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.

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 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.

The duration of a sit-to-stand (SiSt) transfer is a representative measure of a person's status of physical mobility. This paper measured the duration unobtrusively and automatically using a pressure sensor array under a bed mattress and a floor plate beside the bed. Pressure sequences were extracted from frames of sensor data measuring bed and floor pressure over time. The start time was determined by an algorithm based on the motion of the center of pressure (COP) on the mattress toward the front edge of the bed. The end time was determined by modeling the foot pressure exerted on the floor in the wavelet domain as the step response of a third-order transfer function. As expected, young and old healthy adults generated shorter SiSt durations of around 2.31 and 2.88 s, respectively, whereas post-hip fracture and post-stroke adults produced longer SiSt durations of around 3.32 and 5.00 s. The unobtrusive nature of pressure sensing techniques used in this paper provides valuable information that can be used for the ongoing monitoring of patients within extended-care facilities or within the smart home environment.

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Heat acclimation is accompanied by reduced core temperature, significant bradycardia, and marked alterations in HRV, which we interpret as being related to vagal dominance. The observed changes in core temperature persist for at least 2 weeks of non-exposure to heat, while the changes in heart rate and HRV decay faster and are only partly evident after 2 weeks of non-exposure to heat.

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.

Prevalence, symptoms, and treatment of depression suggest that major depressive disorders (MDD) present sex differences. Social stress-induced neurovascular pathology is associated with depressive symptoms in male mice; however, this association is unclear in females. Here, we report that chronic social and subchronic variable stress promotes blood-brain barrier (BBB) alterations in mood-related brain regions of female mice. Targeted disruption of the BBB in the female prefrontal cortex (PFC) induces anxiety- and depression-like behaviours. By comparing the endothelium cell-specific transcriptomic profiling of the mouse male and female PFC, we identify several pathways and genes involved in maladaptive stress responses and resilience to stress. Furthermore, we confirm that the BBB in the PFC of stressed female mice is leaky. Then, we identify circulating vascular biomarkers of chronic stress, such as soluble E-selectin. Similar changes in circulating soluble E-selectin, BBB gene expression and morphology can be found in blood serum and postmortem brain samples from women diagnosed with MDD. Altogether, we propose that BBB dysfunction plays an important role in modulating stress responses in female mice and possibly MDD.

Fetal monitoring during labour currently fails to accurately detect acidemia. We developed a method to assess the multidimensional properties of fetal heart rate variability (fHRV) from trans-abdominal fetal electrocardiogram (fECG) during labour. We aimed to assess this novel bioinformatics approach for correlation between fHRV and neonatal pH or base excess (BE) at birth.We enrolled a prospective pilot cohort of uncomplicated singleton pregnancies at 38-42 weeks' gestation in Milan, Italy, and Liverpool, UK. Fetal monitoring was performed by standard cardiotocography. Simultaneously, with fECG (high sampling frequency) was recorded. To ensure clinician blinding, fECG information was not displayed. Data from the last 60 min preceding onset of second-stage labour were analyzed using clinically validated continuous individualized multiorgan variability analysis (CIMVA) software in 5 min overlapping windows. CIMVA allows simultaneous calculation of 101 fHRV measures across five fHRV signal analysis domains. We validated our mathematical prediction model internally with 80:20 cross-validation split, comparing results to cord pH and BE at birth.The cohort consisted of 60 women with neonatal pH values at birth ranging from 7.44 to 6.99 and BE from -0.3 to -18.7 mmol L(-1). Our model predicted pH from 30 fHRV measures (R(2) = 0.90, P < 0.001) and BE from 21 fHRV measures (R(2) = 0.77, P < 0.001).Novel bioinformatics approach (CIMVA) applied to fHRV derived from trans-abdominal fECG during labor correlated well with acid-base balance at birth. Further refinement and validation in larger cohorts are needed. These new measurements of fHRV might offer a new opportunity to predict fetal acid-base balance at birth.