Multimodality monitoring: informatics, integration data display and analysis

Chinese experts' consensus on the application of intensive care big data

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

Prolonged Automated Robotic TCD Monitoring in Acute Severe TBI: Study Design and Rationale

BACKGROUND

Transcranial Doppler ultrasonography (TCD) is a portable, bedside, noninvasive diagnostic tool used for the real-time assessment of cerebral hemodynamics. Despite the evident utility of TCD and the ability of this technique to function as a stethoscope to the brain, its use has been limited to specialized centers because of the dearth of technical and clinical expertise required to acquire and interpret the cerebrovascular parameters. Additionally, the conventional pragmatic episodic TCD monitoring protocols lack dynamic real-time feedback to guide time-critical clinical interventions. Fortunately, with the recent advent of automated robotic TCD technology in conjunction with the automated software for TCD data processing, we now have the technology to automatically acquire TCD data and obtain clinically relevant information in real-time. By obviating the need for highly trained clinical personnel, this technology shows great promise toward a future of widespread noninvasive monitoring to guide clinical care in patients with acute brain injury.

METHODS

Here, we describe a proposal for a prospective observational multicenter clinical trial to evaluate the safety and feasibility of prolonged automated robotic TCD monitoring in patients with severe acute traumatic brain injury (TBI). We will enroll patients with severe non-penetrating TBI with concomitant invasive multimodal monitoring including, intracranial pressure, brain tissue oxygenation, and brain temperature monitoring as part of standard of care in centers with varying degrees of TCD availability and experience. Additionally, we propose to evaluate the correlation of pertinent TCD-based cerebral autoregulation indices such as the critical closing pressure, and the pressure reactivity index with the brain tissue oxygenation values obtained invasively.

CONCLUSIONS

The overarching goal of this study is to establish safety and feasibility of prolonged automated TCD monitoring for patients with TBI in the intensive care unit and identify clinically meaningful and pragmatic noninvasive targets for future interventions.

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.

How do we identify the crashing traumatic brain injury patient - the intensivist's view

PURPOSE OF REVIEW

Over 40% of patients with severe traumatic brain injury (TBI) show clinically significant neurological worsening within the acute admission period. This review addresses the importance of identifying the crashing TBI patient, the difficulties appreciating clinical neurological deterioration in the comatose patient and how neuromonitoring may provide continuous real-time ancillary information to detect physiologic worsening.

RECENT FINDINGS

The latest editions of the Brain Trauma Foundation's Guidelines omitted management algorithms for adult patients with severe TBI. Subsequently, three consensus-based management algorithms were published using a Delphi method approach to provide a bridge between the evidence-based guidelines and integration of the individual treatment modalities at the bedside. These consensus statements highlight the serious situation of critical deterioration requiring emergent evaluation and guidance on sedation holds to obtain a neurological examination while balancing the potential risks of inducing a stress response.

SUMMARY

One of the central tenets of neurocritical care is to detect the brain in trouble. The first and most fundamental neurological monitoring tool is the clinical exam. Ancillary neuromonitoring data may provide early physiologic biomarkers to help anticipate, prevent or halt secondary brain injury processes. Future research should seek to understand how data integration and visualization technologies may reduce the cognitive workload to improve timely detection of neurological deterioration.

Novel displays of patient information in critical care settings: a systematic review

OBJECTIVE

Clinician information overload is prevalent in critical care settings. Improved visualization of patient information may help clinicians cope with information overload, increase efficiency, and improve quality. We compared the effect of information display interventions with usual care on patient care outcomes.

MATERIALS AND METHODS

We conducted a systematic review including experimental and quasi-experimental studies of information display interventions conducted in critical care and anesthesiology settings. Citations from January 1990 to June 2018 were searched in PubMed and IEEE Xplore. Reviewers worked independently to screen articles, evaluate quality, and abstract primary outcomes and display features.

RESULTS

Of 6742 studies identified, 22 studies evaluating 17 information displays met the study inclusion criteria. Information display categories included comprehensive integrated displays (3 displays), multipatient dashboards (7 displays), physiologic and laboratory monitoring (5 displays), and expert systems (2 displays). Significant improvement on primary outcomes over usual care was reported in 12 studies for 9 unique displays. Improvement was found mostly with comprehensive integrated displays (4 of 6 studies) and multipatient dashboards (5 of 7 studies). Only 1 of 5 randomized controlled trials had a positive effect in the primary outcome.

CONCLUSION

We found weak evidence suggesting comprehensive integrated displays improve provider efficiency and process outcomes, and multipatient dashboards improve compliance with care protocols and patient outcomes. Randomized controlled trials of physiologic and laboratory monitoring displays did not show improvement in primary outcomes, despite positive results in simulated settings. Important research translation gaps from laboratory to actual critical care settings exist.

Big data in status epilepticus

Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

Critical care information display approaches and design frameworks: A systematic review and meta-analysis

OBJECTIVE

To systematically review original user evaluations of patient information displays relevant to critical care and understand the impact of design frameworks and information presentation approaches on decision-making, efficiency, workload, and preferences of clinicians.

METHODS

We included studies that evaluated information displays designed to support real-time care decisions in critical care or anesthesiology using simulated tasks. We searched PubMed and IEEExplore from 1/1/1990 to 6/30/2018. The search strategy was developed iteratively with calibration against known references. Inclusion screening was completed independently by two authors. Extraction of display features, design processes, and evaluation method was completed by one and verified by a second author.

RESULTS

Fifty-six manuscripts evaluating 32 critical care and 22 anesthesia displays were included. Primary outcome metrics included clinician accuracy and efficiency in recognizing, diagnosing, and treating problems. Implementing user-centered design (UCD) processes, especially iterative evaluation and redesign, resulted in positive impact in outcomes such as accuracy and efficiency. Innovative display approaches that led to improved human-system performance in critical care included: (1) improving the integration and organization of information, (2) improving the representation of trend information, and (3) implementing graphical approaches to make relationships between data visible.

CONCLUSION

Our review affirms the value of key principles of UCD. Improved information presentation can facilitate faster information interpretation and more accurate diagnoses and treatment. Improvements to information organization and support for rapid interpretation of time-based relationships between related quantitative data is warranted. Designers and developers are encouraged to involve users in formal iterative design and evaluation activities in the design of electronic health records (EHRs), clinical informatics applications, and clinical devices.

Implementation of a Data Acquisition and Integration Device in the Neurologic Intensive Care Unit

The neurologic intensive care unit has evolved into a data-rich, complex arena. Various neurologic monitors, collectively referred to as multimodality monitoring, provide clinicians with a plethora of real-time information about a comatose patient's condition. The time and cognitive burden required to synthesize the available data and reach meaningful clinical conclusions can be overwhelming. The Moberg Component Neuromonitoring System (Moberg Research, Inc) is a data acquisition and integration device that collects data from multiple monitors, displaying them on a single screen in a way that highlights physiological trends throughout a patient's clinical course. Implementation of the Moberg Component Neuromonitoring System in the neurologic intensive care unit can improve understanding of a patient's neurophysiology, enhance clinical decision-making, and improve quality of care. Use of a staged process of implementation including exploration, installation, initial implementation, and full implementation can bring technology to the bedside in a sustainable fashion.

Multimodality Monitoring in the Neurocritical Care Unit

PURPOSE OF REVIEW

This article focuses on the multiple neuromonitoring devices that can be used to collect bedside data in the neurocritical care unit and the methodology to integrate them into a multimodality monitoring system. The article describes how to apply the collected data to appreciate the physiologic changes and develop therapeutic approaches to prevent secondary injury.

RECENT FINDINGS

The neurologic examination has served as the primary monitor for secondary brain injury in patients admitted to the neurocritical care unit. However, the International Multidisciplinary Consensus Conference on Multimodality Monitoring in Neurocritical Care concluded that frequent bedside examinations are not sufficient to detect and prevent secondary brain injury and that integration of multimodality monitoring with advanced informatics tools will most likely enhance our assessments compared to the clinical examinations alone. This article reviews the invasive and noninvasive technologies used to monitor focal and global neurophysiologic cerebral alterations.

SUMMARY

Multimodal monitoring is still in the early stages of development. Research is still needed to establish more advanced monitors with the bioinformatics to identify useful trends from data gathered to predict clinical outcome or prevent secondary brain injury.

"Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology"

Background

Intensive care clinicians use several sources of data in order to inform decision-making. We set out to evaluate a new interactive data integration platform called T3™ made available for pediatric intensive care. Three primary functions are supported: tracking of physiologic signals, displaying trajectory, and triggering decisions, by highlighting data or estimating risk of patient instability. We designed a human factors study to identify interface usability issues, to measure ease of use, and to describe interface features that may enable or hinder clinical tasks.

Methods

Twenty-two participants, consisting of bedside intensive care physicians, nurses, and respiratory therapists, tested the T3™ interface in a simulation laboratory setting. Twenty tasks were performed with a true-to-setting, fully functional, prototype, populated with physiological and therapeutic intervention patient data. Primary data visualization was time series and secondary visualizations were: 1) shading out-of-target values, 2) mini-trends with exaggerated maxima and minima (sparklines), and 3) bar graph of a 16-parameter indicator. Task completion was video recorded and assessed using a use error rating scale. Usability issues were classified in the context of task and type of clinician. A severity rating scale was used to rate potential clinical impact of usability issues.

Results

Time series supported tracking a single parameter but partially supported determining patient trajectory using multiple parameters. Visual pattern overload was observed with multiple parameter data streams. Automated data processing using shading and sparklines was often ignored but the 16-parameter data reduction algorithm, displayed as a persistent bar graph, was visually intuitive. However, by selecting or automatically processing data, triggering aids distorted the raw data that clinicians use regularly. Consequently, clinicians could not rely on new data representations because they did not know how they were established or derived.

Conclusions

Usability issues, observed through contextual use, provided directions for tangible design improvements of data integration software that may lessen use errors and promote safe use. Data-driven decision making can benefit from iterative interface redesign involving clinician-users in simulated environments. This study is a first step in understanding how software can support clinicians’ decision making with integrated continuous monitoring data. Importantly, testing of similar platforms by all the different disciplines who may become clinician users is a fundamental step necessary to understand the impact on clinical outcomes of decision aids.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0520-7) contains supplementary material, which is available to authorized users.

Informatics in neurocritical care: new ideas for Big Data

PURPOSE OF REVIEW

Big data is the new hype in business and healthcare. Data storage and processing has become cheap, fast, and easy. Business analysts and scientists are trying to design methods to mine these data for hidden knowledge. Neurocritical care is a field that typically produces large amounts of patient-related data, and these data are increasingly being digitized and stored. This review will try to look beyond the hype, and focus on possible applications in neurointensive care amenable to Big Data research that can potentially improve patient care.

RECENT FINDINGS

The first challenge in Big Data research will be the development of large, multicenter, and high-quality databases. These databases could be used to further investigate recent findings from mathematical models, developed in smaller datasets. Randomized clinical trials and Big Data research are complementary. Big Data research might be used to identify subgroups of patients that could benefit most from a certain intervention, or can be an alternative in areas where randomized clinical trials are not possible.

SUMMARY

The processing and the analysis of the large amount of patient-related information stored in clinical databases is beyond normal human cognitive ability. Big Data research applications have the potential to discover new medical knowledge, and improve care in the neurointensive care unit.

Computerized Decision Support System for Traumatic Brain Injury Management

Mortality and morbidity related to traumatic brain injury (TBI) present a major health care burden. Patients with severe TBI must be managed rapidly and efficiently to minimize secondary brain injury potentially leading to permanent sequelae. This is especially important in young patients, whose brain is still in development, making them particularly susceptible to secondary insults. The complexity of both brain injury pathophysiology and the intensive care unit environment makes the management of these patients challenging, with a risk of delayed response and/or patient instability contributing to worsened outcome. Computerized assistance in TBI appears likely to improve patient management, by helping clinicians quickly analyze and respond to ongoing clinical changes and optimizing patient status by guiding management. Currently, computerized decision support systems (CDSSs) do not feature continuous medical assistance with individualized treatment plans. This review presents new developments in CDSSs specialized in TBI. We also present the framework for future CDSSs needed to improve TBI management in real time, taking into account individual patient characteristics.

Brain Multimodality Monitoring: Updated Perspectives

The challenges posed by acute brain injury (ABI) involve the management of the initial insult in addition to downstream inflammation, edema, and ischemia that can result in secondary brain injury (SBI). SBI is often subclinical, but can be detected through physiologic changes. These changes serve as a surrogate for tissue injury/cell death and are captured by parameters measured by various monitors that measure intracranial pressure (ICP), cerebral blood flow (CBF), brain tissue oxygenation (PbtO2), cerebral metabolism, and electrocortical activity. In the ideal setting, multimodality monitoring (MMM) integrates these neurological monitoring parameters with traditional hemodynamic monitoring and the physical exam, presenting the information needed to clinicians who can intervene before irreversible damage occurs. There are now consensus guidelines on the utilization of MMM, and there continue to be new advances and questions regarding its use. In this review, we examine these recommendations, recent evidence for MMM, and future directions for MMM.