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

Feasibility and Optimization of Donation Advisor: a Decision Support Tool for Deceased Organ Donation and Transplantation

Background

This study aimed to evaluate the ability of Donation Advisor (DA), a validated clinical decision support tool that uses continuous monitoring, variability analysis, and predictive models, to (i) predict likelihood of successful donation after circulatory determination of death (DCD) before withdrawal of life-sustaining measures (WLSM), and (ii) describe ischemia during WLSM in DCD patients.

Methods

Eligible patients were screened at the 5 sites where DA was implemented. DA reports were generated in real time but shown to clinicians after the donation was complete (noninterventional). Clinicians were interviewed for improvement of the tool.

Results

We enrolled 34 donor patients in the study; 27 had DCD attempts and 20 proceeded to organ recovery. DA reports were generated before WLSM in all 27 attempted DCD patients (100%) while post-WLSM ischemia reports were generated in 26 of 27 DCD attempts (96%). Nineteen of 34 involved clinicians completed interviews, 10 from intensive care, and 9 from transplantation team members. Following a user-centered design approach, feedback was used to create 5 versions. Revisions included additions, removals, clarifications, and formatting changes; the number of revisions decreased with each amendment. The report's predictive scores were found to be useful by most practitioners (83%). We identified barriers and drivers to use the report in future practice, some of which may be addressed through improved education and awareness.

Conclusions

DA can be deployed in real time during the DCD process. The usefulness and usability of the DA report has improved through user feedback; both barriers and drivers to implementation exist.

Heart Rate Variability During Weaning from Invasive Mechanical Ventilation: A Systematic Review

Background: The role of Heart Rate Variability (HRV) indices in predicting the outcome of the weaning process remains a subject of debate. The aim of this study is to investigate HRV analysis in critically ill adult patients undergoing weaning from invasive mechanical ventilation (IMV). Methods: The protocol of this systematic review was registered with PROSPERO (CRD42024485800). We searched PubMed and Scopus databases from inception till March 2023 to identify randomized controlled trials and observational studies investigating HRV analysis in critically ill adult patients undergoing weaning from invasive mechanical ventilation. Our primary outcome was to investigate HRV changes occurring during the weaning from IMV. Results: Seven studies (n = 342 patients) were included in this review. All studies reported significant changes in at least one HRV parameter. The indices Low Frequency (LF), High Frequency (HF), and LF/HF ratio seem to be the most promising in predicting the outcome of weaning with reliability. Some HRV indices showed modification in response to different ventilator settings or modalities. Conclusions: Available data report HRV modifications during the process of weaning and suggest a promising role of some HRV indices in predicting weaning outcomes in critically ill patients. Point-of-care HRV monitoring systems might help to early detect patients at risk of weaning failure.

Kinematic signature of high risk labored breathing revealed by novel signal analysis

Breathing patterns (respiratory kinematics) contain vital prognostic information. This dimension of physiology is not captured by conventional vital signs. We sought to determine the feasibility and utility of quantifying respiratory kinematics. Using inertial sensors, we analyzed upper rib, lower rib, and abdominal motion of 108 patients with respiratory symptoms during a hospital encounter (582 two-minute recordings). We extracted 34 features based on an explainable correspondence with well-established breathing patterns. K-means clustering revealed that respiratory kinematics had three dimensions apart from the respiratory rate. We represented these dimensions using respiratory rate variability, respiratory alternans (rib-predominant breaths alternating with abdomen-predominant ones), and recruitment of accessory muscles (increased upper rib excursion). Latent profile analysis of the kinematic measures revealed two profiles consistent with the established clinical constructs of labored and unlabored breathing. In logistic regression, the labored breathing profile improved model discrimination for critical illness beyond the Sequential Organ Failure Assessment (SOFA) score (AUROC 0.77 v/s 0.72; p = 0.02). These findings quantitatively confirm the prior understanding that the respiratory rate alone does not adequately represent the complexity of respiratory kinematics; they demonstrate that high-dimensional signatures of labored breathing can be quantified in routine practice settings, and they can improve predictions of clinical deterioration.

Extubation Advisor: Implementation and Evaluation of A Novel Extubation Clinical Decision Support Tool

IMPORTANCE

Extubation Advisor (EA) is a novel software tool that generates a synoptic report for each Spontaneous Breathing Trial (SBT) conducted to inform extubation decision-making.

OBJECTIVES

To assess bedside EA implementation, perceptions of utility, and identify barriers and facilitators of use.

DESIGN, SETTING AND PARTICIPANTS

We conducted a phase I mixed-methods interventional study in three mixed intensive care unit (ICUs) in two academic hospitals. We interviewed critical care physicians (MDs) and respiratory therapists (RTs) regarding user-centered design principles and usability.

ANALYSIS

We evaluated our ability to consent participants (feasibility threshold 50%), capture complete data (threshold 90%), generate and review EA reports in real-time (thresholds 75% and 80%, respectively), and MD perception of tool usefulness (6-point Likert scale). We analyzed interview transcripts using inductive coding to identify facilitators and barriers to EA implementation and perceived benefit of tool use.

RESULTS

We enrolled 31 patients who underwent 70 SBTs. Although consent rates [31/31 (100%], complete data capture [68/68 (100%)], and EA report generation [68/70 (97.1%)] exceeded feasibility thresholds, reports were reviewed by MDs for [55/70 (78.6%)] SBTs. Mean MD usefulness score was 4.0/6. Based on feedback obtained from 36 interviews (15 MDs, 21 RTs), we revised the EA report twice and identified facilitators (ability to track patient progress, enhance extubation decision-making, and provide support in resource-limited settings) and barriers (resource constraints, need for education) to tool implementation. Half of respondents (9 MDs, 9 RTs; combined 50%) perceived definite or potential benefit to EA tool use.

CONCLUSION

This is the first study of a waveform-based variability-derived, predictive clinical decision support tool evaluated in adult ICUs. Our findings support the feasibility of integrating the EA tool into bedside workflow. Clinical trials are needed to assess the utility of the EA tool in practice and its impact on extubation decision-making and outcomes.

TRIAL REGISTRATION

NCT04708509.

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.

Rapid shallow breathing index predicting extubation outcomes: A systematic review and meta-analysis

OBJECTIVE

This meta-analysis aimed to assess the predictive value of the rapid shallow breathing index for extubation outcomes.

METHODOLOGY

We conducted a systematic review of literature (inception to March 2023) and a meta-analysis. Statistical analysis was performed using Meta-Disc 1.4 software, RevMan 5.4 software and Stata 14.0 software to evaluate the predictive value of RSBI for extubation outcomes.

RESULTS

A total of 1,987 studies were retrieved, and after applying the inclusion criteria, 79 studies were included in the final analysis, involving 13,170 patients undergoing mechanical ventilation. The random-effects model was employed for statistical analysis. The summary receiver operating characteristic curves (SROC) area under the curve (AUC) was 0.8144. The pooled sensitivity was 0.60 (95% CI: 0.59, 0.61), the pooled specificity was 0.68 (95% CI: 0.66, 0.70).

CONCLUSIONS

The Rapid Shallow Breathing Index demonstrated moderate accuracy, poor pooled sensitivity and specificity in predicting successful extubation, however the study does not present adequate data to support or reject the use of this tool as a single parameter that predicts extubation outcome. Future studies should explore the combination of The Rapid Shallow Breathing Index with other indicators and clinical experience to improve the success rate of extubation and reduce the risk of extubation failure.

IMPLICATIONS FOR CLINICAL PRACTICE

Premature and delayed extubation in mechanically ventilated patients can have a negative impact on prognosis and prolong hospital stay. The Rapid Shallow Breathing Index is a simple, cost-effective, and easily monitored objective evaluation index, which can be used to predict the outcome of extubation, especially in primary hospitals. Our study comprehensively evaluated the value of this tool in predicting extubation outcomes, which can help clinicians combine subjective experience with objective indicators to improve the accuracy of extubation time decisions.

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.

The Constrained Disorder Principle Accounts for the Variability That Characterizes Breathing: A Method for Treating Chronic Respiratory Diseases and Improving Mechanical Ventilation

Variability characterizes breathing, cellular respiration, and the underlying quantum effects. Variability serves as a mechanism for coping with changing environments; however, this hypothesis does not explain why many of the variable phenomena of respiration manifest randomness. According to the constrained disorder principle (CDP), living organisms are defined by their inherent disorder bounded by variable boundaries. The present paper describes the mechanisms of breathing and cellular respiration, focusing on their inherent variability. It defines how the CDP accounts for the variability and randomness in breathing and respiration. It also provides a scheme for the potential role of respiration variability in the energy balance in biological systems. The paper describes the option of using CDP-based artificial intelligence platforms to augment the respiratory process's efficiency, correct malfunctions, and treat disorders associated with the respiratory system.

Dynamics of ventilatory pattern variability and Cardioventilatory Coupling during systemic inflammation in rats

Introduction: Biometrics of common physiologic signals can reflect health status. We have developed analytics to measure the predictability of ventilatory pattern variability (VPV, Nonlinear Complexity Index (NLCI) that quantifies the predictability of a continuous waveform associated with inhalation and exhalation) and the cardioventilatory coupling (CVC, the tendency of the last heartbeat in expiration to occur at preferred latency before the next inspiration). We hypothesized that measures of VPV and CVC are sensitive to the development of endotoxemia, which evoke neuroinflammation. Methods: We implanted Sprague Dawley male rats with BP transducers to monitor arterial blood pressure (BP) and recorded ventilatory waveforms and BP simultaneously using whole-body plethysmography in conjunction with BP transducer receivers. After baseline (BSLN) recordings, we injected lipopolysaccharide (LPS, n = 8) or phosphate buffered saline (PBS, n =3) intraperitoneally on 3 consecutive days. We recorded for 4-6 h after the injection, chose 3 epochs from each hour and analyzed VPV and CVC as well as heart rate variability (HRV). Results: First, the responses to sepsis varied across rats, but within rats the repeated measures of NLCI, CVC, as well as respiratory frequency (fR), HR, BP and HRV had a low coefficient of variation, (<0.2) at each time point. Second, HR, fR, and NLCI increased from BSLN on Days 1-3; whereas CVC decreased on Days 2 and 3. In contrast, changes in BP and the relative low-(LF) and high-frequency (HF) of HRV were not significant. The coefficient of variation decreased from BSLN to Day 3, except for CVC. Interestingly, NLCI increased before fR in LPS-treated rats. Finally, we histologically confirmed lung injury, systemic inflammation via ELISA and the presence of the proinflammatory cytokine, IL-1β, with immunohistochemistry in the ponto-medullary respiratory nuclei. Discussion: Our findings support that NLCI reflects changes in the rat's health induced by systemic injection of LPS and reflected in increases in HR and fR. CVC decreased over the course to the experiment. We conclude that NLCI reflected the increase in predictability of the ventilatory waveform and (together with our previous work) may reflect action of inflammatory cytokines on the network generating respiration.

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.

Predictors of Noninvasive Ventilation Failure in the Post-Extubation Period: A Systematic Review and Meta-Analysis

OBJECTIVES

To identify factors associated with failure of noninvasive ventilation (NIV) in the post-extubation period.

DATA SOURCES

We searched Embase Classic +, MEDLINE, and the Cochrane Database of Systematic Reviews from inception to February 28, 2022.

STUDY SELECTION

We included English language studies that provided predictors of post-extubation NIV failure necessitating reintubation.

DATA EXTRACTION

Two authors conducted data abstraction and risk-of-bias assessments independently. We used a random-effects model to pool binary and continuous data and summarized estimates of effect using odds ratios (ORs) mean difference (MD), respectively. We used the Quality in Prognosis Studies tool to assess risk of bias and the Grading of Recommendations, Assessment, Development and Evaluations to assess certainty.

DATA SYNTHESIS

We included 25 studies ( n = 2,327). Illness-related factors associated with increased odds of post-extubation NIV failure were higher critical illness severity (OR, 3.56; 95% CI, 1.96-6.45; high certainty) and a diagnosis of pneumonia (OR, 6.16; 95% CI, 2.59-14.66; moderate certainty). Clinical and biochemical factors associated with moderate certainty of increased risk of NIV failure post-extubation include higher respiratory rate (MD, 1.54; 95% CI, 0.61-2.47), higher heart rate (MD, 4.46; 95% CI, 1.67-7.25), lower Pa o2 :F io2 (MD, -30.78; 95% CI, -50.02 to -11.54) 1-hour after NIV initiation, and higher rapid shallow breathing index (MD, 15.21; 95% CI, 12.04-18.38) prior to NIV start. Elevated body mass index was the only patient-related factor that may be associated with a protective effect (OR, 0.21; 95% CI, 0.09-0.52; moderate certainty) on post-extubation NIV failure.

CONCLUSIONS

We identified several prognostic factors before and 1 hour after NIV initiation associated with increased risk of NIV failure in the post-extubation period. Well-designed prospective studies are required to confirm the prognostic importance of these factors to help further guide clinical decision-making.

Characterisation of the acute hypoxic response using breathing variability parameters: a pilot study in humans

PURPOSE

We aimed to investigate respiratory rate variability (RRV) and tidal volume (V_t) variability during exposure to normobaric hypoxia (i.e., reduction in the fraction of inspired oxygen - FiO₂), and the association of the changes in RRV and V_t variability with the changes in pulse oxygen saturation (SpO₂).

METHODS

Thirty healthy human participants (15 females) were exposed to: (1) 15-min normoxia, (2) 10-min hypoxia simulating 2200m, (3) 10-min hypoxia simulating 4000m, (4) 10-min hypoxia simulating 5000m, (5) 15-min recovery in normoxia. Linear regression modelling was applied with SpO₂ (dependent variable) and the changes in RRV and V_t variability (independent variables), controlling for FiO₂, age, sex, changes in heart rate (HR), changes in HR variability (HRV), and changes in minute ventilation (V_E).

RESULTS

When modelling breathing parameter variability as root-mean-square standard deviation (RMSSD), a significant independent association of the changes in RRV with the changes in SpO₂ was found (B=-4.3e-04, 95% CI=-8.3e-04/-2.1e-05, p=0.04). The changes in V_t variability showed no significant association with the changes in SpO₂ (B=-1.6, 95% CI=-5.5/2.4, p=0.42). When modelling parameters variability as SD, a significant independent association of the changes in RRV with the changes in SpO₂ was found (B=-8.2e-04, 95% CI=-1.5e-03/-9.4e-05, p=0.03). The changes in V_t variability showed no significant association with the changes in SpO₂ (B=1.4, 95% CI=-5.8/8.6, p=0.69).

CONCLUSION

Higher RRV is independently associated with lower SpO₂ during acute hypoxic exposure, while V_t variability parameters are not. Therefore, RRV may be a potentially interesting parameter to characterize individual responses to acute hypoxia.

A new index, Respiratory Insufficiency index and Modified Early Warning Scores predict extubation failure

Background

Extubation failure occurs in 5%-20% of patients and is associated with poor clinical outcomes. The primary aim of this project was to determine the predictive ability of the Respiratory Insufficiency (RI) index, Respiratory Oxygenation (ROX) index and Modified Early Warning Score (MEWS) in identifying extubation failure.

Methods

This was a secondary analysis of a prior cross-sectional retrospective study conducted from February 2018 through December 2018 among adult subjects who received mechanical ventilation for more than 24 h. Extubation failure was defined as the need for reintubation or rescue non-invasive ventilation (NIV) within 48 h after planned extubation. Univariate analysis and logistic regression were used to identify the predictors and final model was validated using 10-fold cross validation. Nomogram was constructed based on the final model.

Results

Of 216 enrolled subjects, 46 (21.3%) experienced extubation failure. The median RI index 1-h post extubation was 20 [interquartile range [IQR] 16.33-24.24] for success group and 27.02 [IQR 22.42-33.83] for the failure group (P<0.001). The median ROX index 1-h post extubation was 16.66 [IQR 12.57-19.84] for success group and 11.11 [IQR 8.09-14.67] for failure group (P<0.001). The median MEWS 1-h post extubation was 2 [IQR 1-3] for the success group and 4 [IQR 3-5] for the failure group (P<0.001). In multivariable analysis, age >60 years [OR 3.89 (95% CI 1.56-9.73); P=0.004], MEWS >4 [OR 4.01 (95% CI (1.59-10.14); P=0.003] and, RI index >20 [OR 4.50 (95% CI 1.43-14.21); P=0.010] were independently associated with extubation failure.

Conclusion

In the present study, RI index and MEWS were independently associated with predicting extubation failure within 1 h of extubation. A prospective validation study is warranted to establish the role of these indices in predicting extubation outcome.

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.

Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps

OBJECTIVE

The main aim of this study was to analyze the performance of different artificial intelligence (AI) models in endoscopic colonic polyp detection and classification and compare them with doctors with different experience.

METHODS

We searched the studies on Colonoscopy, Colonic Polyps, Artificial Intelligence, Machine Learning, and Deep Learning published before May 2020 in PubMed, EMBASE, Cochrane, and the citation index of the conference proceedings. The quality of studies was assessed using the QUADAS-2 table of diagnostic test quality evaluation criteria. The random-effects model was calculated using Meta-DISC 1.4 and RevMan 5.3.

RESULTS

A total of 16 studies were included for meta-analysis. Only one study (1/16) presented externally validated results. The area under the curve (AUC) of AI group, expert group and non-expert group for detection and classification of colonic polyps were 0.940, 0.918, and 0.871, respectively. AI group had slightly lower pooled specificity than the expert group (79% vs. 86%, P < 0.05), but the pooled sensitivity was higher than the expert group (88% vs. 80%, P < 0.05). While the non-experts had less pooled specificity in polyp recognition than the experts (81% vs. 86%, P < 0.05), and higher pooled sensitivity than the experts (85% vs. 80%, P < 0.05).

CONCLUSION

The performance of AI in polyp detection and classification is similar to that of human experts, with high sensitivity and moderate specificity. Different tasks may have an impact on the performance of deep learning models and human experts, especially in terms of sensitivity and specificity.

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.

Predicting Time to Death After Withdrawal of Life-Sustaining Measures Using Vital Sign Variability: Derivation and Validation

To develop a predictive model using vital sign (heart rate and arterial blood pressure) variability to predict time to death after withdrawal of life-supporting measures.

Effectiveness of Different Physiotherapy Protocols in Children in the Intensive Care Unit: A Randomized Clinical Trial

PURPOSE

This study aimed to investigate the effectiveness of different physical therapy protocols on the autonomic modulation of heart rate, time of invasive mechanical ventilation (IMV), and length of hospital stay.

METHODS

This was a randomized clinical study with 20 children on IMV in an intensive care unit (ICU), between July 2018 and September 2019. The control group (n = 10) performed the hospital's physical therapy protocol and the experimental group (n = 10) performed the physical therapy protocol based on physical exercise.

RESULTS

Higher values of heart rate variability were found in the experimental group, both in individual and intergroup analyses. There was a significant reduction in the time of IMV and ICU stay.

CONCLUSION

There was an improvement in heart rate variability, reduced time on mechanical ventilation and length of stay in the ICU in individuals who performed the study protocol.

The Usefulness of the Rapid Shallow Breathing Index in Predicting Successful Extubation: A Systematic Review and Meta-analysis

BACKGROUND

Clinicians use several measures to ascertain whether individual patients will tolerate liberation from mechanical ventilation, including the rapid shallow breathing index (RSBI).

RESEARCH QUESTION

Given varied use of different thresholds, patient populations, and measurement characteristics, how well does RSBI predict successful extubation?

STUDY DESIGN AND METHODS

We searched six databases from inception through September 2019 and selected studies reporting the accuracy of RSBI in the prediction of successful extubation. We extracted study data and assessed quality independently and in duplicate.

RESULTS

We included 48 studies involving RSBI measurements of 10,946 patients. Pooled sensitivity for RSBI of < 105 in predicting extubation success was moderate (0.83 [95% CI, 0.78-0.87], moderate certainty), whereas specificity was poor (0.58 [95% CI, 0.49-0.66], moderate certainty) with diagnostic ORs (DORs) of 5.91 (95% CI, 4.09-8.52). RSBI thresholds of < 80 or 80 to 105 yielded similar sensitivity, specificity, and DOR. These findings were consistent across multiple subgroup analyses reflecting different patient characteristics and operational differences in RSBI measurement.

INTERPRETATION

As a stand-alone test, the RSBI has moderate sensitivity and poor specificity for predicting extubation success. Future research should evaluate its role as a permissive criterion to undergo a spontaneous breathing trial (SBT) for patients who are at intermediate pretest probability of passing an SBT.

TRIAL REGISTRY

PROSPERO; No.: CRD42020149196; URL: www.crd.york.ac.uk/prospero/.

Prediction of extubation outcome in critically ill patients: a systematic review and meta-analysis

BACKGROUND

Extubation failure is an important issue in ventilated patients and its risk factors remain a matter of research. We conducted a systematic review and meta-analysis to explore factors associated with extubation failure in ventilated patients who passed a spontaneous breathing trial and underwent planned extubation. This systematic review was registered in PROPERO with the Registration ID CRD42019137003.

METHODS

We searched the PubMed, Web of Science and Cochrane Controlled Register of Trials for studies published from January 1998 to December 2018. We included observational studies involving risk factors associated with extubation failure in adult intensive care unit patients who underwent invasive mechanical ventilation. Two authors independently extracted data and assessed the validity of included studies.

RESULTS

Sixty-seven studies (involving 26,847 participants) met the inclusion criteria and were included in our meta-analysis. We analyzed 49 variables and, among them, we identified 26 factors significantly associated with extubation failure. Risk factors were distributed into three domains (comorbidities, acute disease severity and characteristics at time of extubation) involving mainly three functions (circulatory, respiratory and neurological). Among these, the physiological respiratory characteristics at time of extubation were the most represented. The individual topic of secretion management was the one with the largest number of variables. By Bayesian multivariable meta-analysis, twelve factors were significantly associated with extubation failure: age, history of cardiac disease, history of respiratory disease, Simplified Acute Physiologic Score II score, pneumonia, duration of mechanical ventilation, heart rate, Rapid Shallow Breathing Index, negative inspiratory force, lower PaO2/FiO2 ratio, lower hemoglobin level and lower Glasgow Coma Scale before extubation, with the latest factor having the strongest association with extubation outcome.

CONCLUSIONS

Numerous factors are associated with extubation failure in critically ill patients who have passed a spontaneous breathing trial. Robust multiparametric clinical scores and/or artificial intelligence algorithms should be tested based on the selected independent variables in order to improve the prediction of extubation outcome in the clinical scenario.

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.

Breathing variability-implications for anaesthesiology and intensive care

The respiratory system reacts instantaneously to intrinsic and extrinsic inputs. This adaptability results in significant fluctuations in breathing parameters, such as respiratory rate, tidal volume, and inspiratory flow profiles. Breathing variability is influenced by several conditions, including sleep, various pulmonary diseases, hypoxia, and anxiety disorders. Recent studies have suggested that weaning failure during mechanical ventilation may be predicted by low respiratory variability. This review describes methods for quantifying breathing variability, summarises the conditions and comorbidities that affect breathing variability, and discusses the potential implications of breathing variability for anaesthesia and intensive care.

Resumption of Cardiac Activity after Withdrawal of Life-Sustaining Measures

BACKGROUND

The minimum duration of pulselessness required before organ donation after circulatory determination of death has not been well studied.

METHODS

We conducted a prospective observational study of the incidence and timing of resumption of cardiac electrical and pulsatile activity in adults who died after planned withdrawal of life-sustaining measures in 20 intensive care units in three countries. Patients were intended to be monitored for 30 minutes after determination of death. Clinicians at the bedside reported resumption of cardiac activity prospectively. Continuous blood-pressure and electrocardiographic (ECG) waveforms were recorded and reviewed retrospectively to confirm bedside observations and to determine whether there were additional instances of resumption of cardiac activity.

RESULTS

A total of 1999 patients were screened, and 631 were included in the study. Clinically reported resumption of cardiac activity, respiratory movement, or both that was confirmed by waveform analysis occurred in 5 patients (1%). Retrospective analysis of ECG and blood-pressure waveforms from 480 patients identified 67 instances (14%) with resumption of cardiac activity after a period of pulselessness, including the 5 reported by bedside clinicians. The longest duration after pulselessness before resumption of cardiac activity was 4 minutes 20 seconds. The last QRS complex coincided with the last arterial pulse in 19% of the patients.

CONCLUSIONS

After withdrawal of life-sustaining measures, transient resumption of at least one cycle of cardiac activity after pulselessness occurred in 14% of patients according to retrospective analysis of waveforms; only 1% of such resumptions were identified at the bedside. These events occurred within 4 minutes 20 seconds after a period of pulselessness. (Funded by the Canadian Institutes for Health Research and others.).

Optimizing Our Patients' Entropy Production as Therapy? Hypotheses Originating from the Physics of Physiology

Understanding how nature drives entropy production offers novel insights regarding patient care. Whilst energy is always preserved and energy gradients irreversibly dissipate (thus producing entropy), increasing evidence suggests that they do so in the most optimal means possible. For living complex non-equilibrium systems to create a healthy internal emergent order, they must continuously produce entropy over time. The Maximum Entropy Production Principle (MEPP) highlights nature's drive for non-equilibrium systems to augment their entropy production if possible. This physical drive is hypothesized to be responsible for the spontaneous formation of fractal structures in space (e.g., multi-scale self-similar tree-like vascular structures that optimize delivery to and clearance from an organ system) and time (e.g., complex heart and respiratory rate variability); both are ubiquitous and essential for physiology and health. Second, human entropy production, measured by heat production divided by temperature, is hypothesized to relate to both metabolism and consciousness, dissipating oxidative energy gradients and reducing information into meaning and memory, respectively. Third, both MEPP and natural selection are hypothesized to drive enhanced functioning and adaptability, selecting states with robust basilar entropy production, as well as the capacity to enhance entropy production in response to exercise, heat stress, and illness. Finally, a targeted focus on optimizing our patients' entropy production has the potential to improve health and clinical outcomes. With the implications of developing a novel understanding of health, illness, and treatment strategies, further exploration of this uncharted ground will offer value.

High Breath-by-Breath Variability Is Associated With Extubation Failure in Children

OBJECTIVES

Extubation failure is multifactorial, and most tools to assess extubation readiness only evaluate snapshots of patient physiology. Understanding variability in respiratory variables may provide additional information to inform extubation readiness assessments.

DESIGN

Secondary analysis of prospectively collected physiologic data of children just prior to extubation during a spontaneous breathing trial. Physiologic data were cleaned to provide 40 consecutive breaths and calculate variability terms, coefficient of variation and autocorrelation, in commonly used respiratory variables (i.e., tidal volume, minute ventilation, and respiratory rate). Other clinical variables included diagnostic and demographic data, median values of respiratory variables during spontaneous breathing trials, and the change in airway pressure during an occlusion maneuver to measure respiratory muscle strength (maximal change in airway pressure generated during airway occlusion [PiMax]). Multivariable models evaluated independent associations with reintubation and prolonged use of noninvasive respiratory support after extubation.

SETTING

Acute care, children's hospital.

PATIENTS

Children were included from the pediatric and cardiothoracic ICUs who were greater than 37 weeks gestational age up to and including 18 years who were intubated greater than or equal to 12 hours with planned extubation. We excluded children who had a contraindication to an esophageal catheter or respiratory inductance plethysmography bands.

INTERVENTIONS

Noninterventional study.

MEASUREMENTS AND MAIN RESULTS

A total of 371 children were included, 32 of them were reintubated. Many variability terms were associated with reintubation, including coefficient of variation and autocorrelation of the respiratory rate. After controlling for confounding variables such as age and neurologic diagnosis, both coefficient of variation of respiratory rate(p < 0.001) and low PiMax (p = 0.002) retained an independent association with reintubation. Children with either low PiMax or high coefficient of variation of respiratory rate had a nearly three-fold higher risk of extubation failure, and when these children developed postextubation upper airway obstruction, reintubation rates were greater than 30%.

CONCLUSIONS

High respiratory variability during spontaneous breathing trials is independently associated with extubation failure in children, with very high rates of extubation failure when these children develop postextubation upper airway obstruction.

Heart Rate Variability, Clinical and Laboratory Measures to Predict Future Deterioration in Patients Presenting With Sepsis

BACKGROUND

Risk stratification of patients presenting to the emergency department (ED) with sepsis can be challenging. We derived and evaluated performance of a predictive model containing clinical, laboratory, and heart rate variability (HRV) measures to quantify risk of deterioration in this population.

METHODS

ED patients aged 21 and older satisfying the 1992 consensus conference criteria for sepsis and able to consent (directly or through a surrogate) were enrolled (n = 1,247). Patients had clinical, laboratory, and HRV data recorded within 1 h of ED presentation, and were followed to identify deterioration within 72 h.

RESULTS

Eight hundred thirty-two patients had complete data, of whom 68 (8%) reached at least one endpoint. Optimal predictive performance was derived from a combination of laboratory values and HRV metrics with an area under the receiver-operating curve (AUROC) of 0.80 (95% CI, 0.65-0.92). This combination of variables was superior to clinical (AUROC = 0.69, 95% CI, 0.54-0.83), laboratory (AUROC = 0.77, 95% CI, 0.63-0.90), and HRV measures (AUROC = 0.76, 95% CI, 0.61-0.90) alone. The HRV+LAB model identified a high-risk cohort of patients (14% of all patients) with a 4.3-fold (95% CI, 3.2-5.4) increased risk of deterioration (incidence of deterioration: 35%), as well as a low-risk group (61% of all patients) with 0.2-fold (95% CI 0.1-0.4) risk of deterioration (incidence of deterioration: 2%).

CONCLUSIONS

A model that combines HRV and laboratory values may help ED physicians evaluate risk of deterioration in patients with sepsis and merits validation and further evaluation.

Dynamic properties of glucose complexity during the course of critical illness: a pilot study

Methods to control the blood glucose (BG) levels of patients in intensive care units (ICU) improve the outcomes. The development of continuous BG levels monitoring devices has also permitted to optimize these processes. Recently it was shown that a complexity loss of the BG signal is linked to poor clinical outcomes. Thus, it becomes essential to decipher this relation to design efficient BG level control methods. In previous studies the BG signal complexity was calculated as a single index for the whole ICU stay. Although, these approaches did not grasp the potential variability of the BG signal complexity. Therefore, we setup this pilot study using a continuous monitoring of central venous BG levels in ten critically ill patients (EIRUS platform, Maquet Critical CARE AB, Solna, Sweden). Data were processed and the complexity was assessed by the detrended fluctuation analysis and multiscale entropy (MSE) methods. Finally, recordings were split into 24 h overlapping intervals and a MSE analysis was applied to each of them. The MSE analysis on time intervals revealed an entropy variation and allowed periodic BG signal complexity assessments. To highlight differences of MSE between each time interval we calculated the MSE complexity index defined as the area under the curve. This new approach could pave the way to future studies exploring new strategies aimed at restoring blood glucose complexity during the ICU stay.

A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems

Optimal levels of chaos and fractality are distinctly associated with physiological health and function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit seemingly random structures, whereas fractality is a measure of the extent of organization underlying such structures. Growing bodies of work are demonstrating both the importance of chaotic dynamics for proper function of natural systems, as well as the suitability of fractal mathematics for characterizing these systems. Here, we review how measures of fractality that quantify the dose of chaos may reflect the state of health across various biological systems, including: brain, skeletal muscle, eyes and vision, lungs, kidneys, tumours, cell regulation, skin and wound repair, bone, vasculature, and the heart. We compare how reports of either too little or too much chaos and fractal complexity can be damaging to normal biological function, and suggest that aiming for the healthy dose of chaos may be an effective strategy for various biomedical applications. We also discuss rising examples of the implementation of fractal theory in designing novel materials, biomedical devices, diagnostics, and clinical therapies. Finally, we explain important mathematical concepts of fractals and chaos, such as fractal dimension, criticality, bifurcation, and iteration, and how they are related to biology. Overall, we promote the effectiveness of fractals in characterizing natural systems, and suggest moving towards using fractal frameworks as a basis for the research and development of better tools for the future of biomedical engineering.

To extubate or not to extubate: Risk factors for extubation failure and deterioration with further mechanical ventilation

BACKGROUND

Extubation is a critical step in the intensive care unit (ICU). In this study, we aim to investigate the risk factors for both extubation failure and deterioration with further mechanical ventilation (MV).

METHODS

Data were collected from a cardiothoracic ICU in a tertiary hospital. The risk factors for extubation failure and deterioration with further MV were investigated by multivariate logistic regression.

RESULTS

A total of 676 patients were enrolled in the study. Patients with extubation failure had a longer ICU length of stay and a higher mortality rate than patients without extubation failure. An age greater than 65 years, abnormal heart rate, respiratory rate exceeding 20 times/min, arterial pH lower than 7.35, pressure of oxygen in arterial blood (PaO2)/fraction of inspired oxygen (FiO2) ratio lower than 300 mmHg, mean arterial pressure lower than 70 mmHg, duration of MV longer than 12 hours, and high quick Sequential Organ Failure Assessment (qSOFA) score were independent risk factors for extubation failure. Furthermore, we found that a respiratory rate greater than 20 times/min and a PaO2/fraction of Inspired Oxygen FiO2 ratio less than 300 mmHg were protective factors, while a mean arterial pressure lower than 70 mmHg, arterial pH lower than 7.35, and high qSOFA score were risk factors for deterioration on continued MV.

CONCLUSIONS

Since the duration of MV increases the risk of extubation failure, physicians should consider not only the risk of extubation failure but also the risk of deterioration with further MV.

Ventilatory pattern variability as a biometric for severity of acute lung injury in rats

We hypothesize that ventilatory pattern variability (VPV) varies with the magnitude of acute lung injury (ALI). In adult male rats, we instilled a low- or high- dose of bleomycin or saline (PBS) intratracheally. While representative samples of pulmonary tissue indicated graded lung injury, coefficient of variation (CV) of TTOT did not differ among the 3 groups. Broncho-alveolar lavage fluid (BALF), respiratory rate (fR), mutual information were greater in ALI than sham rats; but did not differ between bleomycin doses. However, nonlinear complexity index (NLCI), which is the difference in sample entropy between original and surrogate data sets was greater for high- versus low- dose; but did not differ between low-dose and sham groups. Further, NLCI correlated to an injury index based on protein concentration of BALF and failure to gain weight. Finally, Receiver Operator Curves (ROCs) indicated that both mutual information and NLCI had greater sensitivity and specificity than fR and CVTTOT in identifying ALI. Thus, nonlinear analyses of VPV can distinguish ALI and out performs fR as a biometric.

Diffusing an Innovation: Clinician Perceptions of Continuous Predictive Analytics Monitoring in Intensive Care

BACKGROUND

The purpose of this article is to describe neonatal intensive care unit clinician perceptions of a continuous predictive analytics technology and how those perceptions influenced clinician adoption. Adopting and integrating new technology into care is notoriously slow and difficult; realizing expected gains remain a challenge.

METHODS

Semistructured interviews from a cross-section of neonatal physicians (n = 14) and nurses (n = 8) from a single U.S. medical center were collected 18 months following the conclusion of the predictive monitoring technology randomized control trial. Following qualitative descriptive analysis, innovation attributes from Diffusion of Innovation Theory-guided thematic development.

RESULTS

Results suggest that the combination of physical location as well as lack of integration into work flow or methods of using data in care decisionmaking may have delayed clinicians from routinely paying attention to the data. Once data were routinely collected, documented, and reported during patient rounds and patient handoffs, clinicians came to view data as another vital sign. Through clinicians' observation of senior physicians and nurses, and ongoing dialogue about data trends and patient status, clinicians learned how to integrate these data in care decision making (e.g., differential diagnosis) and came to value the technology as beneficial to care delivery.

DISCUSSION

The use of newly created predictive technologies that provide early warning of illness may require implementation strategies that acknowledge the risk-benefit of treatment clinicians must balance and take advantage of existing clinician training methods.

Autonomic Nervous System Dysfunction in Pediatric Sepsis

The autonomic nervous system (ANS) plays a major role in maintaining homeostasis through key adaptive responses to stress, including severe infections and sepsis. The ANS-mediated processes most relevant during sepsis include regulation of cardiac output and vascular tone, control of breathing and airway resistance, inflammation and immune modulation, gastrointestinal motility and digestion, and regulation of body temperature. ANS dysfunction (ANSD) represents an imbalanced or maladaptive response to injury and is prevalent in pediatric sepsis. Most of the evidence on ANSD comes from studies of heart rate variability, which is a marker of ANS function and is inversely correlated with organ dysfunction and mortality. In addition, there is evidence that other measures of ANSD, such as respiratory rate variability, skin thermoregulation, and baroreflex and chemoreflex sensitivity, are associated with outcomes in critical illness. The relevance of understanding ANSD in the context of pediatric sepsis stems from the fact that it might play an important role in the pathophysiology of sepsis, is associated with outcomes, and can be measured continuously and noninvasively. Here we review the physiology and dysfunction of the ANS during critical illness, discuss methods for measuring ANS function in the intensive care unit, and review the diagnostic, prognostic, and therapeutic value of understanding ANSD in pediatric sepsis.

Mid-cervical spinal cord contusion causes robust deficits in respiratory parameters and pattern variability

Mid-cervical spinal cord contusion disrupts both the pathways and motoneurons vital to the activity of inspiratory muscles. The present study was designed to determine if a rat contusion model could result in a measurable deficit to both ventilatory and respiratory motor function under “normal” breathing conditions at acute to chronic stages post trauma. Through whole body plethysmography and electromyography we assessed respiratory output from three days to twelve weeks after a cervical level 3 (C3) contusion. Contused animals showed significant deficits in both tidal and minute volumes which were sustained from acute to chronic time points. We also examined the degree to which the contusion injury impacted ventilatory pattern variability through assessment of Mutual Information and Sample Entropy. Mid-cervical contusion significantly and robustly decreased the variability of ventilatory patterns. The enduring deficit to the respiratory motor system caused by contusion was further confirmed through electromyography recordings in multiple respiratory muscles. When isolated via a lesion, these contused pathways were insufficient to maintain respiratory activity at all time points post injury. Collectively these data illustrate that, counter to the prevailing literature, a profound and lasting ventilatory and respiratory motor deficit may be modelled and measured through multiple physiological assessments at all time points after cervical contusion injury.

Respiratory Rate Variability as a Prognostic Factor in Hospitalized Patients Transferred to the Intensive Care Unit

Introduction

Increasing mortality rates within the intensive care unit (ICU) is an ever growing problem, ultimately leading to increases in the cost of healthcare expenditures. Currently, there are attempts to use guidelines in the hospital setting to predict overall mortality in critically ill patients. However, a predictor of subsequent ICU admissions remains to be explored. Recent data has shown the importance of monitoring respiratory rate variability (RRV) as a useful predictor of the deterioration of patients. Respiratory rate, in comparison to blood pressure or pulse rate, is deemed as the better determinant in identifying high-risk patients.

Aim

Our study aims to assess the role of RRV monitoring as a potential prognostic marker predictive of ICU admission.

Results

There was a significant (p = 0.009) increase in RRV between the third and fourth set of respiratory rates prior to ICU admission, such that coefficient of variation percentage (CV%) increased from 0.3% (95% confidence interval (CI): 0.09 - 0.42) to 0.7% (95% CI: 0.04 - 0.9) about 12 hours before admission to the ICU independent from diagnosis.

Conclusion

Using elevated RRV as a signal may be a useful prognostic tool in providing early intervention, thus reducing the incidence of subsequent morbidity and mortality in patients that might necessitate an ICU admission.

Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet

Respiratory diseases are highly complex, being driven by host-environment interactions and manifested by inflammatory, structural, and functional abnormalities that vary over time. Traditional reductionist approaches have contributed vastly to our knowledge of biological systems in health and disease to date; however, they are insufficient to provide an understanding of the behavior of the system as a whole. In this Pulmonary Perspective, we discuss systems biology approaches, especially but not limited to the study of the lung as a complex system. Such integrative approaches take into account the large number of dynamic subunits and their interactions found in biological systems. Borrowing methods from physics and mathematics, it is possible to study the collective behavior of these systems over time and in a multidimensional manner. We first examine the physiological basis for complexity in the respiratory system and its implications for disease. We then expand on the potential applications of systems biology methods to study complex systems, within the context of diagnosis and monitoring of respiratory diseases including asthma, chronic obstructive pulmonary disease (COPD), and critical illness. We summarize the significant advances made in recent years using systems approaches for disease phenotyping, applied to data ranging from the molecular to clinical level, obtained from large-scale asthma and COPD networks. We describe new studies using temporal complexity patterns to characterize asthma and COPD and predict exacerbations as well as predict adverse outcomes in critical care. We highlight new methods that are emerging with this approach and discuss remaining questions that merit greater attention in the field.

A Framework for Patient State Tracking by Classifying Multiscalar Physiologic Waveform Features

OBJECTIVE

state-of-the-art algorithms that quantify nonlinear dynamics in physiologic waveforms are underutilized clinically due to their esoteric nature. We present a generalizable framework for classifying multiscalar waveform features, designed for patient-state tracking directly at the bedside.

METHODS

an artificial neural network classifier was designed to evaluate multiscale waveform features against a fingerprint database of multifractal synthetic time series. The results are mapped into a physiologic state space for near real-time patient-state tracking.

RESULTS

the framework was validated on cardiac beat-to-beat dynamics processed with the multiscale entropy algorithm, and assessed using PhysioNet databases. We then applied our algorithm to predict 28-day mortality for sepsis patients, and found it had greater prognostic accuracy than standard clinical severity scores.

CONCLUSION

we developed a novel framework to classify multiscale features of beat-to-beat dynamics, and performed an initial clinical validation to demonstrate that our approach generates a robust quantification of a patient's state, compatible with real-time bedside implementations.

SIGNIFICANCE

the framework generates meaningful and actionable patient-specific information, and could facilitate the dissemination of a new class of "always-on" diagnostic tools.

Monitoring Severity of Multiple Organ Dysfunction Syndrome: New Technologies

OBJECTIVE

To describe new technologies (biomarkers and tests) used to assess and monitor the severity and progression of multiple organ dysfunction syndrome in children as discussed as part of the Eunice Kennedy Shriver National Institute of Child Health and Human Development MODS Workshop (March 26-27, 2015).

DATA SOURCES

Literature review, research data, and expert opinion.

STUDY SELECTION

Not applicable.

DATA EXTRACTION

Moderated by an experienced expert from the field, investigators developing and assessing new technologies to improve the care and understanding of critical illness presented their research and the relevant literature.

DATA SYNTHESIS

Summary of presentations and discussion supported and supplemented by relevant literature.

CONCLUSIONS

There are many innovative tools and techniques with the potential application for the assessment and monitoring of severity of multiple organ dysfunction syndrome. If the reliability and added value of these candidate technologies can be established, they hold promise to enhance the understanding, monitoring, and perhaps, treatment of multiple organ dysfunction syndrome in children.

Multi-complexity measures of heart rate variability and the effect of vasopressor titration: a prospective cohort study of patients with septic shock

Background

Septic shock is a common and often devastating syndrome marked by severe cardiovascular dysfunction commonly managed with vasopressors. Whether markers of heart rate complexity before vasopressor up-titration could be used to predict success of the up-titration is not known.

Methods

We studied patients with septic shock requiring vasopressor, newly admitted to the intensive care unit. We measured the complexity of heart rate variability (using the ratio of fractal exponents from detrended fluctuation analysis) in the 5 min before all vasopressor up-titrations in the first 24 h of an intensive care unit (ICU) admission. A successful up-titration was defined as one that did not require further up-titration (or decrease in mean arterial pressure) for 60 min.

Results

We studied 95 patients with septic shock, with a median APACHE II of 27 (IQR: 20–37). The median number of up-titrations, normalized to 24 h, was 12.2 (IQR: 8–17) with a maximum of 49. Of the up-titrations, the median proportion of successful interventions was 0.28 (IQR: 0.12–0.42). The median of mean arterial pressure (MAP) at the time of a vasopressor up-titration was 66 mmHg; the average infusion rate of norepinephrine at the time of an up-titration was 0.11 mcg/kg/min. The ratio of fractal exponents was not associated with successful up-titration on univariate or multivariate regression. On exploratory secondary analyses, however, the long-term fractal exponent was associated (p = 0.003) with success of up-titration. Independent of heart rate variability, MAP was associated (p < 0.001) with success of vasopressor up-titration, while neither Sequential Organ Failure Assessment (SOFA) nor Acute Physiology and Chronic Health Evaluation II (APACHE II) score was associated with vasopressor titration.

Conclusions

Only a third of vasopressor up-titrations were successful among patients with septic shock. MAP and the long-term fractal exponent were associated with success of up-titration. These two, complementary variables may be important to the development of rational vasopressor titration protocols.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-1896-1) contains supplementary material, which is available to authorized users.

Practice Variation in Spontaneous Breathing Trial Performance and Reporting

Background. Spontaneous breathing trials (SBTs) are standard of care in assessing extubation readiness; however, there are no universally accepted guidelines regarding their precise performance and reporting. Objective. To investigate variability in SBT practice across centres. Methods. Data from 680 patients undergoing 931 SBTs from eight North American centres from the Weaning and Variability Evaluation (WAVE) observational study were examined. SBT performance was analyzed with respect to ventilatory support, oxygen requirements, and sedation level using the Richmond Agitation Scale Score (RASS). The incidence of use of clinical extubation criteria and changes in physiologic parameters during an SBT were assessed. Results. The majority (80% and 78%) of SBTs used 5 cmH₂O of ventilator support, although there was variability. A significant range in oxygenation was observed. RASS scores were variable, with RASS 0 ranging from 29% to 86% and 22% of SBTs performed in sedated patients (RASS < −2). Clinical extubation criteria were heterogeneous among centres. On average, there was no change in physiological variables during SBTs. Conclusion. The present study highlights variation in SBT performance and documentation across and within sites. With their impact on the accuracy of outcome prediction, these results support efforts to further clarify and standardize optimal SBT technique.

Classification of Asthma Based on Nonlinear Analysis of Breathing Pattern

Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty age-matched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing pattern was quantified by calculating detrended fluctuation analysis, largest Lyapunov exponents, sample entropy, and cross-sample entropy. The IBI as well as LV fluctuations showed decreased long-range correlation, increased regularity and reduced sensitivity to initial conditions in patients with asthma, particularly in uncontrolled state. Our results also showed a strong synchronization between the IBI and LV in patients with uncontrolled asthma. Receiver operating characteristic (ROC) curve analysis showed that nonlinear analysis of breathing pattern has a diagnostic value in asthma and can be used in differentiating uncontrolled from controlled and non-atopic from atopic asthma. We suggest that complexity analysis of breathing dynamics may represent a novel physiologic marker to facilitate diagnosis and management of patients with asthma. However, future studies are needed to increase the validity of the study and to improve these novel methods for better patient management.

Ventilator Weaning and Spontaneous Breathing Trials; an Educational Review

The term "weaning" is used to describe the gradual process of decreasing ventilator support. It is estimated that 40% of the duration of mechanical ventilation is dedicated to the process of weaning. Spontaneous breathing trial (SBT) assesses the patient's ability to breathe while receiving minimal or no ventilator support. The collective task force in 2001 stated that the process of SBT and weaning should start by assessing whether the underlying cause of respiratory failure has been resolved or not. Weaning predictors are parameters that are intended to help clinicians predict whether weaning attempts will be successful or not. Although the international consensus conference in 2005 did not recommend their routine use for clinical decision making, researchers did not stop working in this area. In the present article, we review some of the recent studies about weaning predictors, criteria, procedure, as well as assessment for extubation a mechanically ventilated patient.

Lung cancer-a fractal viewpoint

Fractals are mathematical constructs that show self-similarity over a range of scales and non-integer (fractal) dimensions. Owing to these properties, fractal geometry can be used to efficiently estimate the geometrical complexity, and the irregularity of shapes and patterns observed in lung tumour growth (over space or time), whereas the use of traditional Euclidean geometry in such calculations is more challenging. The application of fractal analysis in biomedical imaging and time series has shown considerable promise for measuring processes as varied as heart and respiratory rates, neuronal cell characterization, and vascular development. Despite the advantages of fractal mathematics and numerous studies demonstrating its applicability to lung cancer research, many researchers and clinicians remain unaware of its potential. Therefore, this Review aims to introduce the fundamental basis of fractals and to illustrate how analysis of fractal dimension (FD) and associated measurements, such as lacunarity (texture) can be performed. We describe the fractal nature of the lung and explain why this organ is particularly suited to fractal analysis. Studies that have used fractal analyses to quantify changes in nuclear and chromatin FD in primary and metastatic tumour cells, and clinical imaging studies that correlated changes in the FD of tumours on CT and/or PET images with tumour growth and treatment responses are reviewed. Moreover, the potential use of these techniques in the diagnosis and therapeutic management of lung cancer are discussed.

Big Data Analytics in Healthcare

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.