Literatura académica sobre el tema "Clinical EEG data"
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Artículos de revistas sobre el tema "Clinical EEG data"
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Járdánházy, T., I. Somogyi, and T. Asztalos. "Compression methods for EEG spectral data." Electroencephalography and Clinical Neurophysiology 87, no. 2 (1993): S133. http://dx.doi.org/10.1016/0013-4694(93)91489-n.
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Banquet, J. P., W. Guenther, and D. Breitling. "Multidimensional factorial methods for EEG data." Electroencephalography and Clinical Neurophysiology 61, no. 3 (1985): S231. http://dx.doi.org/10.1016/0013-4694(85)90874-0.
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Antony, Mary Judith, Baghavathi Priya Sankaralingam, Shakir Khan, Abrar Almjally, Nouf Abdullah Almujally, and Rakesh Kumar Mahendran. "Brain–Computer Interface: The HOL–SSA Decomposition and Two-Phase Classification on the HGD EEG Data." Diagnostics 13, no. 17 (2023): 2852. http://dx.doi.org/10.3390/diagnostics13172852.
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An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain–Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL–SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method’s ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.
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Gu, Yuqiao, Geir Halnes, Hans Liljenström, and Björn Wahlund. "A cortical network model for clinical EEG data analysis." Neurocomputing 58-60 (June 2004): 1187–96. http://dx.doi.org/10.1016/j.neucom.2004.01.184.
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Goldenholz, Daniel M., Joseph J. Tharayil, Rubin Kuzniecky, Philippa Karoly, William H. Theodore, and Mark J. Cook. "Simulating clinical trials with and without intracranial EEG data." Epilepsia Open 2, no. 2 (2017): 156–61. http://dx.doi.org/10.1002/epi4.12038.
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Ivanov, А. А. "Overview of mathematical EEG analysis. Quantitative EEG." Epilepsy and paroxysmal conditions 15, no. 2 (2023): 171–92. http://dx.doi.org/10.17749/2077-8333/epi.par.con.2023.154.
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The purpose of this article is to familiarize medical specialists involved in registration and analysis of electroencephalographic (EEG) studies using methods of mathematical processing and analysis for recorded EEG data. Understanding the principles of how quantitative EEG analysis tools work should help medical personnel to properly use their capabilities and ultimately improve quality of medical care. Here, we discuss basic and innovative mathematical tools for EEG processing and analysis.
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Salam, Abdus, Selina Husna Banu, Abu Nayeem, and Zobaida Sultana Susan. "Clinical Finding of Electroencephalographic (EEG) Data in Adults: A Retrospective study." Journal of Shaheed Suhrawardy Medical College 6, no. 1 (2017): 14–17. http://dx.doi.org/10.3329/jssmc.v6i1.31486.
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Background: Electroencephalography (EEG) is the first and only real-time monitor of epileptic seizures, and is a powerful measure of cerebral function in the seriously ill.Objective: The purpose of this study was to see the common conditions for doing EEG.Methods: This study was performed retrospectively by collecting and reviewing the electro-clinical information of the adult patients to whom EEGs were done at the 'Central Hospital' laboratory. Clinical problems were categorized into seizures, fainting attacks, headache, giddiness, vertigo, stroke, suicidal tendency, sudden aggressiveness and head injury. Routine EEGs were performed for 30 minutes. EEG findings were categorized as normal for the age, localized or generalized epileptiform discharges, non-epileptogenic dysfunction.Result: In total 53 adult patients 34% population had fainting attacks, 28% had seizures, 10% had stroke and 28% had complaint related to behavior, suicidal tendency, headache and post head injury problems. EEG was normal in 60%.Conclusion: EEG is advised for diverse conditions. The proportion of epileptic patients is small, although this is the principal indication for doing routine EEG.J Shaheed Suhrawardy Med Coll, June 2014, Vol.6(1); 14-17
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Noachtar, Soheyl, Jan Remi, and Elisabeth Kaufmann. "EEG-Update." Klinische Neurophysiologie 53, no. 04 (2022): 243–52. http://dx.doi.org/10.1055/a-1949-1691.
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Durch die rasante Entwicklung digitaler Computertechniken und neuer Analysemethoden hat sich ein neuer Ansatz zur Analyse der Hirnströme (quantitatives EEG) ergeben, die in verschiedenen klinischen Bereichen der Neurologie und Psychiatrie bereits Ergebnisse zeigen. Die neuen Möglichkeiten der Analyse des EEG durch Einsatz künstlicher Intelligenz (Deep Learning) und großer Datenmengen (Big Data) sowie telemedizinischer Datenübermittlung und Interaktion wird den Einsatz der Methode vermutlich in den nächsten Jahren erweitern.
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Cincotti, F., C. Babiloni, C. Miniussi, et al. "EEG Deblurring Techniques in a Clinical Context." Methods of Information in Medicine 43, no. 01 (2004): 114–17. http://dx.doi.org/10.1055/s-0038-1633846.
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Summary Objectives: EEG scalp potential distributions recorded in humans are affected by low spatial resolution and by the dependence on the electrical reference used. High resolution EEG technologies are available to drastically increase the spatial resolution of the raw EEG. Such technologies include the computation of surface Laplacian (SL) of the recorded potentials, as well as the use of realistic head models to estimate the cortical sources via linear inverse procedure (low resolution brain electromagnetic tomography, LORETA). However, these deblurring procedures are generally used in conjunction with EEG recordings with 64-128 scalp electrodes and with realistic head models obtained via sequential magnetic resonance images (MRIs) of the subjects. Such recording setup it is not often available in the clinical context, due to both the unavailability of these technologies and the scarce compliance of the patients with them. In this study we addressed the use of SL and LORETA deblurring techniques to analyze data from a standard 10-20 system (19 electrodes) in a group of Alzheimer disease (AD) patients. Methods: EEG data related to unilateral finger movements were gathered from 10 patients affected by AD. SL and LORETA techniques were applied for source estimation of EEG data. The use of MRIs for the construction of head models was avoided by using the quasi-realistic head model of the Brain Imaging Neurology Institute of Montreal. Results: A similar cortical activity estimated by the SL and LORETA techniques was observed during an identical time period of the acquired EEG data in the examined population. Conclusions: The results of the present study suggest that both SL and LORETA approaches can be usefully applied in the clinical context, by using quasi-realistic head modeling and a standard 10-20 system as electrode montage (19 electrodes). These results represent a reciprocal cross-validation of the two mathematically independent techniques in a clinical environment.
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Kutafina, Ekaterina, Alexander Brenner, Yannic Titgemeyer, Rainer Surges, and Stephan Jonas. "Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection." PeerJ 8 (May 1, 2020): e8969. http://dx.doi.org/10.7717/peerj.8969.
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Development of mobile sensors brings new opportunities to medical research. In particular, mobile electroencephalography (EEG) devices can be potentially used in low cost screening for epilepsy and other neurological and psychiatric disorders. The necessary condition for such applications is thoughtful validation in the specific medical context. As part of validation and quality assurance, we developed a computer-based analysis pipeline, which aims to compare the EEG signal acquired by a mobile EEG device to the one collected by a medically approved clinical-grade EEG device. Both signals are recorded simultaneously during 30 min long sessions in resting state. The data are collected from 22 patients with epileptiform abnormalities in EEG. In order to compare two multichannel EEG signals with differently placed references and electrodes, a novel data processing pipeline is proposed. It allows deriving matching pairs of time series which are suitable for similarity assessment through Pearson correlation. The average correlation of 0.64 is achieved on a test dataset, which can be considered a promising result, taking the positions shift due to the simultaneous electrode placement into account.
Tesis sobre el tema "Clinical EEG data"
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Abazid, Majd. "Topological study of the brain functional organization at the early stages of Alzheimer's disease using electroencephalography." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS026.
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L'électroencéphalographie (EEG) est encore considérée de nos jours comme une technique de neuroimagerie très utile dans les applications cliniques, adaptée aux patients souffrant de troubles cognitifs et physiques, ainsi qu'aux tests à grande échelle. L'EEG est une technologie non invasive, peu coûteuse et facilement accessible. Elle se caractérise par une haute résolution temporelle, ce qui est crucial pour le suivi de la dynamique cérébrale.Plusieurs travaux dans la littérature ont exploité l'EEG pour étudier les altérations de l'activité cérébrale liées aux maladies neurodégénératives, notamment la maladie d'Alzheimer (MA). La MA est une maladie neurodégénérative chronique qui entraîne un déclin progressif des fonctions cognitives, ainsi que des troubles du comportement et une perte insidieuse d'autonomie au quotidien. En l'absence de traitements curatifs, nous observons un intérêt croissant à la caractérisation de l'activité cérébrale aux stades précoces de la maladie. Le stade préclinique de la MA est asymptomatique, mais les lésions cérébrales dues à la MA sont présentes. A ce stade, on parle de troubles cognitifs subjectifs (subjective cognitive impairments, SCI). Au stade prodromal, les patients atteints de troubles cognitifs légers (mild cognitive impairment, MCI) présentent des troubles de la mémoire mesurables, mais leur capacité fonctionnelle est maintenue. Les patients atteints de troubles subjectifs ou légers présentent un risque élevé de développer la MA.Cette thèse s'intéresse au diagnostic précoce de la MA aux stades préclinique et prodromal en utilisant l'EEG au repos, et aborde l'analyse des réseaux cérébraux en étudiant la connectivité fonctionnelle à différents stades cliniques du déclin cognitif (SCI, MCI et MA au stade léger). Pour cela, nous avons mené une étude rétrospective en exploitant une base de données clinique qui contient des signaux EEG enregistrés en conditions réelles.En premier lieu, nous avons proposé d'exploiter une mesure d'entropie, appelée "Epoch-based Entropy" (EpEn), pour quantifier la connectivité fonctionnelle. Cette mesure repose sur une modélisation statistique fine des signaux EEG avec des modèles de Markov cachés. Cette mesure caractérise les changements spatio-temporels des signaux EEG en quantifiant le contenu d'information dans les signaux au niveau temporel et spatial.Par la suite, nous avons effectué une analyse topologique du réseau cérébral cortical de manière différentielle, en exploitant la théorie des graphes. La contribution de notre travail est double. En effet, il s'agit du premier travail qui : (i) aborde l'analyse du réseau cérébral chez les patients ayant des troubles subjectifs, des troubles légers et la MA au stade léger, et (ii) combine la mesure d'entropie à la théorie des graphes puisque nous avons démontré son efficacité à quantifier les changements spatio-temporels liés à la MA.Dans cette thèse, nous avons aussi abordé le problème de la grande quantité d'information extraite des signaux EEG, analysés sur plusieurs bandes de fréquences (delta, theta, alpha, beta), plusieurs électrodes, et plusieurs échelles de densité de réseau (seuillages multiples des graphes). Par conséquent, une autre contribution à ce travail de thèse concerne l'extraction de marqueurs EEG les plus pertinents pour discriminer automatiquement les trois groupes de patients. Ainsi, nous avons proposé une méthode hiérarchique pour l'analyse des signaux EEG, permettant d'identifier les descripteurs les plus pertinents à partir d'une grande quantité d'information issue d'une seule mesure de connectivité fonctionnelle. Enfin, nous avons évalué la corrélation entre les marqueurs numériques extraits des signaux EEG et les marqueurs cliniques à notre disposition (MMSE, RL/RI-16, BREF)<br>Electroencephalography (EEG) is still considered nowadays as a convenient neuroimaging technique in clinical applications, suitable for cognitively and physically disabled patients, as well as for serial tests. In fact, EEG is a non-invasive, cost-effective, and mobile technology. It is characterized by a high temporal resolution, which is crucial for the analysis of fast brain functional dynamics.There is a rich literature addressing the use of EEG to investigate brain activity alterations due to neurodegenerative diseases, especially Alzheimer's disease (AD). AD is a chronic neurodegenerative disease that leads to progressive decline of cognitive functions along with behavioral disorders and insidious loss of autonomy in daily living activities. We observe a growing interest in the earlier stages of the disease since curative treatments are still lacking. The preclinical stage of AD is asymptomatic, but the brain lesions due to AD are present. At this phase, the term of subjective cognitive impairment (SCI) has been recently defined. In the prodromal stage, mild cognitive impairment (MCI) patients show measurable memory impairments but their functional capacity is maintained. SCI and MCI patients are at high risk of developing AD.This thesis investigates the early diagnosis of AD at preclinical and prodromal stages using resting-state EEG, and addresses brain network analysis by studying the functional connectivity over several clinical stages of cognitive decline (SCI, MCI and Mild AD). To this end, we conduct a retrospective study using a clinical database that contains EEG signals recorded in real-life conditions.We first propose to exploit an entropy measure, termed “epoch-based entropy” (EpEn), as a measure of functional connectivity, that relies on a refined statistical modeling of EEG signals based on Hidden Markov Models. This measure characterizes the spatiotemporal changes in EEG signals by quantifying the information content of EEG signals, both at the time and spatial levels.Furthermore, we conduct a topological brain network analysis over the three stages of cognitive decline by employing the Graph Theory. The novelty of our work is twofold. Actually, this is the first work that: (i) addresses EEG brain network analysis over SCI, MCI and Mild AD stages simultaneously, and (ii) combines EpEn to Graph Theory since we have shown its effectiveness in quantifying the complete spatiotemporal alteration due to AD.In this thesis, we decided to invest the largest amount of EEG information for brain network analysis, by exploiting several frequency ranges (delta, theta, alpha, beta), several electrodes locations (instead of regions), and several network density scales (multiple graph thresholding). Therefore, another issue tackled in this thesis concerns the identification of relevant EEG markers to discriminate automatically between SCI, MCI and AD patients in the context of graph analysis framework. To this end, we propose an automatic hierarchical method for EEG analysis, which allows the extraction of relevant markers from large amount of information based on a single EEG connectivity measure.Finally, we also assess the correlation between the relevant EEG markers and the clinical markers at our disposal (MMSE, RL/RI-16, BREF)
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Matlis, Sean Eben Hill. "Functional network and spectral analysis of clinical EEG data to identify quantitative biomarkers and classify brain disorders." Thesis, 2016. https://hdl.handle.net/2144/19059.
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Many cognitive and neurological disorders today, such as Autism Spectrum Disorders (ASD) and various forms of epilepsy such as infantile spasms (IS), manifest as changes in voltage activity recorded in scalp electroencephalograms (EEG). Diagnosis of brain disease often relies on the interpretation of complex EEG features through visual inspection by clinicians. Although clinically useful, such interpretation is subjective and suffers from poor inter-rater reliability, which affects clinical care through increased variability and uncertainty in diagnosis. In addition, such qualitative assessments are often binary, and do not parametrically measure characteristics of disease manifestations. Many cognitive disorders are grouped by similar behaviors, but may arise from distinct biological causes, possibly represented by subtle electrophysiological differences. To address this, quantitative analytical tools - such as functional network connectivity, frequency-domain, and time-domain features - are being developed and applied to clinically obtained EEG data to identify electrophysiological biomarkers. These biomarkers enhance a clinician’s ability to accurately diagnose, categorize, and select treatment for various neurological conditions.
In the first study, we use spectral and functional network analysis of clinical EEG data recorded from a population of children to propose a cortical biomarker for autism. We first analyze a training set of age-matched (4–8 years) ASD and neurotypical children to develop hypotheses based on power spectral features and measures of functional network connectivity. From the training set of subjects, we derive the following hypotheses: 1) The ratio of the power of the posterior alpha rhythm (8–14 Hz) peak to the anterior alpha rhythm peak is significantly lower in ASD than control subjects. 2) The functional network density is lower in ASD subjects than control subjects. 3) A select group of edges provide a more sensitive and specific biomarker of ASD. We then test these hypotheses in a validation set of subjects and show that both the first and third hypotheses, but not the second, are validated. The validated features successfully classified the data with significant accuracy. These results provide a validated study for EEG biomarkers of ASD based on changes in brain rhythms and functional network characteristics.
We next perform a follow-up study that utilizes the same group of ASD and neurotypical subjects, but focuses on differences between these two groups in the sleep state. Motivated by the results from the previous study, we utilize the previously validated biomarkers, including the alpha ratio and the subset of edges found to be a sensitive biomarker of ASD, and test their effectiveness in the sleep state. To complement these frequency domain features, we also investigate the efficacy of several time domain measures. This investigation did not lead to significant findings, which may have important implications for the differences between sleep and wake states in ASD, or perhaps generally for clinical assessment, as well as for the effect of noise on signal in clinically obtained data.
Finally, we design a similar analysis framework to investigate a set of clinical EEG data recorded from a population of children with active infantile spasms (IS) (2-16 months), and age-matched neurotypical children, in both wake and sleep states. The goal of this analysis is to develop a quantitative biomarker from the EEG signal, which ultimately we will apply to predict the clinical outcome of children with IS. In addition to spectral and functional network analysis, we calculate time domain features previously found to correlate with seizures. We compare the two populations by each feature individually, test the effects of age on these features, use all features in a linear discriminant model to categorize IS versus neurotypical EEG, and test the findings using a leave-one-out validation test. We find almost every feature tested shows significant population differences between IS and control groups, and that taken together they serve as an effective classifier, with potential to be informative as to disease severity and long-term outcome. Furthermore, analysis of these features reveals two groups, indicating a possibility that these features reflect two distinct qualitative characteristics of IS and seizures.
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Zeman, Philip Michael. "Feasibility of Multi-Component Spatio-Temporal Modeling of Cognitively Generated EEG Data and its Potential Application to Research in Functional Anatomy and Clinical Neuropathology." Thesis, 2009. http://hdl.handle.net/1828/5010.
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This dissertation is a compendium of multiple research papers that, together, address two
main objectives. The first objective and primary research question is to determine
whether or not, through a procedure of independent component analysis (ICA)-based data
mining, volume-domain validation, and source volume estimation, it is possible to
construct a meaningful, objective, and informative model of brain activity from scalpacquired
EEG data. Given that a methodology to construct such a model can be created,
the secondary objective and research question investigated is whether or not the sources
derived from the EEG data can be used to construct a model of complex brain function
associated with the spatial navigation and the virtual Morris Water Task (vMWT).
The assumptions of the signal and noise characteristics of scalp-acquired EEG data were
discussed in the context of what is currently known about functional brain activity to
identify appropriate characteristics by which to separate the activities comprising EEG
data into parts. A new EEG analysis methodology was developed using both synthetic
and real EEG data that encompasses novel algorithms for (1) data-mining of the EEG to
obtain the activities of individual areas of the brain, (2) anatomical modeling of brain
sources that provides information about the 3-dimensional volumes from which each of
the activities separated from the EEG originates, and (3) validation of data mining results
to determine if a source activity found via the data-mining step originates from a distinct
modular unit inside the head or if it is an artefact. The methodology incorporating the
algorithms developed was demonstrated for EEG data collected from study participants
while they navigated a computer-based virtual maze environment. The brain activities of
participants were meaningfully depicted via brain source volume estimation and
representation of the activity relationships of multiple areas of the brain. A case study
was used to demonstrate the analysis methodology as applied to the EEG of an individual
person. In a second study, a group EEG dataset was investigated and activity
relationships between areas of the brain for participants of the group study were
individually depicted to show how brain activities of individuals can be compared to the
group.
The results presented in this dissertation support the conclusion that it is feasible to use
ICA-based data mining to construct a physiological model of coordinated parts of the
brain related to the vMWT from scalp-recorded EEG data. The methodology was
successful in creating an objective and informative model of brain activity from EEG
data. Furthermore, the evidence presented indicates that this methodology can be used to
provide meaningful evaluation of the brain activities of individual persons and to make
comparisons of individual persons against a group.
In sum, the main contributions of this body of work are 5 fold. The technical
contributions are: (1) a new data mining algorithm tailored for EEG, (2) an EEG
component validation algorithm that identifies noise components via their poor
representation in a head model, (3) a volume estimation algorithm that estimates the
region in the brain from which each source waveform found via data mining originates,
(4) a new procedure to study brain activities associated with spatial navigation. The main
contribution of this work to the understanding of brain function is (5) evidence of specific
functional systems within the brain that are used while persons participate in the vMWT
paradigm (Livingstone and Skelton, 2007) examining spatial navigation.<br>Graduate<br>0541<br>0622<br>0623
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Hiah, Pier Juhng, and 連培中. "Data Stream Mining Technology for ECG Signals of Chronic Pain: Real-Time Tracking and Clinical Correlation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/67yzx2.
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碩士<br>國立交通大學<br>電機資訊國際學程<br>105<br>Evaluating and tracking the progress of treatment for chronic pain is challenging because pain is a subjective experience and can be measured only by self-report. Electrocardiography (ECG) has been proven to be a promising source of physiological biomarkers for chronic pain. Previous studies had demonstrated that heart rate variability (HRV) could be associated with different types of pain and also pain perception. This study aims to identify the relationship between HRV indices and chronic pain through collecting resting ECG data and subjective pain severity from patients with chronic migraine and fibromyalgia before and after treatments. In addition, resting ECG data from healthy controls were also collected for comparison. The results derived from time, frequency, and non-linear analyses showed that the HRV of chronic patients were generally lower than that of healthy control subjects. Besides, the HRV of the chronic pain patients in the responder group significantly increased after the medical treatment, indicating that a useful biomarker of the treatment efficacy. Among 10 HRV indices, the non-linear Poincaré plot analysis is a promising HRV indices in monitoring pain severity as well as determining treatment efficacy. Finally, a data stream mining platform was developed for real-time streaming and analyzing of multimodal data. This platform is presented such that they can be used as an aid for biofeedback treatment of chronic pain in the future.
Libros sobre el tema "Clinical EEG data"
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Lopes da Silva, F. H., 1935-, Storm van Leeuwen W, and Rémond Antoine, eds. Clinical applications of computer analysis of EEG and other neurophysiological signals. Elsevier, 1986.
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Vanhatalo, Sampsa, and J. Matias Palva. Infraslow EEG Activity. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0032.
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Infraslow electroencephalographic (EEG) activity refers to frequencies below the conventional clinical EEG range that starts at about 0.5 Hz. Evidence suggests that salient EEG signals in the infraslow range are essential parts of many physiological and pathological conditions. In addition, brain is known to exhibit multitude of infraslow processes, which may be observed directly as fluctuations in the EEG signal amplitude, as infraslow fluctuations or intermittency in other neurophysiological signals, or as fluctuations in behavioural performance. Both physiological and pathological EEG activity may range from 0.01 Hz to several hundred Hz. In the clinical context, infraslow activity is commonly observed in the neonatal EEG, during and prior to epileptic seizures, and during sleep and arousals. Laboratory studies have demonstrated the presence of spontaneous infraslow EEG fluctuations or very slow event-related potentials in awake and sleeping subjects. Infraslow activity may not only arise in cortical and subcortical networks but is also likely to involve non-neuronal generators such as glial networks. The full, physiologically relevant range of brain mechanisms can be readily recorded with wide dynamic range direct-current (DC)-coupled amplifiers or full-band EEG (FbEEG). Due to the different underlying mechanisms, a single FbEEG recording can even be perceived as a multimodal recording where distinct brain modalities can be studied simultaneously by performing data analysis for different frequency ranges. FbEEG is likely to become the standard approach for a wide range of applications in both basic science and in the clinic.
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Sutter, Raoul, Peter W. Kaplan, and Donald L. Schomer. Historical Aspects of Electroencephalography. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0001.
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Electroencephalography (EEG), a dynamic real-time recording of electrical neocortical brain activity, began in the 1600s with the discovery of electrical phenomena and the concept of an “action current.” The galvanometer was introduced in the 1800s and the first bioelectrical observations of human brain signals were made in the 1900s. Certain EEG patterns were associated with brain disorders, increasing the clinical and scientific use of EEG. In the 1980s, technical advances allowed EEGs to be digitized and linked with videotape recording. In the 1990s, digital data storage increased and computer networking enabled remote real-time EEG reading, which made possible continuous EEG (cEEG) monitoring. Manual cEEG analysis became increasingly labor-intensive, calling for methods to assist this process. In the 2000s, complex algorithms enabling quantitative EEG analyses were introduced, with a new focus on shared activity between rhythms, including phase and magnitude synchrony. The automation of spectral analysis enabled studies of spectral content.
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Herring, Christina. Neuromodulation in Psychiatric Disorders. Edited by Anthony J. Bazzan and Daniel A. Monti. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190690557.003.0013.
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Quantitative electroencephalogram (qEEG) is the transformation of the EEG by spectral analysis in which the amount of electrical activity at a particular frequency is determined and compared against a normative data base. EEG findings are specific for different psychiatric problems and help reveal brain abnormalities associated with psychological symptoms. Repetitive transcranial magnetic stimulation (rTMS) is a system of delivering multiple pulses within a short time period that induce changes that outlast the stimulation period. Operant conditioning involves providing a reward to increase the probability of a certain behavior. Neurofeedback involves recording, analyzing, and presenting results of qEEG analyses in near real-time to patients in order to promote changes in brain electrical activity. This chapter reviews how neuromodulation works both clinically and from a neurophysiological perspective. This chapter also reviews current clinical data on the use of neuromodulation approaches for improving mental health and well-being.
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Thomas, James, and Tanya Monaghan. Clinical data interpretation. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199593972.003.0019.
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Katirji, Bashar. Electromyography in Clinical Practice. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190603434.001.0001.
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Clinical Electromyography in Clinical Practice provides case-based learning of clinical Electromyography (EMG) with a main mission of reducing the gap between theory and practice in the field of electrodiagnostic medicine. The book format includes four introductory chapters that acquaint the discipline and scope of the EMG Examination to the beginners. This include chapters on nerve conduction studies, needle EMG, and specialized testing including late responses, repetitive nerve stimulation and single fiber EMG. Discussion on the electrodiagnostic and clinical EMG findings in the numerous neuromuscular disorders including anterior horn cell disorders, peripheral neuropathies, neuromuscular junction disorders and myopathies. The second part of the book includes comprehensive presentations of 27 cases that encompass the most common disorders encountered in the EMG laboratory and are presented in a similar layout. These are subdivided into (1) focal disorders of the lower extremity, (2) focal disorders of the upper extremity, and (3) generalized neuromuscular disorders. The book focuses on problem solving through analysis of the data obtained on nerve conduction studies and needle EMG. This is meant to be a bedside analysis of data, similar to what occurs in the EMG laboratory on a daily basis. The exact values obtained on nerve conduction studies are examined and the details of the findings on needle EMG are studied. A final diagnosis is then made. This is followed by a detailed discussion of the clinical and electrodiagnostic findings of the disorder. Clinical Electromyography in Clinical Practice is an ideal book for physicians interested in learning and mastering the clinical practice of clinical EMG. This includes specialists in the field of neurology, physical medicine and rehabilitation, orthopedics, hand surgery, neurosurgery, spine, rheumatology and pain management.
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Stanley, Barbara, and Antonia New, eds. Borderline Personality Disorder. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199997510.001.0001.
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Until recently, borderline personality disorder (BPD) has been the stepchild of psychiatric disorders. Many researchers even questioned its existence. Clinicians have been reluctant to reveal the diagnosis to patients because of the stigma attached to it. But individuals with BPD suffer terribly and a significant proportion die by suicide and engage in nonsuicidal self-injury. The aim of this primer on BPD is to fill this void and provide clinicians with an accessible, easy-to-use, clinically oriented, evidenced-based guide for early-stage BPD. We present the most up to date data about BPD by leading experts in the field in a format accessible to trainees and professionals working with individuals with BPD and their family members. The volume is comprehensive and covers the etiology of BPD, its clinical presentation and comorbid disorders, genetics and neurobiology of BPD, effective treatment approaches to BPD, the role of advocacy, and the treatment of special subpopulations (e.g., forensic) in the clinical management of BPD.
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Staedtke, Verena, and Eric H. Kossoff. Epilepsy Syndromes in Childhood. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0074.
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Epilepsy syndromes of childhood are a heterogeneous group of disorders that occur at specific neurodevelopmental stages, with a variable prognosis ranging from benign to catastrophic. In clinical practice they are categorized based on seizure type, age of onset, clinical presentation, electroencephalographic (EEG) findings, as well as response to treatment. In addition, recent advancements in neuroimaging and genetic testing have become important diagnostic tools revealing underlying defects for some of these syndromes. This knowledge has consequences for clinical practice, as it opens new perspectives for early diagnosis, prognosis and treatment. Here, we provide an up-to-date overview of the most common pediatric epilepsy syndromes, their clinical findings, associated EEG findings, and clinical management.
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Poddubnyy, Denis, and Hildrun Haibel. Treatment: DMARDs. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198734444.003.0021.
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In axial spondyloarthritis (axSpA) there is little evidence to support use of classical synthetic disease-modifying antirheumatic drugs (DMARDs), with the majority of studies performed in advanced ankylosing spondylitis. Sulfasalazine is the best investigated DMARD in axSpA. Its positive clinical effect, if any, seems to be more prominent in the presence of peripheral arthritis, although a certain proportion of patients with axial disease might benefit from sulfasalazine therapy. Available data indicate that there is no evidence that methotrexate might be effective in axial disease, and only marginal evidence exists in support of methotrexate use in case of peripheral involvement. No true disease-modifying properties (e.g. retardation of structural damage progression in the spine) have been demonstrated for DMARDs in axSpA to date. Efficacy of a combination therapy (e.g. methotrexate plus sulfasalazine) as well as benefits of methotrexate (or other DMARDs) in addition to tumour necrosis factor α inhibitors in axSpA remain uncertain.
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Kirollos, Ramez, Adel Helmy, Simon Thomson, and Peter Hutchinson, eds. Oxford Textbook of Neurological Surgery. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198746706.001.0001.
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Neurosurgery is a rapidly developing and technically demanding branch of surgery that requires a detailed knowledge of the basic sciences and a thorough clinical approach. The Oxford Textbook of Neurological Surgery is an up-to-date, objective, and readable text that covers the full scope of neurosurgical practice. Each section takes a dual approach with ‘generic surgical management’ chapters that focus on specific clinical problems facing the neurosurgeon (e.g. sellar/suprasellar tumour, intradural spina tumours, and others) and ‘pathology-specific’ chapters (e.g. glioma, meningeal tumours, scoliosis and spinal deformity, aneurysm, and others). Where appropriate, this division provides the reader with easily accessible information for both clinical problems which present in a regional fashion and specific pathologies. The generic chapters cover aspects such as operative approaches, neuroanatomy, and nuances.
Capítulos de libros sobre el tema "Clinical EEG data"
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Gerlá, Vaclav, Lenka Lhotska, Matej Murgas, Vladana Djordjevic Radisavljevic, Vladimir Krajca, and Vaclav Kremen. "An Incremental Approach to Clinical EEG Data Classification." In IFMBE Proceedings. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11128-5_122.
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Hammond, E. J., C. P. Barber, and B. J. Wilder. "Flash Visual Evoked Potential Topographic Mapping: Normative and Clinical Data." In Topographic Brain Mapping of EEG and Evoked Potentials. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-72658-3_27.
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Hammond, E. J., C. P. Barber, and B. J. Wilder. "Scalp Topography of Red LED Flash-Evoked Potentials: Normal and Clinical Data." In Topographic Brain Mapping of EEG and Evoked Potentials. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-72658-3_42.
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Chowdhury, Linkon, Bristy Roy Chowdhury, V. Rajinikanth, and Nilanjan Dey. "A Framework to Evaluate and Classify the Clinical-Level EEG Signals with Epilepsy." In Proceedings of International Conference on Data Science and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7561-7_8.
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Dasgupta, Abhijit, Losiana Nayak, Ritankar Das, Debasis Basu, Preetam Chandra, and Rajat K. De. "Feature Selection and Fuzzy Rule Mining for Epileptic Patients from Clinical EEG Data." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69900-4_11.
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Molina, Edward, Ricardo Salazar-Cabrera, and Diego M. López. "NeuroEHR: Open Source Telehealth System for the Management of Clinical Data, EEG and Remote Diagnosis of Epilepsy." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00350-0_35.
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Herff, Christian, and Dean J. Krusienski. "Extracting Features from Time Series." In Fundamentals of Clinical Data Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99713-1_7.
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AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.
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Silva, Hugo, André Lourenço, Ana Fred, and Joaquim Filipe. "Clinical Data Privacy and Customization via Biometrics Based on ECG Signals." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25364-5_12.
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Storås, Andrea M., Michael A. Riegler, Trine B. Haugen, et al. "Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17030-0_9.
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AbstractThe in vitro fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep features are extracted from the video frames and form the basis for a clustering method. The method provides meaningful clusters representing different stages of the intracytoplasmic sperm injection procedure. The clusters can lead to more efficient examinations and possible new insights that can improve clinical practice. Further on, it may also contribute to improved clinical outcomes due to increased understanding about the technical aspects and better results of the procedure. Despite promising results, the proposed method can be further improved by increasing the amount of data and exploring other types of features.
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Chukwu, Emmanuel C., and Pedro A. Moreno-Sánchez. "Enhancing Arrhythmia Diagnosis with Data-Driven Methods: A 12-Lead ECG-Based Explainable AI Model." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_16.
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AbstractAccurate and early prediction of arrhythmias using Electrocardiograms (ECG) presents significant challenges due to the non-stationary nature of ECG signals and inter-patient variability, posing difficulties even for seasoned cardiologists. Deep Learning (DL) methods offer precision in identifying diagnostic ECG patterns for arrhythmias, yet they often lack the transparency needed for clinical application, thus hindering their broader adoption in healthcare. This study introduces an explainable DL-based prediction model using ECG signals to classify nine distinct arrhythmia categories. We evaluated various DL architectures, including ResNet, DenseNet, and VGG16, using raw ECG data. The ResNet34 model emerged as the most effective, achieving an Area Under the Receiver Operating Characteristic (AUROC) of 0.98 and an F1-score of 0.826. Additionally, we explored a hybrid approach that combines raw ECG signals with Heart Rate Variability (HRV) features. Our explainability analysis, utilizing the SHAP technique, identifies the most influential ECG leads for each arrhythmia type and pinpoints critical signal segments for individual disease prediction. This study emphasizes the importance of explainability in arrhythmia prediction models, a critical aspect often overlooked in current research, and highlights its potential to enhance model acceptance and utility in clinical settings.
Actas de conferencias sobre el tema "Clinical EEG data"
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Dasgupta, Abhijit, Ritankar Das, Losiana Nayak, and Rajat K. De. "Analyzing epileptogenic brain connectivity networks using clinical EEG data." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359791.
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Huang, Zexin, Liyong Han, Zhihua Huang, Zhixiong Lin, and Chenghua Wang. "Automated data set construction system for clinical EEG research." In 2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2023. http://dx.doi.org/10.1109/cisp-bmei60920.2023.10373261.
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Binnie, C. D. "Long term EEG recording and its role in clinical practice." In IEE Colloquium on Data Logging of Physiological Signals. IEE, 1995. http://dx.doi.org/10.1049/ic:19951385.
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Yang, S., S. Lopez, M. Golmohammadi, I. Obeid, and J. Picone. "Semi-automated annotation of signal events in clinical EEG data." In 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2016. http://dx.doi.org/10.1109/spmb.2016.7846855.
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Aghaeeaval, Mahsa, Nathaniel Bendahan, Zaitoon Shivji, et al. "Prediction of patient survival following postanoxic coma using EEG data and clinical features." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9629946.
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Doborjeh, Maryam Gholami, and Nikola Kasabov. "Personalised modelling on integrated clinical and EEG Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network system." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727358.
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Smid, Jerusa, Ricardo Nitrini, Vilma Martins, et al. "THE BRAZILIAN SURVEILLANCE FOR PRION DISEASE: CURRENT DATA." In XIII Meeting of Researchers on Alzheimer's Disease and Related Disorders. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1980-5764.rpda014.
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Background: The Brazilian Surveillance for Prion Disease began in 2005 with compulsory notification of suspected cases. Objective: To determine the diagnosis of reported patients to the Brazilian Surveillance for Prion Disease and evaluate the clinical and genetic data. Methods: Data from the notification sheet were collected and patients were classified according to current clinical and pathological criteria. Results: 757 cases were notified from 2005 to 2019. 51 cases were defined DCJ, 295 probable DCJ, 172 possible DCJ and 38 genetic DCJ. 55 patients had other diagnosis and 146 were unclassified (missing data). The most frequent mutations were E200K, D178N, P102L and V180I. Among defined and probable DCJ: 55% were female, mean age was 62 y.o. and median age was 61.9 y.o.; 51.8% were M129M and 23.2% V129V. CSF 14.3.3 was positive in 69.2%, disease-typical EEG findings were observed in 42.3% and MRI revealed typical findings in 76.9%. No variant CJD were diagnosed. Discussion: considering the Brazilian population and the prevalence of CJD worldwilde, we expected more cases of CJD than were notified. Methionine and valine homozygotes are overrepresented, in agreement with international data. MRI were the more useful subsidiary test for clinical classification.. Conclusion: besides the undernotification, the Brazilian surveillance for prion diseases evaluated 757 suspected cases in the last 15 years. 50.7% were probable, defined CJD or genetic prion disease.
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M. Alves, Lorraine, Klaus F. Côco, Mariane L. de Souza, and Patrick M. Ciarelli. "Graph Theory Analysis of Microstates in Attention-Deficit Hyperactivity Disorder." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1481.
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Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most common disorders of childhood and youth. The diagnosis of ADHD remains essentially clinical, based on history and questionnaires for symptom assessment, therefore, a biomarker can be of great value to reduce the inherent uncertainty of clinical diagnosis. In recent years, several studies have been carried out to assess the usefulness of neurophysiological (electroencephalography - EEG)and functional image data to assist in the process of diagnosing ADHD. Previous researches have revealed evidences that microstates are selectively affected by ADHD, indicating that their analysis may be a useful tool in methods of automatic disease identication. In this paper is proposed a new methodology for the detection of ADHD using EEG microstate analysis and graph theory. The proposed method allows modeling and interpreting each microstate as a complex network, which permits to identify the effect of ADHD on some characteristics of the built networks. In addition, it provides useful information to identify ADHD and subtypes patients with an accuracy around 99%, indicating that the proposed method is promising.
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Sazonova, O. B., and E. M. Troshina. "REFLECTION IN THE EEG OF DISORDERS OF CEREBRAL HEMODYNAMICS IN CHRONIC CEREBRAL ISCHEMIA IN CHILDREN." In NOVEL TECHNOLOGIES IN MEDICINE, BIOLOGY, PHARMACOLOGY AND ECOLOGY. Institute of information technology, 2022. http://dx.doi.org/10.47501/978-5-6044060-2-1.368-375.
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The paper presents the results of a study of the bioelectrical activity of the brain in 77 patients aged 2 to 18 years old with chronic cerebral ischemia, due to pathology of the main arteries of the brain, observed and operated in the Institute of Neurosurgery of N.N. Burdenko from 2012. The study showed the role of EEG in assessing the functional state of the brain in chil-dren with chronic cerebrovascular insufficiency of various etiopathogenesis in children, de-pending on the age of the child. Comparison of EEG data in extra- and intracranial cerebral vascular pathology was performed. Revealed more pronounced changes with steno-occlusive disorders in the intracranial arteries. EEG is dominated by slow forms of activity or their combination with acute potentials. Extracranial pathology led to the predominance of signs of irritation in the bioelectric picture of the brain: accelerated, pointed alpha rhythm, acute potentials, frequent fluctuations. Detailed clinical and neurophysiological examination using EEG served as a rationale for diagnosis, determination of treatment tactics and indications and contraindications for surgical intervention.
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Zhavoronkova, Ludmila Alexeevna, Olga Arsen’evna Maksakova, Elena Mikhailovna Кushnir, and Irina Gennadievna Skorjatina. "DIAGNOSTIC AND REHABILITATION OPPORTUNITIES OF DUAL-TASKS FOR BRAIN TRAUMA." In International conference New technologies in medicine, biology, pharmacology and ecology (NT +M&Ec ' 2020). Institute of information technology, 2020. http://dx.doi.org/10.47501/978-5-6044060-0-7.06.
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Complex clinical, EEG and stabilographic examination revealed predominantly cognitive deficit in patients
 with moderate traumatic injury (mTBI) while dual tasks were performed. The EEG data demonstrated a decrease
 in the coherence for slow (delta-theta) rhythms in the frontal-temporal areas predominantly of the left
 hemisphere during cognitive tasks performance in patients. In healthy subjects an increase of EEG coherence
 for slow spectral bands was observed in these brain areas by contrast. An increase of EEG coherence
 was obtained for fast spectral bands - alpha2 and beta, predominantly at the right hemisphere while the motor
 components of the dual tasks were performed in healthy adults and patients. Rehabilitation course involving
 the use of dual-tasking contributed to a predominant reduction in cognitive deficits and an increase
 of EEG coherence at the frontal-temporal areas of the left hemisphere. So, dual-tasks may be used as diagnostic
 tool in patients after mTBI. Pilot studies allowed the proposed also rehabilitation effect of dualtasking
 in mTBI patients with primary and predominant restoration of cognitive functions and recovery of
 EEG coherence for slow spectral bands after rehabilitation course. So, our data allowed to propose that dual-tasks may be used as diagnostic as well rehabilitation tool in patients after mTB with the most sensitivity of the left hemisphere to traumatic effect and rehabilitation procedures.
Informes sobre el tema "Clinical EEG data"
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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42562.
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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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de Carvalho, Clístenes Crístian, Ioannis Kapsokalyvas, and Kariem El-Boghdadly. Second-generation supraglottic airways vs endotracheal tubes in adults undergoing abdominopelvic surgeries: a protocol for a systematic review with pairwise meta-analyses of randomised clinical trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.9.0041.
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Review question / Objective: We aim to compare second-generation supraglottic airways with endotracheal tubes for perioperative safety and quality of postoperative recovery as well as for ventilation performance and risk of pulmonary aspiration. Eligibility criteria: Inclusion criteria will be as follows: randomized clinical trials; human patients aged ≥ 16 years undergoing abdominopelvic procedures under general anaesthesia from any population (e.g., general population, pregnant women, obese patients); data available on any outcome related to insertion performance (e.g., failed first attempt, failed insertion, and time to insertion), ventilation efficacy (e.g., leak pressure, leak fraction, and ventilation inadequacy), risk of regurgitation and aspiration (e.g., gastric insufflation, regurgitation, and aspiration), quality of postoperative recovery (e.g., sore throat, hoarseness, and postoperative nausea and vomiting [PONV]), and major complications (e.g., laryngospasm, bronchospasm, and hypoxemia); and comparison between any second-generation SGA and an endotracheal tube. We will exclude: studies reported in a language that prevent us of extracting relevant information; outcomes with no objective data presented (i.e., effect sizes, measures of dispersion, frequency, etc.); and studies with contradictory data.
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Meng, kairui, yulin You, lijuan Chen, and yicheng Liu. A meta analysis on the efficacy of Chengqi Decoction in the treatment of ARDS/ALI. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.8.0040.
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Review question / Objective: What is the clinical efficacy of combing Chengqi Decoction in the treatment of ARDS/ALI, compared with the conventional treatment of ARDA/ALI with western medicine? Condition being studied: Acute respiratory distress syndrome(ARDS), Acute lung injury(ALI). Eligibility criteria: (1) lacking literature data (e.g., nonpaired studies) (2) duplicate publications; (3) do not have access to the full text (4) conference reports, system reviews, protocols, or abstracts; (5) RCTs with small sample sizes; . (5)The treatment course is less than 6 days (6) Exclude articles with high risk of deviation
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Leavy, Michelle B., Danielle Cooke, Sarah Hajjar, et al. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Report on Registry Configuration. Agency for Healthcare Research and Quality (AHRQ), 2020. http://dx.doi.org/10.23970/ahrqepcregistryoutcome.
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Background: Major depressive disorder is a common mental disorder. Many pressing questions regarding depression treatment and outcomes exist, and new, efficient research approaches are necessary to address them. The primary objective of this project is to demonstrate the feasibility and value of capturing the harmonized depression outcome measures in the clinical workflow and submitting these data to different registries. Secondary objectives include demonstrating the feasibility of using these data for patient-centered outcomes research and developing a toolkit to support registries interested in sharing data with external researchers. Methods: The harmonized outcome measures for depression were developed through a multi-stakeholder, consensus-based process supported by AHRQ. For this implementation effort, the PRIME Registry, sponsored by the American Board of Family Medicine, and PsychPRO, sponsored by the American Psychiatric Association, each recruited 10 pilot sites from existing registry sites, added the harmonized measures to the registry platform, and submitted the project for institutional review board review Results: The process of preparing each registry to calculate the harmonized measures produced three major findings. First, some clarifications were necessary to make the harmonized definitions operational. Second, some data necessary for the measures are not routinely captured in structured form (e.g., PHQ-9 item 9, adverse events, suicide ideation and behavior, and mortality data). Finally, capture of the PHQ-9 requires operational and technical modifications. The next phase of this project will focus collection of the baseline and follow-up PHQ-9s, as well as other supporting clinical documentation. In parallel to the data collection process, the project team will examine the feasibility of using natural language processing to extract information on PHQ-9 scores, adverse events, and suicidal behaviors from unstructured data. Conclusion: This pilot project represents the first practical implementation of the harmonized outcome measures for depression. Initial results indicate that it is feasible to calculate the measures within the two patient registries, although some challenges were encountered related to the harmonized definition specifications, the availability of the necessary data, and the clinical workflow for collecting the PHQ-9. The ongoing data collection period, combined with an evaluation of the utility of natural language processing for these measures, will produce more information about the practical challenges, value, and burden of using the harmonized measures in the primary care and mental health setting. These findings will be useful to inform future implementations of the harmonized depression outcome measures.
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Totten, Annette, Dana M. Womack, Marian S. McDonagh, et al. Improving Rural Health Through Telehealth-Guided Provider-to-Provider Communication. Agency for Healthcare Research and Quality, 2022. http://dx.doi.org/10.23970/ahrqepccer254.
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Objectives. To assess the use, effectiveness, and implementation of telehealth-supported provider-to-provider communication and collaboration for the provision of healthcare services to rural populations and to inform a scientific workshop convened by the National Institutes of Health Office of Disease Prevention on October 12–14, 2021. Data sources. We conducted a comprehensive literature search of Ovid MEDLINE®, CINAHL®, Embase®, and Cochrane CENTRAL. We searched for articles published from January 1, 2015, to October 12, 2021, to identify data on use of rural provider-to-provider telehealth (Key Question 1) and the same databases for articles published January 1, 2010, to October 12, 2021, for studies of effectiveness and implementation (Key Questions 2 and 3) and to identify methodological weaknesses in the research (Key Question 4). Additional sources were identified through reference lists, stakeholder suggestions, and responses to a Federal Register notice. Review methods. Our methods followed the Agency for Healthcare Research and Quality Methods Guide (available at https://effectivehealthcare.ahrq.gov/topics/cer-methods-guide/overview) and the PRISMA reporting guidelines. We used predefined criteria and dual review of abstracts and full-text articles to identify research results on (1) regional or national use, (2) effectiveness, (3) barriers and facilitators to implementation, and (4) methodological weakness in studies of provider-to-provider telehealth for rural populations. We assessed the risk of bias of the effectiveness studies using criteria specific to the different study designs and evaluated strength of evidence (SOE) for studies of similar telehealth interventions with similar outcomes. We categorized barriers and facilitators to implementation using the Consolidated Framework for Implementation Research (CFIR) and summarized methodological weaknesses of studies. Results. We included 166 studies reported in 179 publications. Studies on the degree of uptake of provider-to-provider telehealth were limited to specific clinical uses (pharmacy, psychiatry, emergency care, and stroke management) in seven studies using national or regional surveys and claims data. They reported variability across States and regions, but increasing uptake over time. Ninety-seven studies (20 trials and 77 observational studies) evaluated the effectiveness of provider-to-provider telehealth in rural settings, finding that there may be similar rates of transfers and lengths of stay with telehealth for inpatient consultations; similar mortality rates for remote intensive care unit care; similar clinical outcomes and transfer rates for neonates; improvements in medication adherence and treatment response in outpatient care for depression; improvements in some clinical monitoring measures for diabetes with endocrinology or pharmacy outpatient consultations; similar mortality or time to treatment when used to support emergency assessment and management of stroke, heart attack, or chest pain at rural hospitals; and similar rates of appropriate versus inappropriate transfers of critical care and trauma patients with specialist telehealth consultations for rural emergency departments (SOE: low). Studies of telehealth for education and mentoring of rural healthcare providers may result in intended changes in provider behavior and increases in provider knowledge, confidence, and self-efficacy (SOE: low). Patient outcomes were not frequently reported for telehealth provider education, but two studies reported improvement (SOE: low). Evidence for telehealth interventions for other clinical uses and outcomes was insufficient. We identified 67 program evaluations and qualitative studies that identified barriers and facilitators to rural provider-to-provider telehealth. Success was linked to well-functioning technology; sufficient resources, including time, staff, leadership, and equipment; and adequate payment or reimbursement. Some considerations may be unique to implementation of provider-to-provider telehealth in rural areas. These include the need for consultants to better understand the rural context; regional initiatives that pool resources among rural organizations that may not be able to support telehealth individually; and programs that can support care for infrequent as well as frequent clinical situations in rural practices. An assessment of methodological weaknesses found that studies were limited by less rigorous study designs, small sample sizes, and lack of analyses that address risks for bias. A key weakness was that studies did not assess or attempt to adjust for the risk that temporal changes may impact the results in studies that compared outcomes before and after telehealth implementation. Conclusions. While the evidence base is limited, what is available suggests that telehealth supporting provider-to-provider communications and collaboration may be beneficial. Telehealth studies report better patient outcomes in some clinical scenarios (e.g., outpatient care for depression or diabetes, education/mentoring) where telehealth interventions increase access to expertise and high-quality care. In other applications (e.g., inpatient care, emergency care), telehealth results in patient outcomes that are similar to usual care, which may be interpreted as a benefit when the purpose of telehealth is to make equivalent services available locally to rural residents. Most barriers to implementation are common to practice change efforts. Methodological weaknesses stem from weaker study designs, such as before-after studies, and small numbers of participants. The rapid increase in the use of telehealth in response to the Coronavirus disease 2019 (COVID-19) pandemic is likely to produce more data and offer opportunities for more rigorous studies.
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McCausland, Rachel, Joann Fontanarosa, and Ravi Patel. Nonemergent Percutaneous Coronary Intervention Versus Optimal Medical Treatment for Stable Ischemic Heart Disease: A Rapid Response Literature Review. Agency for Healthcare Research and Quality (AHRQ), 2023. http://dx.doi.org/10.23970/ahrqepcrapidcoronary.
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Aims. There is uncertainty around the optimal role of percutaneous coronary intervention (PCI) for management of chronic coronary syndrome, specifically when patients have disease in multiple coronary vessels and disease in the proximal portion of the left anterior descending coronary artery. This uncertainty was reflected in 2021 guidance from the American College of Cardiology (ACC)/American Heart Association (AHA) on coronary artery revascularization. The Agency for Healthcare Research and Quality has commissioned this rapid response literature review to meet a Congressional request for a summary of recent evidence on the benefits of angioplasties conducted in nonemergency situations. Methods. This rapid response literature review on the comparative effectiveness of nonemergent PCI followed established best systematic review methods, modified to meet a shortened project timeframe. We searched PubMed®, Embase®, and the Trip© medical database from 2018 through April 2023 for systematic reviews (SRs), clinical practice guidelines, and randomized controlled trials, and summarized the evidence comparing PCI to optimal medical therapy (OMT) for stable ischemic heart disease (SIHD). Our primary outcomes of interest were major objective cardiovascular outcomes, including mortality, myocardial infarction, stroke, urgent revascularization, or composites of one or more of these hard clinical outcomes. Where available, we also abstracted patient reported outcomes (e.g., angina severity and quality of life [QoL]) from included studies. Findings. Key findings from nine SRs and one primary study include: • The body of evidence directly comparing PCI to OMT for SIHD has remained largely unchanged since the 2021 ACC/AHA guidance’s publication. • Most studies of revascularization for coronary artery disease do not focus on direct head-to-head comparisons of PCI versus OMT for SIHD but instead either (1) compare OMT to invasive revascularization (PCI and coronary artery bypass graft [CABG] combined cohort); (2) compare PCI to CABG; or (3) compare different PCI techniques. • Another factor that complicates comparison is that the meta-analyses often included data from CABG and PCI combined cohorts (e.g., the recent landmark ISCHEMIA trial) but reported the outcomes as PCI specific. • In the general SIHD population, our review did not find evidence to support survival benefit or effect on hard clinical outcomes when PCI is added to OMT. • Limited evidence indicates there may be a beneficial effect of PCI on angina symptoms and measures of QoL, but most systematic reviews focused on major objective cardiovascular outcomes and did not consider QoL or freedom from angina. • Both OMT and PCI have evolved significantly during the period of time in which the systematic reviews’ included studies were conducted. It is not clear how these changes may have affected the applicability of past studies to current practice. Conclusions. The evidence directly comparing PCI to OMT for SIHD has remained largely unchanged since publication of the 2021 ACC/AHA guidelines. More research is needed to verify the comparative effectiveness of nonemergent PCI compared to medical treatment for individuals with SIHD, and how the effectiveness varies by certain patient populations and clinical presentation.
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Newman-Toker, David E., Susan M. Peterson, Shervin Badihian, et al. Diagnostic Errors in the Emergency Department: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), 2022. http://dx.doi.org/10.23970/ahrqepccer258.
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Objectives. Diagnostic errors are a known patient safety concern across all clinical settings, including the emergency department (ED). We conducted a systematic review to determine the most frequent diseases and clinical presentations associated with diagnostic errors (and resulting harms) in the ED, measure error and harm frequency, as well as assess causal factors. Methods. We searched PubMed®, Cumulative Index to Nursing and Allied Health Literature (CINAHL®), and Embase® from January 2000 through September 2021. We included research studies and targeted grey literature reporting diagnostic errors or misdiagnosis-related harms in EDs in the United States or other developed countries with ED care deemed comparable by a technical expert panel. We applied standard definitions for diagnostic errors, misdiagnosis-related harms (adverse events), and serious harms (permanent disability or death). Preventability was determined by original study authors or differences in harms across groups. Two reviewers independently screened search results for eligibility; serially extracted data regarding common diseases, error/harm rates, and causes/risk factors; and independently assessed risk of bias of included studies. We synthesized results for each question and extrapolated U.S. estimates. We present 95 percent confidence intervals (CIs) or plausible range (PR) bounds, as appropriate. Results. We identified 19,127 citations and included 279 studies. The top 15 clinical conditions associated with serious misdiagnosis-related harms (accounting for 68% [95% CI 66 to 71] of serious harms) were (1) stroke, (2) myocardial infarction, (3) aortic aneurysm and dissection, (4) spinal cord compression and injury, (5) venous thromboembolism, (6/7 – tie) meningitis and encephalitis, (6/7 – tie) sepsis, (8) lung cancer, (9) traumatic brain injury and traumatic intracranial hemorrhage, (10) arterial thromboembolism, (11) spinal and intracranial abscess, (12) cardiac arrhythmia, (13) pneumonia, (14) gastrointestinal perforation and rupture, and (15) intestinal obstruction. Average disease-specific error rates ranged from 1.5 percent (myocardial infarction) to 56 percent (spinal abscess), with additional variation by clinical presentation (e.g., missed stroke average 17%, but 4% for weakness and 40% for dizziness/vertigo). There was also wide, superimposed variation by hospital (e.g., missed myocardial infarction 0% to 29% across hospitals within a single study). An estimated 5.7 percent (95% CI 4.4 to 7.1) of all ED visits had at least one diagnostic error. Estimated preventable adverse event rates were as follows: any harm severity (2.0%, 95% CI 1.0 to 3.6), any serious harms (0.3%, PR 0.1 to 0.7), and deaths (0.2%, PR 0.1 to 0.4). While most disease-specific error rates derived from mainly U.S.-based studies, overall error and harm rates were derived from three prospective studies conducted outside the United States (in Canada, Spain, and Switzerland, with combined n=1,758). If overall rates are generalizable to all U.S. ED visits (130 million, 95% CI 116 to 144), this would translate to 7.4 million (PR 5.1 to 10.2) ED diagnostic errors annually; 2.6 million (PR 1.1 to 5.2) diagnostic adverse events with preventable harms; and 371,000 (PR 142,000 to 909,000) serious misdiagnosis-related harms, including more than 100,000 permanent, high-severity disabilities and 250,000 deaths. Although errors were often multifactorial, 89 percent (95% CI 88 to 90) of diagnostic error malpractice claims involved failures of clinical decision-making or judgment, regardless of the underlying disease present. Key process failures were errors in diagnostic assessment, test ordering, and test interpretation. Most often these were attributed to inadequate knowledge, skills, or reasoning, particularly in “atypical” or otherwise subtle case presentations. Limitations included use of malpractice claims and incident reports for distribution of diseases leading to serious harms, reliance on a small number of non-U.S. studies for overall (disease-agnostic) diagnostic error and harm rates, and methodologic variability across studies in measuring disease-specific rates, determining preventability, and assessing causal factors. Conclusions. Although estimated ED error rates are low (and comparable to those found in other clinical settings), the number of patients potentially impacted is large. Not all diagnostic errors or harms are preventable, but wide variability in diagnostic error rates across diseases, symptoms, and hospitals suggests improvement is possible. With 130 million U.S. ED visits, estimated rates for diagnostic error (5.7%), misdiagnosis-related harms (2.0%), and serious misdiagnosis-related harms (0.3%) could translate to more than 7 million errors, 2.5 million harms, and 350,000 patients suffering potentially preventable permanent disability or death. Over two-thirds of serious harms are attributable to just 15 diseases and linked to cognitive errors, particularly in cases with “atypical” manifestations. Scalable solutions to enhance bedside diagnostic processes are needed, and these should target the most commonly misdiagnosed clinical presentations of key diseases causing serious harms. New studies should confirm overall rates are representative of current U.S.-based ED practice and focus on identified evidence gaps (errors among common diseases with lower-severity harms, pediatric ED errors and harms, dynamic systems factors such as overcrowding, and false positives). Policy changes to consider based on this review include: (1) standardizing measurement and research results reporting to maximize comparability of measures of diagnostic error and misdiagnosis-related harms; (2) creating a National Diagnostic Performance Dashboard to track performance; and (3) using multiple policy levers (e.g., research funding, public accountability, payment reforms) to facilitate the rapid development and deployment of solutions to address this critically important patient safety concern.
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Rankin, Nicole, Deborah McGregor, Candice Donnelly, et al. Lung cancer screening using low-dose computed tomography for high risk populations: Investigating effectiveness and screening program implementation considerations: An Evidence Check rapid review brokered by the Sax Institute (www.saxinstitute.org.au) for the Cancer Institute NSW. The Sax Institute, 2019. http://dx.doi.org/10.57022/clzt5093.
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Background Lung cancer is the number one cause of cancer death worldwide.(1) It is the fifth most commonly diagnosed cancer in Australia (12,741 cases diagnosed in 2018) and the leading cause of cancer death.(2) The number of years of potential life lost to lung cancer in Australia is estimated to be 58,450, similar to that of colorectal and breast cancer combined.(3) While tobacco control strategies are most effective for disease prevention in the general population, early detection via low dose computed tomography (LDCT) screening in high-risk populations is a viable option for detecting asymptomatic disease in current (13%) and former (24%) Australian smokers.(4) The purpose of this Evidence Check review is to identify and analyse existing and emerging evidence for LDCT lung cancer screening in high-risk individuals to guide future program and policy planning. Evidence Check questions This review aimed to address the following questions: 1. What is the evidence for the effectiveness of lung cancer screening for higher-risk individuals? 2. What is the evidence of potential harms from lung cancer screening for higher-risk individuals? 3. What are the main components of recent major lung cancer screening programs or trials? 4. What is the cost-effectiveness of lung cancer screening programs (include studies of cost–utility)? Summary of methods The authors searched the peer-reviewed literature across three databases (MEDLINE, PsycINFO and Embase) for existing systematic reviews and original studies published between 1 January 2009 and 8 August 2019. Fifteen systematic reviews (of which 8 were contemporary) and 64 original publications met the inclusion criteria set across the four questions. Key findings Question 1: What is the evidence for the effectiveness of lung cancer screening for higher-risk individuals? There is sufficient evidence from systematic reviews and meta-analyses of combined (pooled) data from screening trials (of high-risk individuals) to indicate that LDCT examination is clinically effective in reducing lung cancer mortality. In 2011, the landmark National Lung Cancer Screening Trial (NLST, a large-scale randomised controlled trial [RCT] conducted in the US) reported a 20% (95% CI 6.8% – 26.7%; P=0.004) relative reduction in mortality among long-term heavy smokers over three rounds of annual screening. High-risk eligibility criteria was defined as people aged 55–74 years with a smoking history of ≥30 pack-years (years in which a smoker has consumed 20-plus cigarettes each day) and, for former smokers, ≥30 pack-years and have quit within the past 15 years.(5) All-cause mortality was reduced by 6.7% (95% CI, 1.2% – 13.6%; P=0.02). Initial data from the second landmark RCT, the NEderlands-Leuvens Longkanker Screenings ONderzoek (known as the NELSON trial), have found an even greater reduction of 26% (95% CI, 9% – 41%) in lung cancer mortality, with full trial results yet to be published.(6, 7) Pooled analyses, including several smaller-scale European LDCT screening trials insufficiently powered in their own right, collectively demonstrate a statistically significant reduction in lung cancer mortality (RR 0.82, 95% CI 0.73–0.91).(8) Despite the reduction in all-cause mortality found in the NLST, pooled analyses of seven trials found no statistically significant difference in all-cause mortality (RR 0.95, 95% CI 0.90–1.00).(8) However, cancer-specific mortality is currently the most relevant outcome in cancer screening trials. These seven trials demonstrated a significantly greater proportion of early stage cancers in LDCT groups compared with controls (RR 2.08, 95% CI 1.43–3.03). Thus, when considering results across mortality outcomes and early stage cancers diagnosed, LDCT screening is considered to be clinically effective. Question 2: What is the evidence of potential harms from lung cancer screening for higher-risk individuals? The harms of LDCT lung cancer screening include false positive tests and the consequences of unnecessary invasive follow-up procedures for conditions that are eventually diagnosed as benign. While LDCT screening leads to an increased frequency of invasive procedures, it does not result in greater mortality soon after an invasive procedure (in trial settings when compared with the control arm).(8) Overdiagnosis, exposure to radiation, psychological distress and an impact on quality of life are other known harms. Systematic review evidence indicates the benefits of LDCT screening are likely to outweigh the harms. The potential harms are likely to be reduced as refinements are made to LDCT screening protocols through: i) the application of risk predication models (e.g. the PLCOm2012), which enable a more accurate selection of the high-risk population through the use of specific criteria (beyond age and smoking history); ii) the use of nodule management algorithms (e.g. Lung-RADS, PanCan), which assist in the diagnostic evaluation of screen-detected nodules and cancers (e.g. more precise volumetric assessment of nodules); and, iii) more judicious selection of patients for invasive procedures. Recent evidence suggests a positive LDCT result may transiently increase psychological distress but does not have long-term adverse effects on psychological distress or health-related quality of life (HRQoL). With regards to smoking cessation, there is no evidence to suggest screening participation invokes a false sense of assurance in smokers, nor a reduction in motivation to quit. The NELSON and Danish trials found no difference in smoking cessation rates between LDCT screening and control groups. Higher net cessation rates, compared with general population, suggest those who participate in screening trials may already be motivated to quit. Question 3: What are the main components of recent major lung cancer screening programs or trials? There are no systematic reviews that capture the main components of recent major lung cancer screening trials and programs. We extracted evidence from original studies and clinical guidance documents and organised this into key groups to form a concise set of components for potential implementation of a national lung cancer screening program in Australia: 1. Identifying the high-risk population: recruitment, eligibility, selection and referral 2. Educating the public, people at high risk and healthcare providers; this includes creating awareness of lung cancer, the benefits and harms of LDCT screening, and shared decision-making 3. Components necessary for health services to deliver a screening program: a. Planning phase: e.g. human resources to coordinate the program, electronic data systems that integrate medical records information and link to an established national registry b. Implementation phase: e.g. human and technological resources required to conduct LDCT examinations, interpretation of reports and communication of results to participants c. Monitoring and evaluation phase: e.g. monitoring outcomes across patients, radiological reporting, compliance with established standards and a quality assurance program 4. Data reporting and research, e.g. audit and feedback to multidisciplinary teams, reporting outcomes to enhance international research into LDCT screening 5. Incorporation of smoking cessation interventions, e.g. specific programs designed for LDCT screening or referral to existing community or hospital-based services that deliver cessation interventions. Most original studies are single-institution evaluations that contain descriptive data about the processes required to establish and implement a high-risk population-based screening program. Across all studies there is a consistent message as to the challenges and complexities of establishing LDCT screening programs to attract people at high risk who will receive the greatest benefits from participation. With regards to smoking cessation, evidence from one systematic review indicates the optimal strategy for incorporating smoking cessation interventions into a LDCT screening program is unclear. There is widespread agreement that LDCT screening attendance presents a ‘teachable moment’ for cessation advice, especially among those people who receive a positive scan result. Smoking cessation is an area of significant research investment; for instance, eight US-based clinical trials are now underway that aim to address how best to design and deliver cessation programs within large-scale LDCT screening programs.(9) Question 4: What is the cost-effectiveness of lung cancer screening programs (include studies of cost–utility)? Assessing the value or cost-effectiveness of LDCT screening involves a complex interplay of factors including data on effectiveness and costs, and institutional context. A key input is data about the effectiveness of potential and current screening programs with respect to case detection, and the likely outcomes of treating those cases sooner (in the presence of LDCT screening) as opposed to later (in the absence of LDCT screening). Evidence about the cost-effectiveness of LDCT screening programs has been summarised in two systematic reviews. We identified a further 13 studies—five modelling studies, one discrete choice experiment and seven articles—that used a variety of methods to assess cost-effectiveness. Three modelling studies indicated LDCT screening was cost-effective in the settings of the US and Europe. Two studies—one from Australia and one from New Zealand—reported LDCT screening would not be cost-effective using NLST-like protocols. We anticipate that, following the full publication of the NELSON trial, cost-effectiveness studies will likely be updated with new data that reduce uncertainty about factors that influence modelling outcomes, including the findings of indeterminate nodules. Gaps in the evidence There is a large and accessible body of evidence as to the effectiveness (Q1) and harms (Q2) of LDCT screening for lung cancer. Nevertheless, there are significant gaps in the evidence about the program components that are required to implement an effective LDCT screening program (Q3). Questions about LDCT screening acceptability and feasibility were not explicitly included in the scope. However, as the evidence is based primarily on US programs and UK pilot studies, the relevance to the local setting requires careful consideration. The Queensland Lung Cancer Screening Study provides feasibility data about clinical aspects of LDCT screening but little about program design. The International Lung Screening Trial is still in the recruitment phase and findings are not yet available for inclusion in this Evidence Check. The Australian Population Based Screening Framework was developed to “inform decision-makers on the key issues to be considered when assessing potential screening programs in Australia”.(10) As the Framework is specific to population-based, rather than high-risk, screening programs, there is a lack of clarity about transferability of criteria. However, the Framework criteria do stipulate that a screening program must be acceptable to “important subgroups such as target participants who are from culturally and linguistically diverse backgrounds, Aboriginal and Torres Strait Islander people, people from disadvantaged groups and people with a disability”.(10) An extensive search of the literature highlighted that there is very little information about the acceptability of LDCT screening to these population groups in Australia. Yet they are part of the high-risk population.(10) There are also considerable gaps in the evidence about the cost-effectiveness of LDCT screening in different settings, including Australia. The evidence base in this area is rapidly evolving and is likely to include new data from the NELSON trial and incorporate data about the costs of targeted- and immuno-therapies as these treatments become more widely available in Australia.
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Peterson, Bradley S., Joey Trampush, Margaret Maglione, et al. ADHD Diagnosis and Treatment in Children and Adolescents. Agency for Healthcare Research and Quality (AHRQ), 2024. http://dx.doi.org/10.23970/ahrqepccer267.
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Objective. The systematic review assessed evidence on the diagnosis, treatment, and monitoring of attention deficit hyperactivity disorder (ADHD) in children and adolescents to inform a planned update of the American Academy of Pediatrics (AAP) guidelines. Data sources. We searched PubMed®, Embase®, PsycINFO®, ERIC, clinicaltrials.gov, and prior reviews for primary studies published since 1980. The report includes studies published to June 15, 2023. Review methods. The review followed a detailed protocol and was supported by a Technical Expert Panel. Citation screening was facilitated by machine learning; two independent reviewers screened full text citations for eligibility. We abstracted data using software designed for systematic reviews. Risk of bias assessments focused on key sources of bias for diagnostic and intervention studies. We conducted strength of evidence (SoE) and applicability assessments for key outcomes. The protocol for the review has been registered in PROSPERO (CRD42022312656). Results. Searches identified 23,139 citations, and 7,534 were obtained as full text. We included 550 studies reported in 1,097 publications (231 studies addressed diagnosis, 312 studies addressed treatment, and 10 studies addressed monitoring). Diagnostic studies reported on the diagnostic performance of numerous parental ratings, teacher rating scales, teen/child self-reports, clinician tools, neuropsychological tests, EEG approaches, imaging, and biomarkers. Multiple approaches showed promising diagnostic performance (e.g., using parental rating scales), although estimates of performance varied considerably across studies and the SoE was generally low. Few studies reported estimates for children under the age of 7. Treatment studies evaluated combined pharmacological and behavior approaches, medication approved by the Food and Drug Administration, other pharmacologic treatment, psychological/behavioral approaches, cognitive training, neurofeedback, neurostimulation, physical exercise, nutrition and supplements, integrative medicine, parent support, school interventions, and provider or model-of-care interventions. Medication treatment was associated with improved broadband scale scores and ADHD symptoms (high SoE) as well as function (moderate SoE), but also appetite suppression and adverse events (high SoE). Psychosocial interventions also showed improvement in ADHD symptoms based on moderate SoE. Few studies have evaluated combinations of pharmacological and youth-directed psychosocial interventions, and we did not find combinations that were systematically superior to monotherapy (low SoE). Published monitoring approaches for ADHD were limited and the SoE is insufficient. Conclusion. Many diagnostic tools are available to aid the diagnosis of ADHD, but few monitoring strategies have been studied. Medication therapies remain important treatment options, although with a risk of side effects, as the evidence base for psychosocial therapies strengthens and other nondrug treatment approaches emerge.
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Gong, Xuan, Zhou Chen, Kui Yang, et al. Endoscopic Transsphenoidal Surgery for Infra-Diaphragmatic Craniopharyngiomas: Impact of Diaphragm Sellae Competence on Hypothalamic Injury. International Journal of Surgery, 2024. http://dx.doi.org/10.60122/j.ijs.2024.20.03.
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Objective: Investigate the impact of diaphragm sellae competence on surgical outcomes and risk factors for postoperative hypothalamic injury (HI) in patients undergoing endoscopic transsphenoidal surgery (ETS) for infra-diaphragmatic craniopharyngiomas (ICs). Methods: A retrospective analysis of 54 consecutive patients (2016-2023) with ICs treated by ETS was conducted. All tumors originated from the sellar region inferior to the diaphragm sellae and were classified into two subtypes in terms of diaphragm sellae competence: IC with competent diaphragm sellae (IC-CDS) and IC with incompetent diaphragm sellae (IC-IDS). Clinical features, intraoperative findings, and follow-up data were compared between subtypes. Postoperative HI was assessed using a magnetic resonance imaging-based scoring system. Results: Fifty-four patients (29 males, 25 females) were included in this study, with 12 (22.2%) under 18 years old. Overall, 35 cases were IC-CDS, while 19 were IC-IDS. Compared with IC-CDS, patients with IC-IDS tended to have hormone hypofunction before surgery (p = 0.03). Tumor volume in IC-IDS group (9.0 ± 8.6 cm3) was also higher than that in IC-CDS group (3.3 ±3.4 cm, p = 0.011). Thirty-seven patients underwent standard endoscopic transsphenoidal approach (SEA) and 17 underwent an extended endoscopic transsphenoidal approach (EEA). Gross total resection (GTR) was achieved in 50 cases (92.6%). Postoperative CSF leak was observed in four patients (7.4%). Permanent diabetes insipidus (DI) occurred in 13 patients (27.7%), six in IC-CDS and seven in IC-IDS. Postoperative HI occurred in 38.9% of patients. Univariate analysis revealed that large tumor size (p = 0.014), prior hypopituitarism (p = 0.048) and IC-IDS (p < 0.001) were significantly associated with postoperative HI. Multivariate analysis revealed that IC- IDS was the sole predictor of postoperative HI. Conclusion: To our knowledge, this is the largest case series in the literature to describe IC resected by endoscopic surgery in a single institution. Classification based on diaphragm sellae competence highlights distinct clinical features and surgical outcomes between IC-CDS and IC-IDS subtypes. Notably, IC-IDS is an independent risk factor for postoperative HI. Preoperative identification of subtype can guide surgical strategy and potentially minimize complications.