Christophe Herry

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

IntroductionProlonged 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.MethodsWe 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.ResultsOf 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.ConclusionsAltered 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 registrationClinicalTrials.gov NCT01237886. Registered 13 October 2010.

Does heart rate variability reflect the systemic inflammatory response in a fetal sheep model of lipopolysaccharide-induced sepsis?

Fetal inflammatory response occurs during chorioamnionitis, a frequent and often subclinical inflammation associated with increased risk for brain injury and life-lasting neurologic deficits. No means of early detection exist. We hypothesized that systemic fetal inflammation without septic shock will be reflected in alterations of fetal heart rate (FHR) variability (fHRV) distinguishing baseline versus inflammatory response states. In chronically instrumented near-term fetal sheep (n = 24), we induced an inflammatory response with lipopolysaccharide (LPS) injected intravenously (n = 14). Ten additional fetuses served as controls. We measured fetal plasma inflammatory cytokine IL-6 at baseline, 1, 3, 6, 24 and 48 h. 44 fHRV measures were determined continuously every 5 min using continuous individualized multi-organ variability analysis (CIMVA). CIMVA creates an fHRV measures matrix across five signal-analytical domains, thus describing complementary properties of fHRV. Using principal component analysis (PCA), a widely used technique for dimensionality reduction, we derived and quantitatively compared the CIMVA fHRV PCA signatures of inflammatory response in LPS and control groups. In the LPS group, IL-6 peaked at 3 h. In parallel, PCA-derived fHRV composite measures revealed a significant difference between LPS and control group at different time points. For the LPS group, a sharp increase compared to baseline levels was observed between 3 h and 6 h, and then abating to baseline levels, thus tracking closely the IL-6 inflammatory profile. This pattern was not observed in the control group. We also show that a preselection of fHRV measures prior to the PCA can potentially increase the difference between LPS and control groups, as early as 1 h post LPS injection. We propose a fHRV composite measure that correlates well with levels of inflammation and tracks well its temporal profile. Our results highlight the potential role of HRV to study and monitor the inflammatory response non-invasively over time.

Fractal Structure and Entropy Production within the Central Nervous System

Abstract: Our goal is to explore the relationship between two traditionally unrelated concepts, fractal structure and entropy production, evaluating both within the central nervous system (CNS). Fractals are temporal or spatial structures with self-similarity across scales of measurement; whereas entropy production represents the necessary exportation of entropy to our environment that comes with metabolism and life. Fractals may be measured by their fractal dimension; and human entropy production may be estimated by oxygen and glucose metabolism. In this paper, we observe fractal structures ubiquitously present in the CNS, and explore a hypothetical and unexplored link between fractal structure and entropy production, as measured by oxygen and glucose metabolism. Rapid increase in both fractal structures and metabolism occur with childhood and adolescent growth, followed by slow decrease during aging. Concomitant increases and decreases in fractal structure and metabolism occur with cancer

Can Monitoring Fetal Intestinal Inflammation Using Heart Rate Variability Analysis Signal Incipient Necrotizing Enterocolitis of the Neonate

Objective: Necrotizing enterocolitis of the neonate is an acute inflammatory intestinal disease that can cause necrosis and sepsis. Chorioamnionitis is a risk factor of necrotizing enterocolitis. The gut represents the biggest vagus-innervated organ. Vagal activity can be measured via fetal heart rate variability. We hypothesized that fetal heart rate variability can detect fetuses with incipient gut inflammation. Design: Prospective animal study. Setting: University research laboratory. Subjects: Chronically instrumented near-term fetal sheep (n = 21). Measurements and Main Results: Animals were surgically instrumented with vascular catheters and electrocardiogram to allow manipulation and recording from nonanesthetized animals. In 14 fetal sheep, inflammation was induced with lipopolysaccharide (IV) to mimic chorioamnionitis. Fetal arterial blood samples were drawn at selected time points over 54 hours post lipopolysaccharide for blood gas and cytokines (interleukin-6 and tumor necrosis factor-&agr; enzymelinked immunosorbent assay). Fetal heart rateV was quantified throughout the experiment. The time-matched fetal heart rate variability measures were correlated to the levels of interleukin-6 and tumor necrosis factor-&agr;. Upon necropsy, ionized calcium binding adaptor molecule 1+ (Iba1+), CD11c+ (M1), CD206+ (M2 macrophages), and occludin (leakiness marker) immunofluorescence in the terminal ileum was quantified along with regional Iba1+ signal in the brain (microglia). Interleukin-6 peaked at 3 hours post lipopolysaccharide accompanied by mild cardiovascular signs of sepsis. At 54 hours, we identified an increase in Iba1+ and, specifically, M1 macrophages in the ileum accompanied by increased leakiness, with no change in Iba1 signal in the brain. Preceding this change on tissue level, at 24 hours, a subset of nine fetal heart rate variability measures correlated exclusively to the Iba+ markers of ileal, but not brain, inflammation. An additional fetal heart rate variability measure, mean of the differences of R-R intervals, correlated uniquely to M1 ileum macrophages increasing due to lipopolysaccharide. Conclusions: We identified a unique subset of fetal heart rate variability measures reflecting 1.5 days ahead of time the levels of macrophage activation and increased leakiness in terminal ileum. We propose that such subset of fetal heart rate variability measures reflects brain-gut communication via the vagus nerve. Detecting such noninvasively obtainable organ-specific fetal heart rate variability signature of inflammation would alarm neonatologists about neonates at risk of developing necrotizing enterocolitis and sepsis. Clinical validation studies are required.

Heart rate variability during high heat stress: a comparison between young and older adults with and without type 2 diabetes

We examined whether older individuals with and without Type 2 diabetes (T2D) experience differences in heart rate variability (HRV) during a 3-h exposure to high heat stress compared with young adults. Young (Young; n = 22; 23 ± 3 yr) and older individuals with (T2D; n = 11; 59 ± 9 yr) and without (Older; n = 25; 63 ± 5 yr) T2D were exposed to heat stress (44°C, 30% relative humidity) for 3 h. Fifty-five HRV measures were assessed for 15 min at baseline and at minutes 82.5-97.5 (Mid) and minutes 165-180 (End) during heat stress. When compared with Young, a similar number of HRV indices were significantly different (P < 0.05) in Older (Baseline: 35; Mid: 29; End: 32) and T2D (Baseline: 31; Mid: 30; End: 27). In contrast, the number of HRV indices significantly different (P < 0.05) between Older and T2D were far fewer (Baseline: 13, Mid: 1, End: 3). Within-group analyses demonstrated a greater change in the Young groups HRV during heat stress compared with Older and T2D; the number of significantly different (P < 0.05) HRV indices between baseline and End were 42, 29, and 20, for Young, Older, and T2D, respectively. Analysis of specific HRV domains suggest that the Young group experienced greater sympathetic activity during heat stress compared with Older and T2D. In conclusion, when compared with young, older individuals with and without T2D demonstrate low HRV at baseline and less change in HRV (including an attenuated sympathetic response) during 3 h high heat stress, potentially contributing to impaired thermoregulatory function.

Heart beat classification from single-lead ECG using the synchrosqueezing transform

The processing of ECG signal provides a wealth of information on cardiac function and overall cardiovascular health. While multi-lead ECG recordings are often necessary for a proper assessment of cardiac rhythms, they are not always available or practical, for example in fetal ECG applications. Moreover, a wide range of small non-obtrusive single-lead ECG ambulatory monitoring devices are now available, from which heart rate variability (HRV) and other health-related metrics are derived. Proper beat detection and classification of abnormal rhythms is important for reliable HRV assessment and can be challenging in single-lead ECG monitoring devices. In this manuscript, we modelled the heart rate signal as an adaptive non-harmonic model and used the newly developed synchrosqueezing transform (SST) to characterize ECG patterns. We show how the proposed model can be used to enhance heart beat detection and classification between normal and abnormal rhythms. In particular, using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and the Association for the Advancement of Medical Instrumentation (AAMI) beat classes, we trained and validated a support vector machine (SVM) classifier on a portion of the annotated beat database using the SST-derived instantaneous phase, the R-peak amplitudes and R-peak to R-peak interval durations, based on a single ECG lead. We obtained sentivities and positive predictive values comparable to other published algorithms using multiple leads and many more features.

Do physiological and pathological stresses produce different changes in heart rate variability

Although physiological (e.g., exercise) and pathological (e.g., infection) stress affecting the cardiovascular system have both been documented to be associated with a reduction in overall heart rate variability (HRV), it remains unclear if loss of HRV is ubiquitously similar across different domains of variability analysis or if distinct patterns of altered HRV exist depending on the stressor. Using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software, heart rate (HR) and four selected measures of variability were measured over time (windowed analysis) from two datasets, a set (n = 13) of patients who developed systemic infection (i.e., sepsis) after bone marrow transplant (BMT), and a matched set of healthy subjects undergoing physical exercise under controlled conditions. HR and the four HRV measures showed similar trends in both sepsis and exercise. The comparison through Wilcoxon sign-rank test of the levels of variability at baseline and during the stress (i.e., exercise or after days of sepsis development) showed similar changes, except for LF/HF, ratio of power at low (LF) and high (HF) frequencies (associated with sympathovagal modulation), which was affected by exercise but did not show any change during sepsis. Furthermore, HRV measures during sepsis showed a lower level of correlation with each other, as compared to HRV during exercise. In conclusion, this exploratory study highlights similar responses during both exercise and infection, with differences in terms of correlation and inter-subject fluctuations, whose physiologic significance merits further investigation.

Temporal Patterns in Sheep Fetal Heart Rate Variability Correlate to Systemic Cytokine Inflammatory Response: A Methodological Exploration of Monitoring Potential Using Complex Signals Bioinformatics

Fetal inflammation is associated with increased risk for postnatal organ injuries. No means of early detection exist. We hypothesized that systemic fetal inflammation leads to distinct alterations of fetal heart rate variability (fHRV). We tested this hypothesis deploying a novel series of approaches from complex signals bioinformatics. In chronically instrumented near-term fetal sheep, we induced an inflammatory response with lipopolysaccharide (LPS) injected intravenously (n = 10) observing it over 54 hours; seven additional fetuses served as controls. Fifty-one fHRV measures were determined continuously every 5 minutes using Continuous Individualized Multi-organ Variability Analysis (CIMVA). CIMVA creates an fHRV measures matrix across five signal-analytical domains, thus describing complementary properties of fHRV. We implemented, validated and tested methodology to obtain a subset of CIMVA fHRV measures that matched best the temporal profile of the inflammatory cytokine IL-6. In the LPS group, IL-6 peaked at 3 hours. For the LPS, but not control group, a sharp increase in standardized difference in variability with respect to baseline levels was observed between 3 h and 6 h abating to baseline levels, thus tracking closely the IL-6 inflammatory profile. We derived fHRV inflammatory index (FII) consisting of 15 fHRV measures reflecting the fetal inflammatory response with prediction accuracy of 90%. Hierarchical clustering validated the selection of 14 out of 15 fHRV measures comprising FII. We developed methodology to identify a distinctive subset of fHRV measures that tracks inflammation over time. The broader potential of this bioinformatics approach is discussed to detect physiological responses encoded in HRV measures.

Segmentation and classification of capnograms: application in respiratory variability analysis.

Variability analysis of respiratory waveforms has been shown to provide key insights into respiratory physiology and has been used successfully to predict clinical outcomes. The current standard for quality assessment of the capnogram signal relies on a visual analysis performed by an expert in order to identify waveform artifacts. Automated processing of capnograms is desirable in order to extract clinically useful features over extended periods of time in a patient monitoring environment. However, the proper interpretation of capnogram derived features depends upon the quality of the underlying waveform. In addition, the comparison of capnogram datasets across studies requires a more practical approach than a visual analysis and selection of high-quality breath data. This paper describes a system that automatically extracts breath-by-breath features from capnograms and estimates the quality of individual breaths derived from them. Segmented capnogram breaths were presented to expert annotators, who labeled the individual physiological breaths into normal and multiple abnormal breath types. All abnormal breath types were aggregated into the abnormal class for the purpose of this manuscript, with respiratory variability analysis as the end-application. A database of 11,526 breaths from over 300 patients was created, comprising around 35% abnormal breaths. Several simple classifiers were trained through a stratified repeated ten-fold cross-validation and tested on an unseen portion of the labeled breath database, using a subset of 15 features derived from each breath curve. Decision Tree, K-Nearest Neighbors (KNN) and Naive Bayes classifiers were close in terms of performance (AUC of 90%, 89% and 88% respectively), while using 7, 4 and 5 breath features, respectively. When compared to airflow derived timings, the 95% confidence interval on the mean difference in interbreath intervals was ± 0.18 s. This breath classification system provides a fast and robust pre-processing of continuous respiratory waveforms, thereby ensuring reliable variability analysis of breath-by-breath parameter time series.

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.