and gyroscopic data were collected from light-weight wireless sensors placed on the chest and abdomen of 100 normal volunteers who simulated various breathing events (central sleep apnea, coughing, obstructive sleep apnea, sighing, and yawning). We then constructed synthetic datasets by injecting annotated examples of the various patterns into segments of normal breathing. A one-dimensional convolutional neural network was implemented to detect the location of each event in each synthetic dataset and to classify it as belonging to one of the above event types. We achieved a mean F1 score of 92% for normal breathing, 87% for central sleep apnea, 72% for coughing, 51% for obstructive sleep apnea, 57% for sighing, and 63% for yawning. These results demonstrate that using deep learning to analyze chest and abdomen movement data from wearable sensors provides an unobtrusive means of monitoring the breathing pattern. This could have application in a number of critical medical situations such as detecting apneas during sleep at home and monitoring breathing events in mechanically ventilated patients in the intensive care unit.

Extubating the Neurocritical Care Patient: A Spontaneous Breathing Trial Algorithmic Approach

BACKGROUND

Delaying extubation in neurologically impaired patients otherwise ready for extubation is a source for significant morbidity, mortality, and costs. There is no consensus to suggest one spontaneous breathing trial (SBT) over another in predicting extubation success. We studied an algorithm using zero pressure support and zero positive end-expiratory pressure (ZEEP) SBT followed by 5-cm H2O pressure support and 5-cm H2O positive end-expiratory pressure (i.e., 5/5) SBT in those who failed ZEEP SBT.

METHODS

This is a retrospective analysis of intubated patients in a neurosciences intensive care unit. All eligible patients were initially challenged with ZEEP SBT. If failed, a 5/5 SBT was immediately performed. If passed either the ZEEP SBT or the subsequent 5/5 SBT, patients were liberated from mechanical ventilation.

RESULTS

In total, 108 adult patients were included. The majority of patients were successfully liberated from mechanical ventilation using ZEEP SBT alone (82.4%; p = 0.0007). Fifteen (13.8%) patients failed ZEEP SBT but immediately passed 5/5 SBT (p = 0.0005). One patient (0.93%) required reintubation. We found high sensitivity of this extubation algorithm (100; 95% CI 95.94-100%) but poor specificity (6.67; 95% CI 0.17-31.95%).

CONCLUSION

This study showed that the majority of patients could be successfully liberated from mechanical ventilation after a ZEEP SBT. In those who failed, a 5/5 SBT increased the successful liberation from mechanical ventilation.