Breathing variability-implications for anaesthesiology and intensive care

A nomogram for predicting the necessity of tracheostomy after severe acute brain injury in patients within the neurosurgery intensive care unit: A retrospective cohort study

Objective

This retrospective study was aimed to develop a predictive model for assessing the necessity of tracheostomy (TT) in patients admitted to the neurosurgery intensive care unit (NSICU).

Method

We analyzed data from 1626 NSICU patients with severe acute brain injury (SABI) who were admitted to the Department of NSICU at the Affiliated People's Hospital of Jiangsu University between January 2021 and December 2022. Data of the patients were retrospectively obtained from the clinical research data platform. The patients were randomly divided into training (70%) and testing (30%) cohorts. The least absolute shrinkage and selection operator (LASSO) regression identified the optimal predictive features. A multivariate logistic regression model was then constructed and represented by a nomogram. The efficacy of the model was evaluated based on discrimination, calibration, and clinical utility.

Results

The model highlighted six predictive variables, including the duration of NSICU stay, neurosurgery, orotracheal intubation time, Glasgow Coma Scale (GCS) score, systolic pressure, and respiration rate. Receiver operating characteristic (ROC) analysis of the nomogram yielded area under the curve (AUC) values of 0.854 (95% confidence interval [CI]: 0.822-0.886) for the training cohort and 0.865 (95% CI: 0.817-0.913) for the testing cohort, suggesting commendable differential performance. The predictions closely aligned with actual observations in both cohorts. Decision curve analysis demonstrated that the numerical model offered a favorable net clinical benefit.

Conclusion

We developed a novel predictive model to identify risk factors for TT in SABI patients within the NSICU. This model holds the potential to assist clinicians in making timely surgical decisions concerning TT.

[Exploratory study on quantitative analysis of nocturnal breathing patterns in patients with acute heart failure based on wearable devices]

Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%-104.28)% vs. 58.48% (45.34%-65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.

Variability in exercise is linked to improved age-related dysfunctions: A potential role for the constrained-disorder principle-based second-generation artificial intelligence system

Objective: Regular physical activity (PA) promotes mental and physical health. Nevertheless, inactivity is a worldwide pandemic, and methods to augment exercise benefits are required. The constrained disorder principle (CDP) characterizes biological systems based on their inherent variability. We aimed to investigate the association between intra-individual variability in PA and disability among non-athlete adults. Methods: In this retrospective analysis of the longitudinal SHARE survey, we included non-disabled adults aged >50 with at least six visits over 14 years. Self-reported PA frequency was documented bi- to triennially. Low PA intensity was defined as vigorous PA frequency less than once a week. Stable PA was described as an unchanged PA intensity in all consecutive middle observations. The primary outcome was defined as a physical limitation in everyday activities at the end of the survey. Secondary outcomes were cognitive functions, including short-term memory, long-term memory, and verbal fluency. Results: The study included 2,049 non-disabled adults with a mean age of 53 and 49.1% women. In the initially high PA intensity group, variability in PA was associated with increased physical disability prevalence (23.3% vs. 33.2%, stable vs. unstable PA ; P<0.01; adjusted P<0.01). In the initially low PA intensity group, variability was associated with a reduced physical disability (45.6% vs. 33.3%, stable vs. unstable PA ; P=0.02; adjusted P=0.03). There were no statistically significant differences in cognitive parameters between the groups. Among individuals with the same low PA intensity at the beginning and end of follow-up, variability was associated with reduced physical disability (56.9% vs. 36.5%, stable vs. unstable PA ; P=0.02; adjusted P=0.04) and improved short-term memory (score change: -0.28 vs. +0.29, stable vs. unstable PA ; P=0.05). Conclusion: Incorporating variability into PA regimens of inactive adults may enhance their physical and cognitive benefits.

Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography

Effective monitoring of respiratory disturbances during sleep requires a sensor capable of accurately capturing chest movements or airflow displacement. Gold-standard monitoring of sleep and breathing through polysomnography achieves this task through dedicated chest/abdomen bands, thermistors, and nasal flow sensors, and more detailed physiology, evaluations via a nasal mask, pneumotachograph, and airway pressure sensors. However, these measurement approaches can be invasive and time-consuming to perform and analyze. This work compares the performance of a non-invasive wearable stretchable morphic sensor, which does not require direct skin contact, embedded in a t-shirt worn by 32 volunteer participants (26 males, 6 females) with sleep-disordered breathing who performed a detailed, overnight in-laboratory sleep study. Direct comparison of computed respiratory parameters from morphic sensors versus traditional polysomnography had approximately 95% (95 ± 0.7) accuracy. These findings confirm that novel wearable morphic sensors provide a viable alternative to non-invasively and simultaneously capture respiratory rate and chest and abdominal motions.

Multivariable Regression Model to Estimate Tidal Volume for Different Respiratory Patterns

Respiratory patterns present great variability, both in healthy subjects and in patients with different diseases and forms of nasal, oral, superficial or deep breathing. The analysis of this variability depends, among others, on the device used to record the signals that describe these patterns. In this study, we propose multivariable regression models to estimate tidal volume (VT) considering different breathing patterns. Twenty-three healthy volunteers underwent continuous multisensor recordings considering different modes of breathing. Respiratory flow and volume signals were recorded with a pneumotachograph and thoracic and abdominal respiratory inductive plethysmographic bands. Several respiratory parameters were extracted from the volume signals, such as inspiratory and expiratory areas (Areains, Areaexp), maximum volume relative to the cycle start and end (VTins, VTexp), inspiratory and expiratory time (Tins, Texp), cycle duration (Ttot), and normalized parameters of clinical interest. The parameters with the greatest individual predictive power were combined using multivariable models to estimate VT. Their performance were quantified in terms of determination coefficient (R2), relative error (ER) and interquartile range (IQR). Using only three parameters, the results obtained for the thoracic band (VTexp, Ttot, Areaexp) were better than those obtained from the abdominal band (VTexp, Tins, Areains) with R2 = 0.94 (IQR: 0.07); ER = 6.99 (IQR: 6.12) vs R2 = 0.91 (IQR: 0.09), ER = 8.70 (IQR: 4.62). Overall performance increased to R2 = 0.97 (IQR: 0.02) and ER = 4.60 (IQR: 3.68) when parameters from the different bands were combined, further improving when was applied to segments with different inspiration-expiration patterns. In particular, the nose-nose ER = 1.39 (IQR: 0.73), nose-mouth ER = 2.11 (IQR: 1.23) and mouth-mouth ER = 2.29 (IQR: 1.44) patterns showed the best results compared to those obtained for basal, shallow and deep breathing.Clinical relevance- Respiratory pattern variability can be described using multivariable regression model for tidal volume.

Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters

Background

Successful weaning from mechanical ventilation is important for patients admitted to intensive care units. However, models for predicting real-time weaning outcomes remain inadequate. Therefore, this study aimed to develop a machine-learning model for predicting successful extubation only using time-series ventilator-derived parameters with good accuracy.

Methods

Patients with mechanical ventilation admitted to the Yuanlin Christian Hospital in Taiwan between August 2015 and November 2020 were retrospectively included. A dataset with ventilator-derived parameters was obtained before extubation. Recursive feature elimination was applied to select the most important features. Machine-learning models of logistic regression, random forest (RF), and support vector machine were adopted to predict extubation outcomes. In addition, the synthetic minority oversampling technique (SMOTE) was employed to address the data imbalance problem. The area under the receiver operating characteristic (AUC), F1 score, and accuracy, along with the 10-fold cross-validation, were used to evaluate prediction performance.

Results

In this study, 233 patients were included, of whom 28 (12.0%) failed extubation. The six ventilatory variables per 180 s dataset had optimal feature importance. RF exhibited better performance than the others, with an AUC value of 0.976 (95% confidence interval [CI], 0.975-0.976), accuracy of 94.0% (95% CI, 93.8-94.3%), and an F1 score of 95.8% (95% CI, 95.7-96.0%). The difference in performance between the RF and the original and SMOTE datasets was small.

Conclusion

The RF model demonstrated a good performance in predicting successful extubation in mechanically ventilated patients. This algorithm made a precise real-time extubation outcome prediction for patients at different time points.

Phrenic nerve stimulation mitigates hippocampal and brainstem inflammation in an ARDS model

Rationale: In porcine healthy-lung and moderate acute respiratory distress syndrome (ARDS) models, groups that received phrenic nerve stimulation (PNS) with mechanical ventilation (MV) showed lower hippocampal apoptosis, and microglia and astrocyte percentages than MV alone. Objectives: Explore whether PNS in combination with MV for 12 h leads to differences in hippocampal and brainstem tissue concentrations of inflammatory and synaptic markers compared to MV-only animals. Methods: Compare tissue concentrations of inflammatory markers (IL-1α, IL-1β, IL-6, IL-8, IL-10, IFN-γ, TNFα and GM-CSF), pre-synaptic markers (synapsin and synaptophysin) and post-synaptic markers (disc-large-homolog 4, N-methyl-D-aspartate receptors 2A and 2B) in the hippocampus and brainstem in three groups of mechanically ventilated pigs with injured lungs: MV only (MV), MV plus PNS every other breath (MV + PNS50%), and MV plus PNS every breath (MV + PNS100%). MV settings in volume control were tidal volume 8 ml/kg, and positive end-expiratory pressure 5 cmH₂O. Moderate ARDS was achieved by infusing oleic acid into the pulmonary artery. Measurements and Main Results: Hippocampal concentrations of GM-CSF, N-methyl-D-aspartate receptor 2B, and synaptophysin were greater in the MV + PNS100% group compared to the MV group, p = 0.0199, p = 0.0175, and p = 0.0479, respectively. The MV + PNS100% group had lower brainstem concentrations of IL-1β, and IL-8 than the MV group, p = 0.0194, and p = 0.0319, respectively; and greater brainstem concentrations of IFN-γ and N-methyl-D-aspartate receptor 2A than the MV group, p = 0.0329, and p = 0.0125, respectively. Conclusion: In a moderate-ARDS porcine model, MV is associated with hippocampal and brainstem inflammation, and phrenic nerve stimulation on every breath mitigates that inflammation.

Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks

The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality.

Comprehensive breathing variability indices enhance the prediction of extubation failure in patients on mechanical ventilation

BACKGROUND AND OBJECTIVE

Despite the numerous studies on extubation readiness assessment for patients who are invasively ventilated in the intensive care unit, a 10-15% extubation failure rate persists. Although breathing variability has been proposed as a potential predictor of extubation failure, it is mainly assessed using simple statistical metrics applied to basic respiratory parameters. Therefore, the complex pattern of breathing variability conveyed by continuous ventilation waveforms may be underexplored.

METHODS

Here, we aimed to develop novel breathing variability indices to predict extubation failure among invasively ventilated patients. First, breath-to-breath basic and comprehensive respiratory parameters were computed from continuous ventilation waveforms 1 h before extubation. Subsequently, the basic and advanced variability methods were applied to the respiratory parameter sequences to derive comprehensive breathing variability indices, and their role in predicting extubation failure was assessed. Finally, after reducing the feature dimensionality using the forward search method, the combined effect of the indices was evaluated by inputting them into the machine learning models, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost).

RESULTS

The coefficient of variation of the dynamic mechanical power per breath (CV-MPd[J/breath]) exhibited the highest area under the receiver operating characteristic curve (AUC) of 0.777 among the individual indices. Furthermore, the XGBoost model obtained the best AUC (0.902) by combining multiple selected variability indices.

CONCLUSIONS

These results suggest that the proposed novel breathing variability indices can improve extubation failure prediction in invasively ventilated patients.

Department of Medicine 2040: Implementing a Constrained Disorder Principle-Based Second-Generation Artificial Intelligence System for Improved Patient Outcomes in the Department of Internal Medicine

Internal medicine departments must adapt their structures and methods of operation to accommodate changing healthcare systems. The present paper discusses some challenges departments of medicine face as healthcare providers and consumers continue to change. A co-pilot model is described in this article for augmenting physicians rather than replacing them. The paper presents the co-pilot models to improve diagnoses, treatments, and monitoring. Personalized variability patterns based on the constrained-disorder principle (CDP) are described to assess chronic therapies' effectiveness in improving patient outcomes. Based on CDP-based enhanced digital twins, this paper presents personalized treatments and follow-ups that improve diagnosis accuracy and therapy outcomes. While maintaining their professional values, departments of internal medicine must respond proactively to the needs of patients and healthcare systems. To meet the needs of patients and healthcare systems, they must strive for medical professionalism and adapt to the dynamic environment.

Step-adaptive sound guidance enhances locomotor-respiratory coupling in novice female runners: A proof-of-concept study

Introduction

Many runners struggle to find a rhythm during running. This may be because 20-40% of runners experience unexplained, unpleasant breathlessness at exercise onset. Locomotor-respiratory coupling (LRC), a synchronization phenomenon in which the breath is precisely timed with the steps, may provide metabolic or perceptual benefits to address these limitations. It can also be consciously performed. Hence, we developed a custom smartphone application to provide real-time LRC guidance based on individual step rate.

Methods

Sixteen novice-intermediate female runners completed two control runs outdoors and indoors at a self-selected speed with auditory step rate feedback. Then, the runs were replicated with individualized breath guidance at specific LRC ratios. Hexoskin smart shirts were worn and analyzed with custom algorithms to estimate continuous LRC frequency and phase coupling.

Results

LRC guidance led to a large significant increase in frequency coupling outdoor from 26.3 ± 10.7 (control) to 69.9 ± 20.0 % (LRC) "attached". There were similarly large differences in phase coupling between paired trials, and LRC adherence was stronger for the indoor treadmill runs versus outdoors. There was large inter-individual variability in running pace, preferred LRC ratio, and instruction adherence metrics.

Discussion

Our approach demonstrates how personalized, step-adaptive sound guidance can be used to support this breathing strategy in novice runners. Subsequent investigations should evaluate the skill learning of LRC on a longer time basis to effectively clarify its risks and advantages.

Temporal variations in the pattern of breathing: techniques, sources, and applications to translational sciences

The breathing process possesses a complex variability caused in part by the respiratory central pattern generator in the brainstem; however, it also arises from chemical and mechanical feedback control loops, network reorganization and network sharing with nonrespiratory motor acts, as well as inputs from cortical and subcortical systems. The notion that respiratory fluctuations contain hidden information has prompted scientists to decipher respiratory signals to better understand the fundamental mechanisms of respiratory pattern generation, interactions with emotion, influences on the cortical neuronal networks associated with cognition, and changes in variability in healthy and disease-carrying individuals. Respiration can be used to express and control emotion. Furthermore, respiration appears to organize brain-wide network oscillations via cross-frequency coupling, optimizing cognitive performance. With the aid of information theory-based techniques and machine learning, the hidden information can be translated into a form usable in clinical practice for diagnosis, emotion recognition, and mental conditioning.

Automated evaluation of respiratory signals to provide insight into respiratory drive

The diaphragm muscle (DIAm) is the primary inspiratory muscle in mammals and is highly active throughout life displaying rhythmic activity. The repetitive activation of the DIAm (and of other muscles driven by central pattern generator activity) presents an opportunity to analyze these physiological data on a per-event basis rather than pooled on a per-subject basis. The present study highlights the development and implementation of a graphical user interface-based algorithm using an analysis of critical points to detect the onsets and offsets of individual respiratory events across a range of motor behaviors, thus facilitating analyses of within-subject variability. The algorithm is designed to be robust regardless of the signal type (e.g., EMG or transdiaphragmatic pressure). Our findings suggest that this approach may be particularly beneficial in reducing animal numbers in certain types of studies, for assessments of perturbation studies where the effects are relatively small but potentially physiologically meaningful, and for analyses of respiratory variability.

Breathing variability during propofol/remifentanil procedural sedation with a single additional dose of midazolam or s-ketamine: a prospective observational study

Purpose

Regulation of spontaneous breathing is highly complex and may be influenced by drugs administered during the perioperative period. Because of their different pharmacological properties we hypothesized that midazolam and s-ketamine exert different effects on the variability of minute ventilation (MV), tidal volume (TV) and respiratory rate (RR).

Methods

Patients undergoing procedural sedation (PSA) with propofol and remifentanil received a single dose of midazolam (1–3 mg, n = 10) or s-ketamine (10–25 mg, n = 10). We used non-invasive impedance-based respiratory volume monitoring to record RR as well as changes in TV and MV. Variability of these three parameters was calculated as coefficients of variation.

Results

TV and MV decreased during PSA to a comparable extent in both groups, whereas there was no significant change in RR. In line with our hypothesis we observed marked differences in breathing variability. The variability of MV (– 47.5% ± 24.8%, p = 0.011), TV (– 42.1% ± 30.2%, p = 0.003), and RR (– 28.5% ± 29.3%, p = 0.011) was significantly reduced in patients receiving midazolam. In contrast, variability remained unchanged in patients receiving s-ketamine (MV + 16% ± 45.2%, p = 0.182; TV +12% ± 47.7%, p = 0.390; RR +39% ± 65.2%, p = 0.129). After termination of PSA breathing variables returned to baseline values.

Conclusions

While midazolam reduces respiratory variability in spontaneously breathing patients undergoing procedural sedation, s-ketamine preserves variability suggesting different effects on the regulation of spontaneous breathing.