Cerebrovascular signal complexity six hours after ICU admission correlates with outcome following severe traumatic brain injury

Disease states are associated with a breakdown in healthy interactions and are often characterised by reduced signal complexity. We applied approximate entropy (ApEn) analysis to investigate the correlation between the complexity of heart rate (ApEn-HR), mean arterial pressure (ApEn-MAP), intracranial pressure (ApEn-ICP) and a combined ApEn-Product (product of the three individual ApEns) and outcome after traumatic brain injury. In 174 severe traumatic brain injured patients we found significant differences across groups classified by the Glasgow Outcome Score in ApEn-HR (p = 0.007), ApEn-MAP (p = 0.02), ApEn-ICP (p = 0.01), ApEn-Product (p = 0.001) and PRx (p = 0.004) in the first 6-hours. This relationship strengthened in a 24-hour and 72-hour analysis (ApEn-MAP continued to correlate with death but was not correlated with favourable outcome). Outcome was dichotomized as survival vs death, and favourable vs unfavourable; the ApEn-Product achieved the strongest statistical significance at 6-hours (F = 11.0; p = 0.001 and F = 10.5; p = 0.001, respectively) and was a significant independent predictor of mortality and favourable outcome (p < 0.001). Patients in the lowest quartile for ApEn-Product were over four times more likely to die (39 .5% vs 9.3%, p < 0.001) compared to those with the highest quartile. ApEn-ICP was inversely correlated with PRx (r = -0.39, p < 0.000001) indicating unique information related to impaired cerebral autoregulation. Our results demonstrate that as early as 6-hours into monitoring, complexity measures from easily attainable vital signs, such as heart rate and mean arterial pressure, in addition to intracranial pressure can help triage those who require more intensive neurological management at an early stage.This is the author accepted manuscript. The final version is available from Mary Ann Liebert via http://dx.doi.org/10.1089/neu.2015.422

Cerebrovascular Signal Complexity Six Hours after Intensive Care Unit Admission Correlates with Outcome after Severe Traumatic Brain Injury.

Disease states are associated with a breakdown in healthy interactions and are often characterized by reduced signal complexity. We applied approximate entropy (ApEn) analysis to investigate the correlation between the complexity of heart rate (ApEn-HR), mean arterial pressure (ApEn-MAP), intracranial pressure (ApEn-ICP), and a combined ApEn-product (product of the three individual ApEns) and outcome after traumatic brain injury. In 174 severe traumatic brain injured patients, we found significant differences across groups classified by the Glasgow Outcome Score in ApEn-HR (p = 0.007), ApEn-MAP (p = 0.02), ApEn-ICP (p = 0.01), ApEn-product (p = 0.001), and pressure reactivity index (PRx) (p = 0.004) in the first 6 h. This relationship strengthened in a 24 h and 72 h analysis (ApEn-MAP continued to correlate with death but was not correlated with favorable outcome). Outcome was dichotomized as survival versus death, and favorable versus unfavorable; the ApEn-product achieved the strongest statistical significance at 6 h (F = 11.0; p = 0.001 and F = 10.5; p = 0.001, respectively) and was a significant independent predictor of mortality and favorable outcome (p < 0.001). Patients in the lowest quartile for ApEn-product were over four times more likely to die (39.5% vs. 9.3%, p < 0.001) than those in the highest quartile. ApEn-ICP was inversely correlated with PRx (r = -0.39, p < 0.000001) indicating unique information related to impaired cerebral autoregulation. Our results demonstrate that as early as 6 h into monitoring, complexity measures from easily attainable vital signs, such as HR and MAP, in addition to ICP, can help triage those who require more intensive neurological management at an early stage.This is the author accepted manuscript. The final version is available from Mary Ann Liebert via http://dx.doi.org/10.1089/neu.2015.422

Signal Information Prediction of Mortality Identifies Unique Patient Subsets after Severe Traumatic Brain Injury: A Decision-Tree Analysis Approach.

Nonlinear physiological signal features that reveal information content and causal flow have recently been shown to be predictors of mortality after severe traumatic brain injury (TBI). The extent to which these features interact together, and with traditional measures to describe patients in a clinically meaningful way remains unclear. In this study, we incorporated basic demographics (age and initial Glasgow Coma Scale [GCS]) with linear and non-linear signal information based features (approximate entropy [ApEn], and multivariate conditional Granger causality [GC]) to evaluate their relative contributions to mortality using cardio-cerebral monitoring data from 171 severe TBI patients admitted to a single neurocritical care center over a 10 year period. Beyond linear modelling, we employed a decision tree analysis approach to define a predictive hierarchy of features. We found ApEn (p = 0.009) and GC (p = 0.004) based features to be independent predictors of mortality at a time when mean intracranial pressure (ICP) was not. Our combined model with both signal information-based features performed the strongest (area under curve = 0.86 vs. 0.77 for linear features only). Although low "intracranial" complexity (ApEn-ICP) outranked both age and GCS as crucial drivers of mortality (fivefold increase in mortality where ApEn-ICP 60 years of age died, whereas those with higher ApEn-ICP and GCS ≥5 all survived. Yet, even with low initial intracranial complexity, as long as patients maintained robust GC and "extracranial" complexity (ApEn of mean arterial pressure), they all survived. Incorporating traditional linear and novel, non-linear signal information features, particularly in a framework such as decision trees, may provide better insight into "health" status. However, caution is required when interpreting these results in a clinical setting prior to external validation

Ventricular Volume Load Reveals the Mechanoelastic Impact of Communicating Hydrocephalus on Dynamic Cerebral Autoregulation.

Several studies have shown that the progression of communicating hydrocephalus is associated with diminished cerebral perfusion and microangiopathy. If communicating hydrocephalus similarly alters the cerebrospinal fluid circulation and cerebral blood flow, both may be related to intracranial mechanoelastic properties as, for instance, the volume pressure compliance. Twenty-three shunted patients with communicating hydrocephalus underwent intraventricular constant-flow infusion with Hartmann's solution. The monitoring included transcranial Doppler (TCD) flow velocities (FV) in the middle (MCA) and posterior cerebral arteries (PCA), intracranial pressure (ICP), and systemic arterial blood pressure (ABP). The analysis covered cerebral perfusion pressure (CPP), the index of pressure-volume compensatory reserve (RAP), and phase shift angles between Mayer waves (3 to 9 cpm) in ABP and MCA-FV or PCA-FV. Due to intraventricular infusion, the pressure-volume reserve was exhausted (RAP) 0.84+/-0.1 and ICP was increased from baseline 11.5+/-5.6 to plateau levels of 20.7+/-6.4 mmHg. The ratio dRAP/dICP distinguished patients with large 0.1+/-0.01, medium 0.05+/-0.02, and small 0.02+/-0.01 intracranial volume compliances. Both M wave phase shift angles (r = 0.64; p<0.01) and CPP (r = 0.36; p<0.05) displayed a gradual decline with decreasing dRAP/dICP gradients. This study showed that in communicating hydrocephalus, CPP and dynamic cerebral autoregulation in particular, depend on the volume-pressure compliance. The results suggested that the alteration of mechanoelastic characteristics contributes to a reduced cerebral perfusion and a loss of autonomy of cerebral blood flow regulation. Results warrant a prospective TCD follow-up to verify whether the alteration of dynamic cerebral autoregulation may indicate a progression of communicating hydrocephalus.Alexander-von-Humboldt foundation, Cambridge Enterprise Ltd.This is the final version of the article. It first appeared from the Public Library of Science via http://dx.doi.org/10.1371/journal.pone.015850

Near-Infrared Spectroscopy can Monitor Dynamic Cerebral Autoregulation in Adults

Objective: To study the correlation between a dynamic index of cerebral autoregulation assessed with blood flow velocity (FV) using transcranial Doppler, and a tissue oxygenation index (TOI) recorded with near-infrared spectroscopy (NIRS). Methods: Twenty-three patients with sepsis, severe sepsis, or septic shock were monitored daily on up to four consecutive days. FV, TOI, and mean arterial blood pressure (ABP) were recorded for 60min every day. An index of autoregulation (Mx) was calculated as the moving correlation coefficient between 10-s averaged values of FV and ABP over moving 5min time-windows. The index Tox was evaluated as the correlation coefficient between TOI and ABP in the same way. The indices Mx and Tox, ABP and arterial partial pressure of CO2 were averaged for each patient. Results: Synchronized slow waves, presenting with periods from 20s to 2min, were seen in the TOI and FV of most patients, with a reasonable coherence between the signals in this bandwidth (coherence >0.5). The indices, Mx and Tox, demonstrated good correlation with each other (R=0.81; P<0.0001) in the whole group of patients. Both indices showed a significant (P<0.05) tendency to indicate weaker autoregulation in the state of vasodilatation associated with greater values of arterial partial pressure of CO2 or lower values of ABP. Conclusion: NIRS shows promise for the continuous assessment of cerebral autoregulation in adult

Feasibility of Hidden Markov Models for the Description of Time-Varying Physiologic State After Severe Traumatic Brain Injury.

OBJECTIVES: Continuous assessment of physiology after traumatic brain injury is essential to prevent secondary brain insults. The present work aims at the development of a method for detecting physiologic states associated with the outcome from time-series physiologic measurements using a hidden Markov model. DESIGN: Unsupervised clustering of hourly values of intracranial pressure/cerebral perfusion pressure, the compensatory reserve index, and autoregulation status was attempted using a hidden Markov model. A ternary state variable was learned to classify the patient's physiologic state at any point in time into three categories ("good," "intermediate," or "poor") and determined the physiologic parameters associated with each state. SETTING: The proposed hidden Markov model was trained and applied on a large dataset (28,939 hr of data) using a stratified 20-fold cross-validation. PATIENTS: The data were collected from 379 traumatic brain injury patients admitted to Addenbrooke's Hospital, Cambridge between 2002 and 2016. INTERVENTIONS: Retrospective observational analysis. MEASUREMENTS AND MAIN RESULTS: Unsupervised training of the hidden Markov model yielded states characterized by intracranial pressure, cerebral perfusion pressure, compensatory reserve index, and autoregulation status that were physiologically plausible. The resulting classifier retained a dose-dependent prognostic ability. Dynamic analysis suggested that the hidden Markov model was stable over short periods of time consistent with typical timescales for traumatic brain injury pathogenesis. CONCLUSIONS: To our knowledge, this is the first application of unsupervised learning to multidimensional time-series traumatic brain injury physiology. We demonstrated that clustering using a hidden Markov model can reduce a complex set of physiologic variables to a simple sequence of clinically plausible time-sensitive physiologic states while retaining prognostic information in a dose-dependent manner. Such states may provide a more natural and parsimonious basis for triggering intervention decisions

Critical thresholds for intracranial pressure vary over time in non-craniectomised traumatic brain injury patients.

BACKGROUND: Intracranial pressure (ICP)- and cerebral perfusion pressure (CPP)-guided therapy is central to neurocritical care for traumatic brain injury (TBI) patients. We sought to identify time-dependent critical thresholds for mortality and unfavourable outcome for ICP and CPP in non-craniectomised TBI patients. METHODS: This is a retrospective cohort study of 355 patients with moderate-to-severe TBI who received ICP monitoring and were managed without decompressive craniectomy in a tertiary hospital neurocritical care unit. Patients were grouped in 2 × 2 tables according to survival/death or favourable/unfavourable outcomes at 6 months and serial thresholds of mean ICP and CPP, using increments of 0.1 and 0.5 mmHg respectively. Sequential chi-square analysis was performed, and the thresholds yielding the highest chi-square test statistic were taken as having the best discriminative value for outcome. This process was repeated over monitoring periods of 1, 3, 5 and 7 days and for each day of recording to establish time-dependent thresholds. The same analysis was performed for age and sex subgroups. RESULTS: Global ICP thresholds were 21.3 and 20.5 mmHg for mortality and unfavourable outcome respectively (p < 0.001). After the first day of ICP monitoring, ICP thresholds fell to between 15 and 20 mmHg and remained significant (p < 0.05). Significant time-dependent CPP thresholds for mortality or unfavourable outcome were often not identified, and no identifiable trends were produced. CONCLUSION: Critical ICP thresholds in non-craniectomised TBI patients vary with time and fall below established ICP targets after the first day of monitoring

Can interhemispheric desynchronization of cerebral blood flow anticipate upcoming vasospasm in aneurysmal subarachnoid haemorrhage patients?

BACKGROUND: Asymmetry of cerebral autoregulation (CA) was demonstrated in patients after aneurysmal subarachnoid haemorrhage (aSAH). A classical method for CA assessment requires simultaneous measurement of both arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). In this study, we have proposed a cerebral blood flow asymmetry index based only on CBFV and analysed its association with the occurrence of vasospasm after aSAH. NEW METHOD: The phase shifts (PS) between slow oscillations in left and right CBFV (side-to-side PS) and between ABP and CBFV (CBFV-ABP PS) were estimated using multichannel matching pursuit (MMP) and cross-spectral analysis. RESULTS: We retrospectively analysed data collected from 45 aSAH patients (26 with vasospasm). Data were analysed up to 7th day after aSAH unless the vasospasm was detected earlier. A progressive asymmetry, manifested by a gradual increase in side-to-side PS on consecutive days after aSAH, was observed in patients who developed vasospasm (Radj2 = 0.14, p = 0.009). In these patients, early side-to-side PS was more positive than in patients without vasospasm (2.8° ± 5.6° vs -1.7° ± 5.7°, p = 0.011). No such a difference was found in CBFV-ABP PS. Patients with positive side-to-side PS were more likely to develop vasospasm than patients with negative side-to-side PS (21/7 vs 5/12, p = 0.0047). COMPARISON WITH EXISTING METHOD: MMP, in contrast to the spectral approach, accounts for non-stationarity of analysed signals. MMP applied to the PS estimation reflects the cerebral blood flow asymmetry in aSAH better than the spectral analysis. CONCLUSIONS: Changes in side-to-side PS might be helpful to identify patients who are at risk of vasospasm

Non-Invasive Pressure Reactivity Index Using Doppler Systolic Flow Parameters: A Pilot Analysis.

The goal was to predict pressure reactivity index (PRx) using non-invasive transcranial Doppler (TCD) based indices of cerebrovascular reactivity, systolic flow index (Sx_a), and mean flow index (Mx_a). Continuous extended duration time series recordings of middle cerebral artery cerebral blood flow velocity (CBFV) were obtained using robotic TCD in parallel with direct intracranial pressure (ICP). PRx, Sx_a, and Mx_a were derived from high frequency archived signals. Using time-series techniques, autoregressive integrative moving average (ARIMA) structure of PRx was determined and embedded in the following linear mixed effects (LME) models of PRx: PRx ∼ Sx_a and PRx ∼ Sx_a + Mx_a. Using 80% of the recorded patient data, the LME models were created and trained. Model superiority was assessed via Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood (LL). The superior two models were then used to predict PRx using the remaining 20% of the signal data. Predicted and observed PRx were compared via Pearson correlation, linear models, and Bland-Altman (BA) analysis. Ten patients had 3-4 h of continuous uninterrupted ICP and TCD data and were used for this pilot analysis. Optimal ARIMA structure for PRx was determined to be (2,0,2), and this was embedded in all LME models. The top two LME models of PRx were determined to be: PRx ∼ Sx_a and PRx ∼ Sx_a + Mx_a. Estimated and observed PRx values from both models were strongly correlated (r > 0.9; p < 0.0001 for both), with acceptable agreement on BA analysis. Predicted PRx using these two models was also moderately correlated with observed PRx, with acceptable agreement (r = 0.797, p = 0.006; r = 0.763, p = 0.011; respectively). With application of ARIMA and LME modeling, it is possible to predict PRx using non-invasive TCD measures. These are the first and as well as being preliminary attempts at doing so. Much further work is required