Changes of heart rate variability during ventilator weaning

Central venous pressure waveform analysis during sleep/rest: a novel approach to enhance intensive care unit post-extubation monitoring of extubation failure

This pilot study aimed to investigate the relation between cardio-respiratory parameters derived from Central Venous Pressure (CVP) waveform and Extubation Failure (EF) in mechanically ventilated ICU patients during post-extubation period. This study also proposes a new methodology for analysing these parameters during rest/sleep periods to try to improve the identification of EF. We conducted a prospective observational study, computing CVP-derived parameters including breathing effort, spectral analyses, and entropy in twenty critically ill patients post-extubation. The Dynamic Warping Index (DWi) was calculated from the respiratory component extracted from the CVP signal to identify rest/sleep states. The obtained parameters from EF patients and patients without EF were compared both during arbitrary periods and during reduced DWi (rest/sleep). We have analysed data from twenty patients of which nine experienced EF. Our findings may suggest significantly increased respiratory effort in EF patients compared to those successfully extubated. Our study also suggests the occurrence of significant change in the frequency dispersion of the cardiac signal component. We also identified a possible improvement in the differentiation between the two groups of patients when assessed during rest/sleep states. Although with caveats regarding the sample size, the results of this pilot study may suggest that CVP-derived cardio-respiratory parameters are valuable for monitoring respiratory failure during post-extubation, which could aid in managing non-invasive interventions and possibly reduce the incidence of EF. Our findings also indicate the possible importance of considering sleep/rest state when assessing cardio-respiratory parameters, which could enhance respiratory failure detection/monitoring.

Occurrence of Dysrhythmias During Ventilatory Weaning and Its Association With Length of Mechanical Ventilation and In-hospital Complications

BACKGROUND

The occurrence of cardiac dysrhythmias during ventilatory weaning and its impact on the length of ventilation and occurrence of complications have not been systematically investigated.

OBJECTIVES

The aim of this study was to evaluate the association between cardiac dysrhythmias during weaning and the duration of ventilation and complications during ventilator weaning.

METHOD

Data on the length of ventilation and complications were collected from the medical records of 30 mechanically ventilated patients. Continuous electrocardiographic recordings were collected at baseline and during the initial weaning trial. Multiple regression analysis was used to evaluate the association between dysrhythmias and length of ventilation. The relationship between prolonged ventilation and complications was assessed using the χ2 analysis.

RESULTS

Supraventricular ectopic beats during weaning positively predicted the length of ventilation (P < .01). Prolonged ventilation (>7 days) is associated with the occurrence of renal insufficiency and septicemia.

CONCLUSION

The association between the occurrence of supraventricular ectopic beats during ventilator weaning and the length of ventilation requires further evaluation and tailored management to improve patient outcomes.

Ventilator Weaning and Extubation

Increasing evidence supports specific approaches to liberate patients from invasive ventilation including the use of liberation protocols, inspiratory assistance during spontaneous breathing trials (SBTs), early extubation of patients with chronic obstructive pulmonary disease to noninvasive ventilation, and prophylactic use of noninvasive support strategies after extubation. Additional research is needed to elucidate the best criteria to identify patients who are ready to undergo an SBT and to inform optimal screening frequency, the best SBT technique and duration, extubation assessments, and extubation decision-making. Additional clarity is also needed regarding the optimal timing to measure and report extubation success.

Defining predictors for successful mechanical ventilation weaning, using a data-mining process and artificial intelligence

Mechanical ventilation weaning within intensive care units (ICU) is a difficult process, while crucial when considering its impact on morbidity and mortality. Failed extubation and prolonged mechanical ventilation both carry a significant risk of adverse events. We aimed to determine predictive factors of extubation success using data-mining and artificial intelligence. A prospective physiological and biomedical signal data warehousing project. A 21-beds medical Intensive Care Unit of a University Hospital. Adult patients undergoing weaning from mechanical ventilation. Hemodynamic and respiratory parameters of mechanically ventilated patients were prospectively collected and combined with clinical outcome data. One hundred and eight patients were included, for 135 spontaneous breathing trials (SBT) allowing to identify physiological parameters either measured before or during the trial and considered as predictive for extubation success. The Early-Warning Score Oxygen (EWSO2) enables to discriminate patients deemed to succeed extubation, at 72-h and 7-days. Cut-off values for EWSO2 (AUC = 0.80; Se = 0.75; Sp = 0.76), mean arterial pressure and heart-rate variability parameters were determined. A predictive model for extubation success was established including body-mass index (BMI) on inclusion, occlusion pressure at 0,1 s. (P0.1) and heart-rate analysis parameters (LF/HF) both measured before SBT, and heart rate during SBT (global performance 62%; 83%). The data-mining process enabled to detect independent predictive factors for extubation success and to develop a dynamic predictive model using artificial intelligence. Such predictive tools may help clinicians to better discriminate patients deemed to succeed extubation and thus improve clinical performance.

Comment on: "heart rate variability as a predictor of mechanical ventilation weaning outcomes": Letter to the Editor

None.

Autonomic markers of extubation readiness in premature infants

BACKGROUND

In premature infants, extubation failure is common and difficult to predict. Heart rate variability (HRV) is a marker of autonomic tone. Our aim is to test the hypothesis that autonomic impairment is associated with extubation readiness.

METHODS

Retrospective study of 89 infants <28 weeks. HRV metrics 24 h prior to extubation were compared for those with and without extubation success within 72 h. Receiver-operating curve analysis was conducted to determine the predictive ability of each metric, and a predictive model was created.

RESULTS

Seventy-three percent were successfully extubated. The success group had significantly lower oxygen requirement, higher sympathetic HRV metrics, and a lower parasympathetic HRV metric. α₁ (measure of autocorrelation, related to sympathetic tone) was the best predictor of success-area under the curve (AUC) of .73 (p = 0.001), and incorporated into a predictive model had an AUC of 0.81 (p < 0.0001)-sensitivity of 81% and specificity of 78%.

CONCLUSIONS

Extubation success is associated with HRV. We show an autonomic imbalance with low sympathetic and elevated parasympathetic tone in those who failed. α₁, a marker of sympathetic tone, was noted to be the best predictor of extubation success especially when incorporated into a clinical model.

IMPACT

This article depicts autonomic markers predictive of extubation success. We depict an autonomic imbalance in those who fail extubation with heightened parasympathetic and blunted sympathetic signal. We describe a predictive model for extubation success with a sensitivity of 81% and specificity of 78%.

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.

Autonomic heart rate modulation in patients with coronavirus disease 2019 in mechanical ventilation

BACKGROUND

Patients with coronavirus disease 2019 on automatic mechanical ventilation have greater heart rate modulation with greater parasympathetic modulation.

OBJECTIVE

To analyze the autonomic modulation of heart rate in critically ill patients with coronavirus disease 2019 on invasive mechanical ventilation.

METHODS

A cross-section study was carried out with 36 individuals divided into two groups. The control group included patients of both genders, in orotracheal intubation with invasive mechanical ventilation under controlled assisted mode, hospitalized in the intensive care unit for another 24 h. In the non-COVID group, patients diagnosed with coronavirus disease 2019 in the same condition mentioned in the control group.

RESULTS

There was a significant increase in heart rate variability (standard deviation of all normal RR intervals recorded at an interval of time; p=0.001; triangular interpolation histogram of RR intervals; p=0.048; and SD2; p=0.014) in the coronavirus disease group compared to the non-COVID group. Successively, the parameters that demonstrate parasympathetic modulation are shown to be higher in the group of patients with coronavirus disease 2019 (root mean square of the square of differences between adjacent normal RR intervals in an interval of time; p<0.001; pNN50; p<0.001; SD1; p=0.002; and high frequency; p=0.022).

CONCLUSIONS

There was a greater autonomic modulation of heart rate with a greater parasympathetic modulation in patients with coronavirus disease 2019 on mechanical ventilation.

Heart rate variability as a predictor of mechanical ventilation weaning outcomes

BACKGROUND

Delays in the mechanical ventilation (MV) weaning process increase mortality. The spontaneous breathing test is the gold standard to assess weaning and extubation success, although it has approximately 85% accuracy. Studies have demonstrated a correlation between decreased heart rate variability (HRV) and weaning failure.

OBJECTIVES

To assess the usefulness of HRV as a predictor of MV weaning outcomes.

METHODS

Cross-sectional analytical study in adults of both sexes on MV in intensive care unit (ICU) stay. Patients were divided into weaning success and failure groups. Clinical data were collected, and HRV records were obtained with a heart rate monitor.

RESULTS

The study included 68 individuals - 91.1% in the weaning success group and 8.9% in the failure group. All HRV indices analyzed in both groups were lower than the reference values. No statistical difference was found in the mean RR interval (RRi), the standard deviation of the NN interval (SDNN), and the square root of the mean squared differences of successive NN intervals (RMSSD) between the groups. The weaning failure group had a significant increase in LF (41 vs. 69.4) and LF/HF ratio (0.685 vs. 2.6) and a significant decrease in HF (58.85 vs. 30.2).

CONCLUSIONS

HRV measure with spectral analysis can be a good predictor of MV weaning failure. Integrating this assessment tool in ICU to predict weaning outcomes could provide more precise prognoses and more adequate assistance quality.

Early heart rate variability evaluation enables to predict ICU patients' outcome

Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has been proposed in various types of patients. We attempted to evaluate the best determinants of such variation in survival prediction using a physiological data-warehousing program. Plethysmogram tracings (PPG) were recorded at 75 Hz from the standard monitoring system, for a 2 h period, during the 24 h following ICU admission. Physiological data recording was associated with metadata collection. HRV was derived from PPG in either the temporal and non-linear domains. 540 consecutive patients were recorded. A lower LF/HF, SD2/SD1 ratios and Shannon entropy values on admission were associated with a higher ICU mortality. SpO2/FiO2 ratio and HRV parameters (LF/HF and Shannon entropy) were independent correlated with mortality in the multivariate analysis. Machine-learning using neural network (kNN) enabled to determine a simple decision tree combining the three best determinants (SDNN, Shannon Entropy, SD2/SD1 ratio) of a composite outcome index. HRV measured on admission enables to predict outcome in the ICU or at Day-28, independently of the admission diagnosis, treatment and mechanical ventilation requirement.

Trial registration: ClinicalTrials.gov identifier NCT02893462.

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.

Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients' readiness, there is still around 15-20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation -being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

Predicting extubation outcomes using the Heart Rate Characteristics index in preterm infants: a cohort study

A strategy of early extubation to noninvasive respiratory support in preterm infants could be boosted by the availability of a decision support tool for clinicians. Using the Heart Rate Characteristics index (HRCi) with clinical parameters, we derived and validated predictive models for extubation readiness and success.Peri-extubation demographic, clinical and HRCi data for up to 96 h were collected from mechanically ventilated infants in the control arm of a randomised trial involving eight neonatal centres, where clinicians were blinded to the HRCi scores. The data were used to produce a multivariable regression model for the probability of subsequent re-intubation. Additionally, a survival model was produced to estimate the probability of re-intubation in the period after extubation.Of the 577 eligible infants, data from 397 infants (69%) were used to derive the pre-extubation model and 180 infants (31%) for validation. The model was also fitted and validated using all combinations of training (five centres) and test (three centres) centres. The estimated probability for the validation episodes showed discrimination with high statistical significance, with an area under the curve of 0.72 (95% CI 0.71-0.74; p<0.001). Data from all infants were used to derive models of the predictive instantaneous hazard of re-intubation adjusted for clinical parameters.Predictive models of extubation readiness and success in real-time can be derived using physiological and clinical variables. The models from our analyses can be accessed using an online tool available at www.heroscore.com/extubation, and have the potential to inform and supplement the confidence of the clinician considering extubation in preterm infants.

Vagal withdrawal and psychological distress during ventilator weaning and the related outcomes

OBJECTIVE

This study investigated the associations between changes in autonomic nervous system (ANS) function, psychological status during the mechanical ventilation (MV) weaning process, and weaning outcomes.

METHODS

In this prospective study, we recruited 67 patients receiving MV for >24h at a medical center in northern Taiwan. Patients' ANS function, represented by heart rate variability (HRV), the rapid shallow breathing index (RSBI), anxiety, fear, and dyspnea, was repeatedly measured 10min before and 30min after undergoing a weaning trial. Forty-nine patients capable of sustaining a 2-h weaning trial were successfully weaned.

RESULTS

Compared with the failed group, the success group showed significantly smaller decreases in high-frequency HRV (HRV-HF) and smaller increases in RSBI (per 10 breaths/min/L), fear, dyspnea, and anxiety in response to the weaning trial (odds ratio [OR]=2.19, 0.81, 0.69, 0.66, and 0.77, respectively; p<0.05). Multivariate analyses revealed that low-frequency HRV before weaning (OR=2.32; 95% confidence interval [CI]=1.13-4.78, p=0.02), changes in HRV-HF (OR=3.33; 95% CI=1.18-9.44, p=0.02), and psychological fear during the weaning process (OR=0.50; 95% CI=0.27-0.92, p=0.03) were three independent factors associated with 2-h T-piece weaning success.

CONCLUSIONS

ANS responses and psychological distress during weaning were associated with T-piece weaning outcomes and may reflect the need for future studies to utilize these factors to guide weaning processes and examine their impact on outcomes.

Non-contact measurement of heart response reflected in human eye

This study aims to develop a non-contact measurement technique for cardiac response that uses an infrared image of the patient's pupil. The pupil contraction rhythm is related to the autonomic balance and major organs (such as the heart) via a neural pathway. In this study, the response of the heart was determined by analyzing the pupillary rhythm based on the harmonic frequencies between them. Seventy undergraduate volunteers of both genders, (35 females and 35 males), with ages ranging between 20 and 30years (mean: 24.52±0.64years) were asked to conduct a simple conversation, perform slight movements, and experience sound stimuli to evoke arousal, relaxation, happiness, sadness, or a neutral mood in this experiment. Electrocardiograms and pupil images were measured and analyzed, and the harmonic frequencies were identified to determine the relational response. The cardiac time (heart rate (HR), beats per minute (BPM), the standard deviation of the normal-to-normal (NN) intervals (SDNN), the mean squared differences in the successive N-N intervals (rMSSD), and the percentage difference between adjacent normal interbeat (R-R) intervals>50 (pNN50)) and frequency (very low frequency (VLF), low frequency (LF), high frequency (HF), VLF/HF, and LF/HF) parameters were also observed with regard to the effects of the movement, conversation, and physiological state. The cardiac response was stable, showing less significance than the effects of the three conditions. Therefore, multi-cardiac measurements were successfully obtained from a simple, low-cost, non-invasive, and non-contact data acquisition method in this study.

Prediction of Extubation readiness in extremely preterm infants by the automated analysis of cardiorespiratory behavior: study protocol

Background

Extremely preterm infants (≤ 28 weeks gestation) commonly require endotracheal intubation and mechanical ventilation (MV) to maintain adequate oxygenation and gas exchange. Given that MV is independently associated with important adverse outcomes, efforts should be made to limit its duration. However, current methods for determining extubation readiness are inaccurate and a significant number of infants fail extubation and require reintubation, an intervention that may be associated with increased morbidities. A variety of objective measures have been proposed to better define the optimal time for extubation, but none have proven clinically useful. In a pilot study, investigators from this group have shown promising results from sophisticated, automated analyses of cardiorespiratory signals as a predictor of extubation readiness. The aim of this study is to develop an automated predictor of extubation readiness using a combination of clinical tools along with novel and automated measures of cardiorespiratory behavior, to assist clinicians in determining when extremely preterm infants are ready for extubation.

Methods

In this prospective, multicenter observational study, cardiorespiratory signals will be recorded from 250 eligible extremely preterm infants with birth weights ≤1250 g immediately prior to their first planned extubation. Automated signal analysis algorithms will compute a variety of metrics for each infant, and machine learning methods will then be used to find the optimal combination of these metrics together with clinical variables that provide the best overall prediction of extubation readiness. Using these results, investigators will develop an Automated system for Prediction of EXtubation (APEX) readiness that will integrate the software for data acquisition, signal analysis, and outcome prediction into a single application suitable for use by medical personnel in the neonatal intensive care unit. The performance of APEX will later be prospectively validated in 50 additional infants.

Discussion

The results of this research will provide the quantitative evidence needed to assist clinicians in determining when to extubate a preterm infant with the highest probability of success, and could produce significant improvements in extubation outcomes in this population.

Trial registration

Clinicaltrials.gov identifier: NCT01909947. Registered on July 17 2013.

Trial sponsor: Canadian Institutes of Health Research (CIHR).

Electronic supplementary material

The online version of this article (doi:10.1186/s12887-017-0911-z) contains supplementary material, which is available to authorized users.

Heart rate variability in critical care medicine: a systematic review

BACKGROUND

Heart rate variability (HRV) has been used to assess cardiac autonomic activity in critically ill patients, driven by translational and biomarker research agendas. Several clinical and technical factors can interfere with the measurement and/or interpretation of HRV. We systematically evaluated how HRV parameters are acquired/processed in critical care medicine.

METHODS

PubMed, MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (1996-2016) were searched for cohort or case-control clinical studies of adult (>18 years) critically ill patients using heart variability analysis. Duplicate independent review and data abstraction. Study quality was assessed using two independent approaches: Newcastle-Ottowa scale and Downs and Black instrument. Conduct of studies was assessed in three categories: (1) study design and objectives, (2) procedures for measurement, processing and reporting of HRV, and (3) reporting of relevant confounding factors.

RESULTS

Our search identified 31/271 eligible studies that enrolled 2090 critically ill patients. A minority of studies (15; 48%) reported both frequency and time domain HRV data, with non-normally distributed, wide ranges of values that were indistinguishable from other (non-critically ill) disease states. Significant heterogeneity in HRV measurement protocols was observed between studies; lack of adjustment for various confounders known to affect cardiac autonomic regulation was common. Comparator groups were often omitted (n = 12; 39%). This precluded meaningful meta-analysis.

CONCLUSIONS

Marked differences in methodology prevent meaningful comparisons of HRV parameters between studies. A standardised set of consensus criteria relevant to critical care medicine are required to exploit advances in translational autonomic physiology.

Diaphragm ultrasound as a new method to predict extubation outcome in mechanically ventilated patients

AIM

To evaluate role of diaphragmatic thickening and excursion, assessed ultrasonographically, in predicting extubation outcome.

METHODS

Fifty-four patients who successfully passed spontaneous breathing trial (SBT) were enrolled. They were assessed by ultrasound during SBT evaluating diaphragmatic excursion, diaphragmatic thickness (Tdi) at end inspiration, at end expiration and diaphragmatic thickness fraction (DTF%). Simultaneously traditional weaning parameters were recorded. Patients were followed up for 48h after extubation.

RESULTS

Out of 54 included patients, 14 (25.9%) failed extubation. Diaphragmatic excursion, Tdi at end inspiration, at end expiration and DTF% were significantly higher in the successful group compared to those who failed extubation (p<0.05). Cutoff values of diaphragmatic measures associated with successful extubation were ≥10.5mm for diaphragmatic excursion, ≥21mm for Tdi at end inspiration, ≥10.5mm for Tdi at end expiration, ≥34.2% for DTF% giving 87.5%, 77.5%, 80% and 90% sensitivity respectively and 71.5%, 86.6%, 50% and 64.3% specificity respectively. Combining diaphragmatic excursion ≥10.5mm and Tdi at end inspiration ≥21mm decreased sensitivity to 64.9% but increased specificity to 100%. Rapid shallow breathing index (RSBI) <105 had 90% sensitivity but 18.7% specificity.

CONCLUSION

Ultrasound evaluation of diaphragmatic excursion and thickness at end inspiration could be a good predictor of extubation outcome in patients who passed SBT. It is recommended to consider the use of these parameters with RSBI consequently to improve extubation outcome.

Spontaneous breathing trial in T-tube negatively impact on autonomic modulation of heart rate compared with pressure support in critically ill patients

INTRODUCTION

Spontaneous breathing with a conventional T-piece (TT) connected to the tracheal tube orotraqueal has been frequently used in clinical setting to weaning of mechanical ventilation (MV), when compared with pressure support ventilation (PSV). However, the acute effects of spontaneous breathing with TT versus PSV on autonomic function assessed through heart rate variability (HRV) have not been fully elucidated.

OBJECTIVE

The purpose of this study was to examine the acute effects of spontaneous breathing in TT vs PSV in critically ill patients.

METHOD

Twenty-one patients who had received MV for ≥ 48 h and who met the study inclusion criteria for weaning were assessed. Eligible patients were randomized to TT and PSV. Cardiorespiratory responses (respiratory rate -ƒ, tidal volume-V_T , mean blood pressure (MBP) and diastolic blood pressure (DBP), end tidal dioxide carbone (P_ET CO₂ ), peripheral oxygen saturation (SpO₂ ) and HRV indices in frequency domain (low-LF, high frequency (HF) and LF/HF ratio were evaluated.

RESULTS

TT increased ƒ (20 ± 5 vs 25 ± 4 breaths/min, P<0.05), MBP (90 ± 14 vs 94 ± 18 mmHg, P<0.05), HR (90 ± 17 vs 96 ± 12 beats/min, P<0.05), P_ET CO₂ (33 ± 8 vs 48 ± 10 mmHg, P<0.05) and reduced SpO₂ (98 ± 1.6 vs 96 ± 1.6%, P<0.05). In addition, LF increased (47 ± 18 vs 38 ± 12 nu, P<0.05) and HF reduced (29 ± 13 vs 32 ± 16 nu, P<0.05), resulting in higher LF/HF ratio (1.62 ± 2 vs 1.18 ± 1, P<0.05) during TT. Conversely, V_T increased with PSV (0.58 ± 0.16 vs 0.50 ± 0.15 L, P<0.05) compared with TT.

CONCLUSION

Acute effects of TT mode may be closely linked to cardiorespiratory mismatches and cardiac autonomic imbalance in critically ill patients.

Classification of patients undergoing weaning from mechanical ventilation using the coherence between heart rate variability and respiratory flow signal

Weaning from mechanical ventilation is still one of the most challenging problems in intensive care. Unnecessary delays in discontinuation and weaning trials that are undertaken too early are both undesirable. This study investigated the contribution of spectral signals of heart rate variability (HRV) and respiratory flow, and their coherence to classifying patients on weaning process from mechanical ventilation. A total of 121 candidates for weaning, undergoing spontaneous breathing tests, were analyzed: 73 were successfully weaned (GSucc), 33 failed to maintain spontaneous breathing so were reconnected (GFail), and 15 were extubated after the test but reintubated within 48 h (GRein). The power spectral density and magnitude squared coherence (MSC) of HRV and respiratory flow signals were estimated. Dimensionality reduction was performed using principal component analysis (PCA) and sequential floating feature selection. The patients were classified using a fuzzy K-nearest neighbour method. PCA of the MSC gave the best classification with the highest accuracy of 92% classifying GSucc versus GFail patients, and 86% classifying GSucc versus GRein patients. PCA of the respiratory flow signal gave the best classification between GFail and GRein patients (79% accuracy). These classifiers showed a good balance between sensitivity and specificity. Besides, the spectral coherence between HRV and the respiratory flow signal, in patients on weaning trial process, can contribute to the extubation decision.

Heart rate variability as a predictor of cardiac dysrhythmias during weaning from mechanical ventilation

BACKGROUND

Weaning from mechanical ventilation to spontaneous breathing is associated with changes in the hemodynamic and autonomic nervous systems that are reflected by heart rate variability. Although cardiac dysrhythmias are an important manifestation of hemodynamic alterations, the impact of heart rate variability on the occurrence of dysrhythmias during weaning has not been specifically studied.

OBJECTIVES

To describe differences in heart rate variability spectral power and occurrence of cardiac dysrhythmias at baseline and during the initial trial of weaning from mechanical ventilation and to evaluate the impact of heart rate variability during weaning on occurrence of dysrhythmias.

METHOD

Continuous 3-lead electrocardiographic recordings were collected from 35 patients receiving mechanical ventilation for 24 hours at baseline and during the initial weaning trial. Heart rate variability was evaluated by using spectral power analysis.

RESULTS

Low-frequency power increased (P = .04) and high-frequency and very-low-frequency power did not change during weaning. The mean number of supraventricular ectopic beats per hour during weaning was higher than the mean at baseline (P < .001); the mean of ventricular ectopic beats did not change. Low-frequency power was a predictor of ventricular and supraventricular ectopic beats during weaning (P < .001). High-frequency power was predictive of ventricular and supraventricular (P = .02) ectopic beats during weaning. Very-low-frequency power was predictive of ventricular ectopic beats (P < .001) only.

CONCLUSION

Heart rate variability power spectra during weaning were predictive of dysrhythmias.

Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

Introduction

Prolonged ventilation and failed extubation are associated with increased harm and cost. The added value of heart and respiratory rate variability (HRV and RRV) during spontaneous breathing trials (SBTs) to predict extubation failure remains unknown.

Methods

We enrolled 721 patients in a multicenter (12 sites), prospective, observational study, evaluating clinical estimates of risk of extubation failure, physiologic measures recorded during SBTs, HRV and RRV recorded before and during the last SBT prior to extubation, and extubation outcomes. We excluded 287 patients because of protocol or technical violations, or poor data quality. Measures of variability (97 HRV, 82 RRV) were calculated from electrocardiogram and capnography waveforms followed by automated cleaning and variability analysis using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software. Repeated randomized subsampling with training, validation, and testing were used to derive and compare predictive models.

Results

Of 434 patients with high-quality data, 51 (12%) failed extubation. Two HRV and eight RRV measures showed statistically significant association with extubation failure (P <0.0041, 5% false discovery rate). An ensemble average of five univariate logistic regression models using RRV during SBT, yielding a probability of extubation failure (called WAVE score), demonstrated optimal predictive capacity. With repeated random subsampling and testing, the model showed mean receiver operating characteristic area under the curve (ROC AUC) of 0.69, higher than heart rate (0.51), rapid shallow breathing index (RBSI; 0.61) and respiratory rate (0.63). After deriving a WAVE model based on all data, training-set performance demonstrated that the model increased its predictive power when applied to patients conventionally considered high risk: a WAVE score >0.5 in patients with RSBI >105 and perceived high risk of failure yielded a fold increase in risk of extubation failure of 3.0 (95% confidence interval (CI) 1.2 to 5.2) and 3.5 (95% CI 1.9 to 5.4), respectively.

Conclusions

Altered HRV and RRV (during the SBT prior to extubation) are significantly associated with extubation failure. A predictive model using RRV during the last SBT provided optimal accuracy of prediction in all patients, with improved accuracy when combined with clinical impression or RSBI. This model requires a validation cohort to evaluate accuracy and generalizability.

Trial registration

ClinicalTrials.gov NCT01237886. Registered 13 October 2010.

Does visual fatigue from 3D displays affect autonomic regulation and heart rhythm?

Most investigations into the negative effects of viewing stereoscopic 3D content on human health have addressed 3D visual fatigue and visually induced motion sickness (VIMS). Very few, however, have looked into changes in autonomic balance and heart rhythm, which are homeostatic factors that ought to be taken into consideration when assessing the overall impact of 3D video viewing on human health. In this study, 30 participants were randomly assigned to two groups: one group watching a 2D video, (2D-group) and the other watching a 3D video (3D-group). The subjects in the 3D-group showed significantly increased heart rates (HR), indicating arousal, and an increased VLF/HF (Very Low Frequency/High Frequency) ratio (a measure of autonomic balance), compared to those in the 2D-group, indicating that autonomic balance was not stable in the 3D-group. Additionally, a more disordered heart rhythm pattern and increasing heart rate (as determined by the R-peak to R-peak (RR) interval) was observed among subjects in the 3D-group compared to subjects in the 2D-group, further indicating that 3D viewing induces lasting activation of the sympathetic nervous system and interrupts autonomic balance.

Application of heart-rate variability in patients undergoing weaning from mechanical ventilation

Introduction

The process of weaning may impose cardiopulmonary stress on ventilated patients. Heart-rate variability (HRV), a noninvasive tool to characterize autonomic function and cardiorespiratory interaction, may be a promising modality to assess patient capability during the weaning process. We aimed to evaluate the association between HRV change and weaning outcomes in critically ill patients.

Methods

This study included 101 consecutive patients recovering from acute respiratory failure. Frequency-domain analysis, including very low frequency, low frequency, high frequency, and total power of HRV was assessed during a 1-hour spontaneous breathing trial (SBT) through a T-piece and after extubation after successful SBT.

Results

Of 101 patients, 24 (24%) had SBT failure, and HRV analysis in these patients showed a significant decrease in total power (P = 0.003); 77 patients passed SBT and were extubated, but 13 (17%) of them required reintubation within 72 hours. In successfully extubated patients, very low frequency and total power from SBT to postextubation significantly increased (P = 0.003 and P = 0.004, respectively). Instead, patients with extubation failure were unable to increase HRV after extubation.

Conclusions

HRV responses differ between patients with different weaning outcomes. Measuring HRV change during the weaning process may help clinicians to predict weaning results and, in the end, to improve patient care and outcome.

Heart rate variability and extubation readiness in extremely preterm infants

BACKGROUND

Mechanical ventilation (MV) is associated with changes in autonomic nervous system activity in preterm infants, which can be assessed by measurements of heart rate variability (HRV). Decreased HRV has been described in adults undergoing disconnection from MV; such information is not available in preterm infants.

OBJECTIVE

To compare differences in HRV between infants successfully extubated and those who failed, and to evaluate the accuracy of HRV as a predictor of extubation readiness.

METHODS

This is a prospective, observational study of infants with a birth weight ≤1,250 g undergoing their first extubation attempt. Heart rate was measured during a 60-min period immediately prior to extubation and HRV was calculated using the frequency domain analysis.

RESULTS

A total of 47 infants were studied; 36 were successfully extubated and 11 reintubated. There were no differences in patient demographics, ventilator settings, blood gases or postextubation management between the groups. All components of the HRV analysis were significantly decreased in infants who failed, generating high areas under the receiver operating characteristic curve. The specificity and positive predictive values were 100, but with limited sensitivity and negative predictive values.

CONCLUSIONS

Infants considered 'ready to be extubated' but who subsequently failed their first extubation attempt had decreased HRV prior to extubation. Though promising, the value of HRV as a predictor of extubation readiness requires further evaluation.

Weaning infants from mechanical ventilation

Protracted mechanical ventilation is associated with increased morbidity and mortality in preterm infants and thus the earliest possible weaning from mechanical ventilation is desirable. Weaning protocols may be helpful in achieving more rapid reduction in support. There is no clear consensus regarding the level of support at which an infant is ready for extubation. An improved ability to predict when a preterm infant has a high likelihood of successful extubation is highly desirable. In this article, available evidence is reviewed and reasonable evidence-based recommendations for expeditious weaning and extubation are provided.

Variáveis hemodinâmicas, hemogasométricas e respiratórias em pacientes cardiopatas submetidos ao teste de respiração espontânea

CONTEXTUALIZAÇÃO: Apesar de existirem situações em que há necessidade da ventilação mecânica (AVM), sua retirada (desmame) deve ocorrer tão breve quanto possível, sendo o teste de respiração espontânea (TRE) uma ferramenta útil para abreviar e reduzir os riscos. Existem diversas formas de realizar o TRE, porém, em pacientes cardiopatas, o mais eficaz parece ser associado à pressão de suporte (PSV) e à pressão positiva no final da expiração (PEEP). OBJETIVO: Analisar, ao início e ao término do TRE no modo PSV, o comportamento das variáveis hemodinâmicas, hemogasométricas e respiratórias em pacientes cardiopatas. MÉTODO: 22 pacientes cardiopatas foram submetidos ao TRE por 30 minutos, dos quais cinco foram excluídos e 17 conseguiram concluir o teste. Para mensuração da força muscular inspiratória e da mecânica respiratória, avaliou-se imediatamente antes do TRE: pressão inspiratória inicial e inspiratória máxima, resistência das vias aéreas, complacência estática e dinâmica seguida da avaliação do volume corrente, frequência respiratória, parâmetros hemodinâmicos e hemogasométricos. Todos os parâmetros foram reavaliados 30 minutos após o TRE. RESULTADOS: Os valores tratados na análise estatística dessas variáveis não apresentaram diferença significativa, apenas o índice de desmame ventilatório mostrou variação significativa (p = 0,011). CONCLUSÃO: Por meio do presente estudo, percebeu-se que a maior parte dos pacientes cardiopatas submetidos ao TRE utilizando PSV manteve-se em estabilidade hemodinâmica, hemogasométrica e respiratória. Além disso, percebeu-se que cerca de 82% obteve sucesso ao desmame, sugerindo que PSV é um método seguro e eficaz na interrupção AVM. Contudo, por causa da amostra reduzida, o desenvolvimento de estudos semelhantes torna-se necessário.

Mechanical ventilation during anaesthesia: challenges and opportunities for investigating the respiration-related cardiovascular oscillations

The vast majority of the available literature regarding cardiovascular oscillations refers to spontaneously breathing subjects. Only a few studies investigated cardiovascular oscillations, and especially respiration-related ones (RCVO), during intermittent positive pressure mechanical ventilation (IPPV) under anaesthesia. Only a handful considered assisted IPPV, in which spontaneous breathing activity is supported, rather than replaced as in controlled IPPV. In this paper, we review the current understanding of RCVO physiology during IPPV, from literature retrieved through PubMed website. In particular, we describe how during controlled IPPV under anaesthesia respiratory sinus arrhythmia appears to be generated by non-neural mechano-electric feedback in the heart (indirectly influenced by tonic sympathetic regulation of vascular tone and heart contractility) and not by phasic vagal modulation of central origin and/or baroreflex mechanisms. Furthermore, assisted IPPV differs from controlled IPPV in terms of RCVO, reintroducing significant central respiratory vagal modulation of respiratory sinus arrhythmia. This evidence indicates against applying to IPPV interpretative paradigms of RCVO derived from spontaneously breathing subjects, and against considering together IPPV and spontaneously breathing subjects for RCVO-based risk assessment. Finally, we highlight the opportunities that IPPV offers for future investigations of RCVO genesis and interactions, and we indicate several possibilities for clinical applications of RCVO during IPPV.

Study of multiparameter respiratory pattern complexity in surgical critically ill patients during weaning trials

Background

Separation from mechanical ventilation is a difficult task, whereas conventional predictive indices have not been proven accurate enough, so far. A few studies have explored changes of breathing pattern variability for weaning outcome prediction, with conflicting results. In this study, we tried to assess respiratory complexity during weaning trials, using different non-linear methods derived from theory of complex systems, in a cohort of surgical critically ill patients.

Results

Thirty two patients were enrolled in the study. There were 22 who passed and 10 who failed a weaning trial. Tidal volume and mean inspiratory flow were analyzed for 10 minutes during two phases: 1. pressure support (PS) ventilation (15-20 cm H₂O) and 2. weaning trials with PS: 5 cm H₂O. Sample entropy (SampEn), detrended fluctuation analysis (DFA) exponent, fractal dimension (FD) and largest lyapunov exponents (LLE) of the two respiratory parameters were computed in all patients and during the two phases of PS. Weaning failure patients exhibited significantly decreased respiratory pattern complexity, reflected in reduced sample entropy and lyapunov exponents and increased DFA exponents of respiratory flow time series, compared to weaning success subjects (p < 0.001). In addition, their changes were opposite between the two phases of the weaning trials. A new model including rapid shallow breathing index (RSBI), its product with airway occlusion pressure at 0.1 sec (P_0.1), SampEn and LLE predicted better weaning outcome compared with RSBI, P_0.1 and RSBI* P_0.1 (conventional model, R² = 0.874 vs 0.643, p < 0.001). Areas under the curve were 0.916 vs 0.831, respectively (p < 0.05).

Conclusions

We suggest that complexity analysis of respiratory signals can assess inherent breathing pattern dynamics and has increased prognostic impact upon weaning outcome in surgical patients.

Changes of heart and respiratory rate dynamics during weaning from mechanical ventilation: a study of physiologic complexity in surgical critically ill patients

PURPOSE

The aim of the study was to investigate heart rate (HR) and respiratory rate (RR) complexity in patients with weaning failure or success, using both linear and nonlinear techniques.

MATERIALS AND METHODS

Forty-two surgical patients were enrolled in the study. There were 24 who passed and 18 who failed a weaning trial. Signals were analyzed for 10 minutes during 2 phases: (1) pressure support (PS) ventilation (15-20 cm H(2)O) and (2) weaning trials with PS (5 cm H(2)O). Low- and high-frequency (LF, HF) components of HR signals, HR multiscale entropy (MSE), RR sample entropy, cross-sample entropy between cardiorespiratory signals, Poincaré plots, and α1 exponent were computed in all patients and during the 2 phases of PS.

RESULTS

Weaning failure patients exhibited significantly decreased RR sample entropy, LF, HF, and α1 exponent, compared with weaning success subjects (P < .001). Their changes were opposite between the 2 phases, except for MSE that increased between and within groups (P < .001). A new model including rapid shallow breathing index (RSBI), α1 exponent, RR, and cross-sample entropies predicted better weaning outcome compared with RSBI, airway occlusion pressure at 0.1 second (P(0.1)), and RSBI × P(0.1) (conventional model, R(2) = 0.887 vs 0.463; P < .001). Areas under the curve were 0.92 vs 0.86, respectively (P < .005).

CONCLUSIONS

We suggest that nonlinear analysis of cardiorespiratory dynamics has increased prognostic impact upon weaning outcome in surgical patients.

Predicting clinical physiology: a Markov chain model of heart rate recovery after spontaneous breathing trials in mechanically ventilated patients

Analysis of heart rate (HR) dynamics before, during, and after a physiologic stress has clinical importance. For example, the celerity of heart rate recovery (HRR) after a cardiac stress test (eg, treadmill exercise test) has been shown to be an independent predictor of all-cause mortality. Heart rate dynamics are modulated, in part, by the autonomic nervous system. These dynamics are commonly abstracted using metrics of heart rate variability (HRV), which are known to be sensitive to the influence of the autonomic nervous system on HR. The patient-specific modulators of HR should be reflected both in the response to stress as well as in the recovery from stress. We therefore hypothesized that the patient-specific HR response to stress could be used to predict the HRR after the stress. We devised a Markov chain model to predict the poststress HRR dynamics using the parameters (transition matrix) calculated from HR data during the stress. The model correctly predicts the exponential shape of poststress HRR. This model features a simple analytical relationship linking poststress HRR time constant (T(off)) with a standard measure of HRV, namely the correlation coefficient of the Poincaré plot (first return map) of the HR recorded during the stress. A corresponding relationship exists between the time constant (T(on)) of R-R interval decrease at the onset of stress and the correlation coefficient of the Poincaré plot of prestress R-R intervals. Consequently, the model can be used for the prediction of poststress HRR using the HRV measured during the stress. This direct relationship between the event-to-event microscopic fluctuations (HRV) during the stress and the macroscopic response (HRR) after the stress terminates can be interpreted as an instance of a fluctuation-dissipation relationship. We have thus applied the fluctuation-dissipation theorem to the analysis of heart rate dynamics. The approach is specific neither to cardiac physiology nor to transitions between mechanical and free ventilation as a specific stress. It may therefore have wider applicability to physiologic systems subject to modest stresses.

Autonomic tone in medical intensive care patients receiving mechanical ventilation and during a CPAP weaning trial

Mechanical ventilator support and the resumption of spontaneous ventilation or weaning create significant alterations in alveolar and intrathoracic pressure that influence thoracic blood volume and flow. Compensatory autonomic tone alterations occur to ensure adequate tissue oxygen delivery, but autonomic responses may produce cardiovascular dysfunction with subsequent weaning failure. The authors describe autonomic responses of critically ill patients (n = 43) during a 24-hr period of mechanical ventilatory support and during the 24 hr that included their initial spontaneous breathing trial using continuous positive airway pressure. Nearly two thirds of these patients demonstrated abnormal autonomic function and this dysfunction was more severe in those patients who were unable to sustain spontaneous ventilation (n = 15). With further systematic study, autonomic responses may be useful in the identification of patients who are likely to develop cardiac dysfunction with the resumption of spontaneous breathing.

Cardiovascular effects of mechanical ventilation and weaning

Because of their anatomic position in the closed thoracic cavity, the heart and lungs interact during each ventilation cycle. The application of mechanical ventilation and subsequent removal changes normal ventilatory mechanics and produces alterations in cardiac preload and afterload that influence global hemodynamic state and delivery of oxygen and nutrients. Adverse cardiovascular responses to mechanical ventilation and weaning from ventilation include hemodynamic alterations and instability, myocardial ischemia, autonomic dysfunction, and cardiac dysrhythmias. Clinicians must have a clear understanding of the cardiovascular effects of mechanical ventilation and weaning so they may anticipate, recognize, and effectively manage negative effects and improve patient outcomes.

Liberation from mechanical ventilation: what monitoring matters?

Over the past 2 decades, the art of "weaning" from mechanical ventilation has been informed by increasing published basic science and outcomes studies. Although monitoring technologies can provide vast amounts of information before, during, and after liberation from mechanical ventilation, little data exists on how to maximally harness even routinely monitored, basic physiologic parameters. Overdependence on technology and derived variables, without data to demonstrate benefit, may even inhibit the patient's progress if it is used inappropriately. We review the scientific evidence for best using routinely available physiologic data and a few more sophisticated and invasive monitoring technologies during weaning. We also suggest future study designs that would better inform the process of liberation from the ventilator and endotracheal extubation.

Time-frequency analysis of cardiac and respiratory parameters for the prediction of ventilator weaning

Mechanical ventilators are used to provide life support in patients with respiratory failure. Assessing autonomic control during the ventilator weaning provides information about physiopathological imbalances. Autonomic parameters can be derived and used to predict success in discontinuing from the mechanical support. Time-frequency analysis is used to derive cardiac and respiratory parameters, as well as their evolution in time, during ventilator weaning in 130 patients. Statistically significant differences have been observed in autonomic parameters between patients who are considered ready for spontaneous breathing and patients who are not. A classification based on respiratory frequency, heart rate and heart rate variability spectral components has been proposed and has been able to correctly classify more than 80% of the cases.

Heart rate variability analysis: a tool to assess cardiac autonomic function

Heart rate monitoring is commonly used to provide an acute indicator of an individual's cardiovascular status and responsiveness. An increasingly popular technique involves quantifying the very small amounts by which the heart rate changes from one cardiac cycle to the next. This "heart rate variability (HRV) analysis" provides a substantial amount of additional information about the cardiovascular system and enables quantification of cardiac regulatory influences on the autonomic nervous system. The autonomic nervous system consists of two main components: the sympathetic system and the parasympathetic system. The relative influence of these two components on the sino-atrial node of the heart determines the heart rate. A number of physiological factors, including blood pressure and respiratory rate, can have a profound effect on this autonomic "balance." HRV analysis therefore provides a noninvasive method for investigating the dynamic influence of changing physiological parameters on cardiac regulation.