Point Shear Wave Elastography by ElastPQ for Fibrosis Screening in Patients with NAFLD: A Prospective, Multicenter Comparison to Vibration-Controlled Elastography.

BACKGROUND: Since nonalcoholic fatty liver disease (NAFLD) has become the leading cause of liver disease in the Western world, clinicians need reliable noninvasive tools for the identification of NAFLD-associated fibrosis. Limited evidence on the performance of the novel shear wave elastography technique Elast-PQ (EPQ) in NAFLD is available. METHOD: In this prospective, European multinational study we assessed the diagnostic accuracy of EPQ using vibration-controlled transient elastography (VCTE) as a reference standard. RESULTS: Among 353 NAFLD patients, 332 (94.1%) fulfilled reliability criteria of VCTE and EPQ (defined by IQR/median ≤0.3; 41.3% female, mean age: 59 [IQR: 16.5], mean BMI: 29.0 (7.1)). 4/353 (1.1%) and 17/353 (4.8%) had unreliable VCTE and EPQ measurements, respectively. VCTE-based NAFLD fibrosis stages were F0/F1: 222(66.9%), F2: 41 (12.3%), F3: 30 (9.1%), F4: 39 (11.7%). We found a strong correlation (Pearson R=0.87; p<0.0001) and concordance (Lin's concordance correlation coefficient =0.792) of EPQ with VCTE. EPQ was able to identify NAFLD-fibrosis risk with the following EPQ cutoffs: ≥6.5 kPa for significant fibrosis (≥F2) (≥1.47 m/s; sensitivity: 78%; specificity: 95%; AUROC: 0.94), ≥6.9 kPa for advanced fibrosis (≥F3) (≥1.52 m/s; sens.: 88%, spec.: 89%; AUROC: 0.949), and ≥10.4 kPa for cirrhosis (F4) (≥1.86 m/s; sens.: 87%; spec.: 94%; AUROC: 0.949). CONCLUSION: The point shear wave elastography technique EPQ shows excellent correlation to and concordance with VCTE. EPQ can reliably exclude NAFLD fibrosis <6.0 kPa (<1.41 m/s) and indicate a high risk of advanced fibrosis ≥10.4 kPa (≥1.86 m/s).

Magnitude dependent discordance in liver stiffness measurements using elastography point quantification with transient elastography as the reference test.

OBJECTIVES: To investigate diagnostic performance of point shear wave elastography by elastography point quantification (ElastPQ) for non-invasive assessment of liver fibrosis in patients with chronic liver diseases (CLD). METHODS: Liver stiffness measurement (LSM) by transient elastography (TE) and ElastPQ was performed in patients with CLD and healthy volunteers. The stage of liver fibrosis was defined by TE which served as the reference. We compared two methods by using correlation, area under the receiver operating characteristics curve (AUC) analysis, Bland and Altman plot and Passing-Bablok regression. RESULTS: A total of 185 subjects (20 healthy volunteers and 165 patients with CLD (128 non-alcoholic fatty liver disease), 83 (44.9%) females, median age 53 years, BMI 27.3 kg/m2) were evaluated. There were 24.3%, 13.5% and 11.4% patients in ≥ F2, ≥ F3 and F4 stage, respectively. The best performing cutoff LSM values by ElastPQ were 5.5 kPa for F ≥ 2 (AUC = 0.96), 8.1 kPa for F ≥ 3 (AUC = 0.98) and 9.9 kPa for F4 (AUC = 0.98). Mean (SD) difference between TE and ElastPQ measurements was 0.98 (3.27) kPa (95% CI 0.51-1.45, range 4.99-21.60 kPa). Two methods correlated significantly (r = 0.86; p < 0.001), yet Bland and Altman plot demonstrated difference between measurements, especially with TE values > 10 kPa. Passing and Bablok regression analysis yielded significant constant and proportional difference between ElastPQ and TE. CONCLUSION: ElastPQ is reliable method for assessment of liver fibrosis but LSM values are not interchangeable with TE, especially above 10 kPa. Diagnostic performance of ElastPQ for sub-classification of patients with compensated advanced chronic liver disease should therefore be furtherly investigated. KEY POINTS: • ElastPQ appears to be reliable method for assessment of liver fibrosis, with data presented here mostly applicable to NAFLD. • LSM values produced by TE and ElastPQ are NOT interchangeable-in values < 10 kPa, they are similar, but in values > 10 kPa, they appear to be increasingly and significantly different. • Diagnostic performance of ElastPQ for sub-classification of patients with compensated advanced chronic liver disease should be furtherly investigated.

Heart rate variability in newborns with hypoxic brain injury.

In neonatal intensive care units, there is a need for continuous monitoring of sick newborns with perinatal hypoxic ischemic brain injury (HIE). We assessed the utility of heart rate variability (HRV) in newborns with acute HIE undergoing simultaneous continuous EEG (cEEG) and ECG monitoring. HIE was classified using clinical criteria as well as visual grading of cEEG. Newborns were divided into two groups depending on the severity of the hypoxic injury and outcome. Various HRV parameters were compared between these groups, and significantly decreased HRV was found in neonates with severe HIE. As HRV is affected by many factors, it is difficult to attribute this difference solely to HIE. However, this study suggests that further investigation of HRV as a monitoring tool for acute neonatal hypoxic injury is warranted.

Automated EEG background analysis to identify neonates with hypoxic-ischemic encephalopathy treated with hypothermia at risk for adverse outcome: A pilot study.

BACKGROUND: To improve the objective assessment of continuous video-EEG (cEEG) monitoring of neonatal brain function, the aim was to relate automated derived amplitude and duration parameters of the suppressed periods in the EEG background (dynamic Interburst Interval= dIBIs) after neonatal hypoxic-ischemic encephalopathy (HIE) to favourable or adverse neurodevelopmental outcome. METHODS: Nineteen neonates (gestational age 36-41 weeks) with HIE underwent therapeutic hypothermia and had cEEG-monitoring. EEGs were retrospectively analyzed with a previously developed algorithm to detect the dynamic Interburst Intervals. Median duration and amplitude of the dIBIs were calculated at 1 h-intervals. Sensitivity and specificity of automated EEG background grading for favorable and adverse outcomes were assessed at 6 h-intervals. RESULTS: Dynamic IBI values reached the best prognostic value between 18 and 24 h (AUC of 0.93). EEGs with dIBI amplitude ≥15 µV and duration <10 s had a specificity of 100% at 6-12 h for favorable outcome but decreased subsequently to 67% at 25-42 h. Suppressed EEGs with dIBI amplitude <15 µV and duration >10 s were specific for adverse outcome (89-100%) at 18-24 h (n = 10). Extremely low voltage and invariant EEG patterns were indicative of adverse outcome at all time points. CONCLUSIONS: Automated analysis of the suppressed periods in EEG of neonates with HIE undergoing TH provides objective and early prognostic information. This objective tool can be used in a multimodal strategy for outcome assessment. Implementation of this method can facilitate clinical practice, improve risk stratification and aid therapeutic decision-making. A multicenter trial with a quantifiable outcome measure is warranted to confirm the predictive value of this method in a more heterogeneous dataset.

Ultrasound Grade of Liver Steatosis Is Independently Associated with the Risk of Metabolic Syndrome.

The aim of the study was to explore (a) prevalence and grade of nonalcoholic fatty liver (NAFL) among outpatients referred for abdominal ultrasound (US) examination and (b) relationship between the presence and severity of liver steatosis and metabolic syndrome (MS). This was a retrospective analysis of patients without history of liver disease examined by abdominal US in the University hospital setting. US was used to detect and semiquantitatively grade (0-3) liver steatosis. Data on patients' age, gender, body mass index (BMI), impaired glucose metabolism (IGM), atherogenic dyslipidaemia (AD), raised blood pressure (RBP), transaminases, and platelet counts were obtained from medical records. MS was defined as having at least 3 of the following components: obesity, IGM, AD, and RBP. Of the 631 patients (median age 60 years, median BMI 27.4 kg/m2, and 57.4% females) 71.5% were overweight and 48.5% had NAFL. In the subgroup of 159 patients with available data on the components of MS, patients with higher US grade of steatosis had significantly higher BMI and increased prevalence of obesity, IGM, AD, RBP, and accordingly more frequently had MS, whereas they did not differ in terms of age and gender. NAFL was independently associated with the risk of having MS in a multivariate model adjusted for age, gender, BMI, and IGM. The grade of liver steatosis did not correlate with the presence of liver fibrosis. We demonstrated worrisome prevalence of obesity and NAFL in the outpatient population from our geographic region. NAFL is independently associated with the risk of having MS implying worse prognosis.

Non-invasive diagnosis of portal hypertension in cirrhosis using ultrasound based elastography.

Liver stiffness measurement (LSM) by ultrasound-based elastography may be used to non-invasively discriminate between the stages of liver fibrosis, rule out cirrhosis and follow its evolution, including the prediction of the presence of oesophageal varices. The same is possible in order to diagnose clinically significant portal hypertension, referring primarilyto transient elastography and LSM values ≥20-25 kPa. The same approach may be used to reliably rule out the presence ofoesophageal varices (LSM <20 kPa + platelets >150x109/L). These recommendations refer primarily to patients with viral aetiology of chronic liver disease (hepatitis C), while additional studies are required for other aetiologies. While spleen stiffness measurement (SSM) also poses a logical choice in this indication, controversial results have nevertheless been published on this issue. It should be emphasized, however, that more recent data indicate that this parameter should be included in the diagnostic algorithm for portal hypertension, if not as the sole then as a part of a sequential algorithm, combined with LSM. Until now, transient elastography has been most extensively studied and founded on scientific evidence, although the results of other ultrasound-based elastography techniques demonstrate the same trend for the non-invasive assessment of portal hypertension.

Interrater agreement in visual scoring of neonatal seizures based on majority voting on a web-based system: The Neoguard EEG database.

OBJECTIVE: To assess interrater agreement based on majority voting in visual scoring of neonatal seizures. METHODS: An online platform was designed based on a multicentre seizure EEG-database. Consensus decision based on 'majority voting' and interrater agreement was estimated using Fleiss' Kappa. The influences of different factors on agreement were determined. RESULTS: 1919 Events extracted from 280h EEG of 71 neonates were reviewed by 4 raters. Majority voting was applied to assign a seizure/non-seizure classification. 44% of events were classified with high, 36% with moderate, and 20% with poor agreement, resulting in a Kappa value of 0.39. 68% of events were labelled as seizures, and in 46%, all raters were convinced about electrographic seizures. The most common seizure duration was <30s. Raters agreed best for seizures lasting 60-120s. There was a significant difference in electrographic characteristics of seizures versus dubious events, with seizures having longer duration, higher power and amplitude. CONCLUSIONS: There is a wide variability in identifying rhythmic ictal and non-ictal EEG events, and only the most robust ictal patterns are consistently agreed upon. Database composition and electrographic characteristics are important factors that influence interrater agreement. SIGNIFICANCE: The use of well-described databases and input of different experts will improve neonatal EEG interpretation and help to develop uniform seizure definitions, useful for evidence-based studies of seizure recognition and management.

Improving Reliability of Monitoring Background EEG Dynamics in Asphyxiated Infants.

The goal of this study is to develop an automated algorithm to quantify background electroencephalography (EEG) dynamics in term neonates with hypoxic ischemic encephalopathy. The recorded EEG signal is adaptively segmented and the segments with low amplitudes are detected. Next, depending on the spatial distribution of the low-amplitude segments, the first part of the algorithm detects (dynamic) interburst intervals (dIBIs) and performs well on the relatively artifact-free EEG periods and well-defined burst-suppression EEG periods. However, on testing the algorithm on EEG recordings of more than 48 h per neonate, a significant number of misclassified and dubious detections were encountered. Therefore, as the next step, we applied machine learning classifiers to differentiate between definite dIBI detections and misclassified ones. The developed algorithm achieved a true positive detection rate of 98%, 97%, 88%, and 95% for four duration-related dIBI groups that we subsequently defined. We benchmarked our algorithm with an expert diagnostic interpretation of EEG periods (1 h long) and demonstrated its effectiveness in clinical practice. We show that the detection algorithm effectively discriminates challenging cases encountered within mild and moderate background abnormalities. The dIBI detection algorithm improves identification of neonates with good clinical outcome as compared to the classification based on the classical burst-suppression interburst interval.

Objective differentiation of neonatal EEG background grades using detrended fluctuation analysis.

A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our formal feasibility study suggested that DFA exponent can be reliably assessed in only part of the EEG epochs, and in only relatively short time scales (10-60 s), while it becomes ambiguous if longer time scales are considered. This prompted further exploration whether paradigm used for quantifying multifractal DFA (MF-DFA) could be applied in a more efficient way, and whether metrics from MF-DFA paradigm could yield useful benchmark with existing clinical EEG gradings. Comparison of MF-DFA metrics showed a significant difference between three visually assessed background EEG grades. MF-DFA parameters were also significantly correlated to interburst intervals quantified with our previously developed automated detector. Finally, we piloted a monitoring application of MF-DFA metrics and showed their evolution during patient recovery from asphyxia. Our exploratory study showed that neonatal EEG can be quantified using multifractal metrics, which might offer a suitable parameter to quantify the grade of EEG background, or to monitor changes in brain state that take place during long-term brain monitoring.

Data-driven metric representing the maturation of preterm EEG.

Essential information about early brain maturation can be retrieved from the preterm human electroencephalogram (EEG). This study proposes a new set of quantitative features that correlate with early maturation. We exploit the known early trend in EEG content from intermittent to continuous activity, which changes the line length content of the EEG. The developmental shift can be captured in the line length histogram, which we use to obtain 28 features; 20 histogram bins and 8 other statistical measurements. Using the mutual information, we select 6 features with high correlation to the infant's age. This subset appears promising to detect deviances from normal brain maturation. The presented data-driven index holds promise for developing into a computational EEG index of maturation that is highly needed for overall assessment in the Neonatal Intensive Care Units.

Line length as a robust method to detect high-activity events: automated burst detection in premature EEG recordings.

OBJECTIVE: EEG is a valuable tool for evaluation of brain maturation in preterm babies. Preterm EEG constitutes of high voltage burst activities and more suppressed episodes, called interburst intervals (IBIs). Evolution of background characteristics provides information on brain maturation and helps in prediction of neurological outcome. The aim is to develop a method for automated burst detection. METHODS: Thirteen polysomnography recordings were used, collected at preterm postmenstrual age of 31.4 (26.1-34.4)weeks. We developed a burst detection algorithm based on the feature line length and compared it with manual scorings of clinical experts and other published methods. RESULTS: The line length-based algorithm is robust (84.27% accuracy, 84.00% sensitivity, 85.70% specificity). It is not critically dependent on the number of measurement channels, because two channels still provide 82% accuracy. Furthermore, it approximates well clinically relevant features, such as median IBI duration 5.45 (4.00-7.11)s, maximum IBI duration 14.02 (8.73-18.80)s and burst percentage 48.89 (35.45-60.12)%, with a median deviation of respectively 0.65s, 1.96s and 6.55%. CONCLUSION: Automated assessment of long-term preterm EEG is possible and its use will optimize EEG interpretation in the NICU. SIGNIFICANCE: This study takes a first step towards fully automatic analysis of the preterm brain.

Holistic approach for automated background EEG assessment in asphyxiated full-term infants.

OBJECTIVE: To develop an automated algorithm to quantify background EEG abnormalities in full-term neonates with hypoxic ischemic encephalopathy. APPROACH: The algorithm classifies 1 h of continuous neonatal EEG (cEEG) into a mild, moderate or severe background abnormality grade. These classes are well established in the literature and a clinical neurophysiologist labeled 272 1 h cEEG epochs selected from 34 neonates. The algorithm is based on adaptive EEG segmentation and mapping of the segments into the so-called segments' feature space. Three features are suggested and further processing is obtained using a discretized three-dimensional distribution of the segments' features represented as a 3-way data tensor. Further classification has been achieved using recently developed tensor decomposition/classification methods that reduce the size of the model and extract a significant and discriminative set of features. MAIN RESULTS: Effective parameterization of cEEG data has been achieved resulting in high classification accuracy (89%) to grade background EEG abnormalities. SIGNIFICANCE: For the first time, the algorithm for the background EEG assessment has been validated on an extensive dataset which contained major artifacts and epileptic seizures. The demonstrated high robustness, while processing real-case EEGs, suggests that the algorithm can be used as an assistive tool to monitor the severity of hypoxic insults in newborns.

Interhemispheric synchrony in the neonatal EEG revisited: activation synchrony index as a promising classifier.

A key feature of normal neonatal EEG at term age is interhemispheric synchrony (IHS), which refers to the temporal co-incidence of bursting across hemispheres during trace alternant EEG activity. The assessment of IHS in both clinical and scientific work relies on visual, qualitative EEG assessment without clearly quantifiable definitions. A quantitative measure, activation synchrony index (ASI), was recently shown to perform well as compared to visual assessments. The present study was set out to test whether IHS is stable enough for clinical use, and whether it could be an objective feature of EEG normality. We analyzed 31 neonatal EEG recordings that had been clinically classified as normal (n = 14) or abnormal (n = 17) using holistic, conventional visual criteria including amplitude, focal differences, qualitative synchrony, and focal abnormalities. We selected 20-min epochs of discontinuous background pattern. ASI values were computed separately for different channel pair combinations and window lengths to define them for the optimal ASI intraindividual stability. Finally, ROC curves were computed to find trade-offs related to compromised data lengths, a common challenge in neonatal EEG studies. Using the average of four consecutive 2.5-min epochs in the centro-occipital bipolar derivations gave ASI estimates that very accurately distinguished babies clinically classified as normal vs. abnormal. It was even possible to draw a cut-off limit (ASI~3.6) which correctly classified the EEGs in 97% of all cases. Finally, we showed that compromising the length of EEG segments from 20 to 5 min leads to increased variability in ASI-based classification. Our findings support the prior literature that IHS is an important feature of normal neonatal brain function. We show that ASI may provide diagnostic value even at individual level, which strongly supports its use in prospective clinical studies on neonatal EEG as well as in the feature set of upcoming EEG classifiers.

Multi-structural signal recovery for biomedical compressive sensing.

Compressive sensing has shown significant promise in biomedical fields. It reconstructs a signal from sub-Nyquist random linear measurements. Classical methods only exploit the sparsity in one domain. A lot of biomedical signals have additional structures, such as multi-sparsity in different domains, piecewise smoothness, low rank, etc. We propose a framework to exploit all the available structure information. A new convex programming problem is generated with multiple convex structure-inducing constraints and the linear measurement fitting constraint. With additional a priori information for solving the underdetermined system, the signal recovery performance can be improved. In numerical experiments, we compare the proposed method with classical methods. Both simulated data and real-life biomedical data are used. Results show that the newly proposed method achieves better reconstruction accuracy performance in term of both L1 and L2 errors.

Multi-sparse signal recovery for compressive sensing.

Signal recovery is one of the key techniques of compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains. To further improve the signal reconstruction performance, we can exploit this multi-sparsity to generate a new convex programming model. The latter is formulated with multiple sparsity constraints in multiple domains and the linear measurement fitting constraint. It improves signal recovery performance by additional a priori information. Since some EMG signals exhibit sparsity both in time and frequency domains, we take them as example in numerical experiments. Results show that the newly proposed method achieves better performance for multi-sparse signals.

Automated EEG inter-burst interval detection in neonates with mild to moderate postasphyxial encephalopathy.

EEG inter-burst interval (IBI) and its evolution is a robust parameter for grading hypoxic encephalopathy and prognostication in newborns with perinatal asphyxia. We present a reliable algorithm for the automatic detection of IBIs. This automated approach is based on adaptive segmentation of EEG, classification of segments and use of temporal profiles to describe the global distribution of EEG activity. A pediatric neurologist has blindly scored data from 8 newborns with perinatal postasphyxial encephalopathy varying from mild to severe. 15 minutes of EEG have been scored per patient, thus totaling 2 hours of EEG that was used for validation. The algorithm shows good detection accuracy and provides insight into challenging cases that are difficult to detect.

Independent component analysis as a preprocessing step for data compression of neonatal EEG.

We propose a novel approach for compressive sampling of the neonatal electro-encefalogram (EEG) data. The method assumes that the set of EEG data is generated by linearly mixing a fewer number of source signals. Another assumption is that the sources are nearly-sparse in Gabor dictionary. The presented method, instead of compressing original EEG channels, first performs a data-reduction, and then compresses the obtained sources. With this approach we showed that the gain in reconstruction speed is 33%-50%, whereas the compression rate is enhanced by 33%.