Abstract:Our analysis suggested that the peaks detection algorithm based on a sliding window approach is feasible for detecting simultaneous peaks, e.g. cross-talk events in the ICP and HR signals.
“…In this figure the black dots represent recurrent points in the system and the lines parallel to the diagonal, show the determinism of the two time series considered. Moreover the similarity in the recurrence plots suggests the presence of an interaction between the two time series ( Dimitri et al 2017 ). Fig.…”
Section: Resultsmentioning
confidence: 98%
“…We implemented a naive sliding window approach for the detection of cross talks events in the time series ( Dimitri et al 2017 ). The goal was to identify the presence of episodes, where both ICP and HR increased by 20% with respect to the minimum value in a given window of the time series.…”
BackgroundWe present a multiplex network model for the analysis of Intracranial Pressure (ICP) and Heart Rate (HR) behaviour after severe brain traumatic injuries in pediatric patients. The ICP monitoring is of vital importance for checking life threathening conditions, and understanding the behaviour of these parameters is crucial for a successful intervention of the clinician. Our own observations, exhibit cross-talks interaction events happening between HR and ICP, i.e. transients in which both the ICP and the HR showed an increase of 20% with respect to their baseline value in the window considered. We used a complex event processing methodology, to investigate the relationship between HR and ICP, after traumatic brain injuries (TBI). In particular our goal has been to analyse events of simultaneous increase by HR and ICP (i.e. cross-talks), modelling the two time series as a unique multiplex network system (Lacasa et al., Sci Rep 5:15508-15508, 2014).Methods and dataWe used a complex network approach based on visibility graphs (Lacasa et al., Sci Rep 5:15508-15508, 2014) to model and study the behaviour of our system and to investigate how and if network topological measures can give information on the possible detection of crosstalks events taking place in the system. Each time series was converted as a layer in a multiplex network. We therefore studied the network structure, focusing on the behaviour of the two time series in the cross-talks events windows detected. We used a dataset of 27 TBI pediatric patients, admitted to Addenbrooke’s Hospital, Cambridge, Pediatric Intensive Care Unit (PICU) between August 2012 and December 2014.ResultsFollowing a preliminary statistical exploration of the two time series of ICP and HR, we analysed the multiplex network proposed, focusing on two standard topological network metrics: the mutual interaction, and the average edge overlap (Lacasa et al., Sci Rep 5:15508-15508, 2014). We compared results obtained for these two indicators, considering windows in which a cross talks event between HR and ICP was detected with windows in which cross talks events were not present. The analysis of such metrics gave us interesting insights on the time series behaviour. More specifically we observed an increase in the value of the mutual interaction in the case of cross talk as compared to non cross talk. This seems to suggest that mutual interaction could be a potentially interesting “marker” for cross talks events.
“…In this figure the black dots represent recurrent points in the system and the lines parallel to the diagonal, show the determinism of the two time series considered. Moreover the similarity in the recurrence plots suggests the presence of an interaction between the two time series ( Dimitri et al 2017 ). Fig.…”
Section: Resultsmentioning
confidence: 98%
“…We implemented a naive sliding window approach for the detection of cross talks events in the time series ( Dimitri et al 2017 ). The goal was to identify the presence of episodes, where both ICP and HR increased by 20% with respect to the minimum value in a given window of the time series.…”
BackgroundWe present a multiplex network model for the analysis of Intracranial Pressure (ICP) and Heart Rate (HR) behaviour after severe brain traumatic injuries in pediatric patients. The ICP monitoring is of vital importance for checking life threathening conditions, and understanding the behaviour of these parameters is crucial for a successful intervention of the clinician. Our own observations, exhibit cross-talks interaction events happening between HR and ICP, i.e. transients in which both the ICP and the HR showed an increase of 20% with respect to their baseline value in the window considered. We used a complex event processing methodology, to investigate the relationship between HR and ICP, after traumatic brain injuries (TBI). In particular our goal has been to analyse events of simultaneous increase by HR and ICP (i.e. cross-talks), modelling the two time series as a unique multiplex network system (Lacasa et al., Sci Rep 5:15508-15508, 2014).Methods and dataWe used a complex network approach based on visibility graphs (Lacasa et al., Sci Rep 5:15508-15508, 2014) to model and study the behaviour of our system and to investigate how and if network topological measures can give information on the possible detection of crosstalks events taking place in the system. Each time series was converted as a layer in a multiplex network. We therefore studied the network structure, focusing on the behaviour of the two time series in the cross-talks events windows detected. We used a dataset of 27 TBI pediatric patients, admitted to Addenbrooke’s Hospital, Cambridge, Pediatric Intensive Care Unit (PICU) between August 2012 and December 2014.ResultsFollowing a preliminary statistical exploration of the two time series of ICP and HR, we analysed the multiplex network proposed, focusing on two standard topological network metrics: the mutual interaction, and the average edge overlap (Lacasa et al., Sci Rep 5:15508-15508, 2014). We compared results obtained for these two indicators, considering windows in which a cross talks event between HR and ICP was detected with windows in which cross talks events were not present. The analysis of such metrics gave us interesting insights on the time series behaviour. More specifically we observed an increase in the value of the mutual interaction in the case of cross talk as compared to non cross talk. This seems to suggest that mutual interaction could be a potentially interesting “marker” for cross talks events.
“…In our own previous work [11], we proposed an algorithm to detect the presence of brain-heart crosstalks given two time series of ICP and HR monitored in patients with TBI.…”
Section: Brain-heart Crosstalks Detection and Analysismentioning
confidence: 99%
“…Such measure was defined to facilitate the study of patterns of interaction between HR and ICP and used as a proxy of brain and heart interaction. Full details of the algorithm can be found in [11]. In the present study we applied our own algorithm just described, to detect the total number of brain-heart crosstalks for adult patients with TBI.…”
Section: Brain-heart Crosstalks Detection and Analysismentioning
confidence: 99%
“…In the work by Gao et al [9], the authors presented an interesting analysis of interaction between brain and heart measures, showing the presence of interaction and Granger causality between ICP, mean arterial pressure and heart rate (HR). In our previous studies, we derived an HR-ICP measure that we denominated "brain-heart crosstalks, " defined as transient elevations of ICP and HR that occur simultaneously [11]. In that work, we presented multiple novelties.…”
Background
Traumatic brain injury (TBI) is an extremely heterogeneous and complex pathology that requires the integration of different physiological measurements for the optimal understanding and clinical management of patients. Information derived from intracranial pressure (ICP) monitoring can be coupled with information obtained from heart rate (HR) monitoring to assess the interplay between brain and heart. The goal of our study is to investigate events of simultaneous increases in HR and ICP and their relationship with patient mortality..
Methods
In our previous work, we introduced a novel measure of brain–heart interaction termed brain–heart crosstalks (ctnp), as well as two additional brain–heart crosstalks indicators [mutual information ($$mi_{ct}$$
m
i
ct
) and average edge overlap (ωct)] obtained through a complex network modeling of the brain–heart system. These measures are based on identification of simultaneous increase of HR and ICP. In this article, we investigated the relationship of these novel indicators with respect to mortality in a multicenter TBI cohort, as part of the Collaborative European Neurotrauma Effectiveness Research in TBI high-resolution work package.
Results
A total of 226 patients with TBI were included in this cohort. The data set included monitored parameters (ICP and HR), as well as laboratory, demographics, and clinical information. The number of detected brain–heart crosstalks varied (mean 58, standard deviation 57). The Kruskal–Wallis test comparing brain–heart crosstalks measures of survivors and nonsurvivors showed statistically significant differences between the two distributions (p values: 0.02 for $$mi_{ct}$$
m
i
ct
, 0.005 for ctnp and 0.006 for ωct). An inverse correlation was found, computed using the point biserial correlation technique, between the three new measures and mortality: − 0.13 for ctnp (p value 0.04), − 0.19 for ωct (p value 0.002969) and − 0.09 for $$mi_{ct}$$
m
i
ct
(p value 0.1396). The measures were then introduced into the logistic regression framework, along with a set of input predictors made of clinical, demographic, computed tomography (CT), and lab variables. The prediction models were obtained by dividing the original cohort into four age groups (16–29, 30–49, 50–65, and 65–85 years of age) to properly treat with the age confounding factor. The best performing models were for age groups 16–29, 50–65, and 65–85, with the deviance of ratio explaining more than 80% in all the three cases. The presence of an inverse relationship between brain–heart crosstalks and mortality was also confirmed.
Conclusions
The presence of a negative relationship between mortality and brain–heart crosstalks indicators suggests that a healthy brain–cardiovascular interaction plays a role in TBI.
eurocritical care (NCC) is a specialty drawing professionals with background training in neurology, neurosurgery, anesthesiology, internal medicine, and emergency medicine. Since the 1980s, NCC has grown into an organized subspecialty with establishment of neurointensive care units (neuro-ICUs) and professional organizations. The most prominent dedicated society, the Neurocritical Care Society (NCS), founded in 2002, counts more than 2500 members, with neurologists comprising the largest primary specialty (Rakela Miller, NCS, written communication, June 5, 2018). Neurocritical care education varies among different countries. In the United States, neurocritical care subspecialization can be pursued through fellowship training after completion of residency training in neurology, neurosurgery, anesthesiology, internal medicine, or emergency medicine, or as additional training after completing a critical care fellowship program. Fellowships are accredited by 2 organizations: United Council for Neurologic Subspecialties (UCNS) and, for neurosurgeons, Committee on Advanced Subspecialty Training. Currently, there are 68 UCNSaccredited neurocritical care programs and 22 Committee on Advanced Subspecialty Training programs; the numbers are growing. Among 1374 UCNS-certified diplomats in neurocritical care, 576 (41.9%) are board-certified neurologists (Becky Swanson, UCNS, written communication, April 17, 2018). Following efforts to integrate neurocritical care with other subspecialties, neurocritical care has recently been approved as a subspecialty by the American Board of Medical Specialties. It is yet to be seen how this approval will affect organization and education.In other countries, the proportion of neurologists within the pool of neurointensivists varies, but independently, the addition of neurocritical care has opened a new career path to neurologists IMPORTANCE Neurocritical care has grown into an organized specialty that may have consequences for patient care, outcomes, research, and neurointensive care (neuroICU) technology.OBSERVATIONS Neurocritical care improves care and outcomes of the patients who are neurocritically ill, and neuroICUs positively affect the financial state of health care systems. The development of neurocritical care as a recognized subspecialty has fostered multidisciplinary research, neuromonitoring, and neurocritical care information technology, with advances and innovations in practice and progress.CONCLUSIONS AND RELEVANCE Neurocritical care has become an important part of health systems and an established subspecialty of neurology. Understanding its structure, scope of practice, consequences for care, and research are important.
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