Assessing the oral and suprahyoid muscles in healthy adults using muscle ultrasound to inform the swallowing process: a proof-of-concept study.

The oral and suprahyoid muscles are responsible for movements of swallowing. Our study aimed to determine the reproducibility of static and dynamic measurements of these muscles using bedside ultrasound equipment. Forty healthy participants were recruited prospectively. Primary outcomes were evaluation of mass measurements of the anterior bellies of the digastric, mylohyoid, geniohyoid and tongue in B-mode ultrasound. Secondary outcomes were evaluation of geniohyoid muscle layer thickness and function using M-mode. Muscle mass measurements demonstrated little within-participant variability. Coefficient of Variance (CoV) across muscles were: anterior belly digastric (5.0%), mylohyoid (8.7%), geniohyoid (5.0%) and tongue (3.2%). A relationship between sex (r2 = 0.131 p = 0.022) was demonstrated for the geniohyoid muscle, with males having higher transverse Cross Sectional Area (CSA) (14.3 ± 3.6 mm vs. 11.9 ± 2.5 mm, p = 0.002). Tongue size was correlated with weight (r2 = 0.356, p = 0.001), height (r2 = 0.156, p = 0.012) and sex (r2 = 0.196, p = 0.004). Resting thickness of the geniohyoid muscle layer changed with increasing bolus sizes (f = 3.898, p = 0.026). Velocity increased with bolus size (p = < 0.001, F = 8.974). However swallow time and slope distance did not, potentially influenced by higher coefficients of variation. Oral and suprahyoid muscle mass are easily assessed using bedside ultrasound. Ultrasound may provide new information about muscle mass and function during swallowing.

Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility.

OBJECTIVES: Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility. MATERIALS AND METHODS: We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts. RESULTS: EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science. DISCUSSION: As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied. CONCLUSION: This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.

Linking Patient Encounters across Primary and Ancillary Electronic Health Record Systems: A Comparison of Two Approaches.

Background: To achieve scientific goals, researchers often require integration of data from a primary electronic health record (EHR) system and one or more ancillary EHR systems used during the same patient care encounter. Although studies have demonstrated approaches for linking patient identity records across different EHR systems, little is known about linking patient encounter records across primary and ancillary EHR systems. Objectives: We compared a patients-first approach versus an encounters-first approach for linking patient encounter records across multiple EHR systems. Methods: We conducted a retrospective observational study of 348,904 patients with 533,283 encounters from 2010 to 2020 across our institution's primary EHR system and an ancillary EHR system used in perioperative settings. For the patients-first approach and the encounters-first approach, we measured the number of patient and encounter links created as well as runtime. Results: While the patients-first approach linked 43% of patients and 49% of encounters, the encounters-first approach linked 98% of patients and 100% of encounters. The encounters-first approach was 20 times faster than the patients-first approach for linking patients and 33% slower for linking encounters. Conclusion: Findings suggest that common patient and encounter identifiers shared among EHR systems via automated interfaces may be clinically useful but not "research-ready" and thus require an encounters-first linkage approach to enable secondary use for scientific purposes. Based on our search, this study is among the first to demonstrate approaches for linking patient encounters across multiple EHR systems. Enterprise data warehouse for research efforts elsewhere may benefit from an encounters-first approach.

Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging.

PURPOSE: Artificial intelligence (AI) in the form of automated machine learning (AutoML) offers a new potential breakthrough to overcome the barrier of entry for non-technically trained physicians. A Clinical Decision Support System (CDSS) for screening purposes using AutoML could be beneficial to ease the clinical burden in the radiological workflow for paranasal sinus diseases. METHODS: The main target of this work was the usage of automated evaluation of model performance and the feasibility of the Vertex AI image classification model on the Google Cloud AutoML platform to be trained to automatically classify the presence or absence of sinonasal disease. The dataset is a consensus labelled Open Access Series of Imaging Studies (OASIS-3) MRI head dataset by three specialised head and neck consultant radiologists. A total of 1313 unique non-TSE T2w MRI head sessions were used from the OASIS-3 repository. RESULTS: The best-performing image classification model achieved a precision of 0.928. Demonstrating the feasibility and high performance of the Vertex AI image classification model to automatically detect the presence or absence of sinonasal disease on MRI. CONCLUSION: AutoML allows for potential deployment to optimise diagnostic radiology workflows and lay the foundation for further AI research in radiology and otolaryngology. The usage of AutoML could serve as a formal requirement for a feasibility study.

STAT3 protects dopaminergic neurons against degeneration in animal model of Parkinson's disease.

INTRODUCTION: Parkinson's disease (PD) is the most prevalent disorder of the basal ganglia, propagated by the degeneration of axon terminals within the striatum and subsequent loss of dopaminergic neurons in the substantia nigra (SN). Exposure of environmental neurotoxins and mutations of several mitochondrial and proteasomal genes are primarily responsible. METHODS: To determine whether signal transducer and activator of transcription 3 (STAT3) could protect dopaminergic neurons against degeneration, we first screened it in the in vitro capacity using immortalized rat dopaminergic N27 cells under 6-OHDA neurotoxicity. We then evaluated the effectiveness of constitutively active (ca) STAT3 as a neuroprotective agent on N27 cells in a 6-hydroxydopamine (6-OHDA) induced rat model of PD and compared it to control animals or animals where AAV/caRheb was expressed in SN. Behavioral outcomes were assessed using rotational and cylinder assays and mitochondrial function using reactive oxygen species (ROS) levels. RESULTS: Using flow cytometry, the in vitro analysis determined caSTAT3 significantly decreased dopaminergic neuronal death under 6-OHDA treatment conditions. Importantly, in vivo overexpression of caSTAT3 in SN dopaminergic neurons using AAV-mediated expression demonstrated significant neuroprotection of dopaminergic neurons following 6-OHDA. Both caSTAT3 and caRheb + caSTAT3 co-injection into substantia nigra reduced D-amphetamine-induced rotational behavior and increased ipsilateral forelimb function when compared to control animals. In addition, caSTAT3 decreased mitochondrial ROS production following 6-OHDA induced neurotoxicity. CONCLUSION: caSTAT3 confers resistance against ROS production in mitochondria of susceptible SN dopaminergic neurons potentially offering a new avenue for treatment against PD.

Implementation of a commercial federated network of electronic health record data to enable sponsor-initiated clinical trials at an academic medical center.

BACKGROUND: A commercial federated network called TriNetX has connected electronic health record (EHR) data from academic medical centers (AMCs) with biopharmaceutical sponsors in a privacy-preserving manner to promote sponsor-initiated clinical trials. Little is known about how AMCs have implemented TriNetX to support clinical trials. FINDINGS: At our AMC over a six-year period, TriNetX integrated into existing institutional workflows enabled 402 requests for sponsor-initiated clinical trials, 14 % (n = 56) of which local investigators expressed interest in conducting. Although clinical trials administrators indicated TriNetX yielded unique study opportunities, measurement of impact of institutional participation in the network was challenging due to lack of a common trial identifier shared across TriNetX, sponsor, and our institution. CONCLUSION: To the best of our knowledge, this study is among the first to describe integration of a federated network of EHR data into institutional workflows for sponsor-initiated clinical trials. This case report may inform efforts at other institutions.

Disease activity 4.5 years after starting cladribine: experience in 264 patients with multiple sclerosis.

Background: Cladribine is an effective immunotherapy for people with multiple sclerosis (pwMS). Whilst most pwMS do not require re-treatment following standard dosing (two treatment courses), disease activity re-emerges in others. The characteristics of pwMS developing re-emerging disease activity remain incompletely understood. Objectives: To explore whether clinical and/or paraclinical baseline characteristics, including the degree of lymphocyte reduction, drug dose and lesions on magnetic resonance imaging (MRI) are associated with re-emerging disease activity. Design: Service evaluation in pwMS undergoing subcutaneous cladribine (SClad) treatment. Methods: Demographics, clinical, laboratory and MRI data of pwMS receiving two courses of SClad were extracted from health records. To assess associations of predictor variables with re-emerging disease activity, a series of Cox proportional hazards models was fitted (one for each predictor variable). Results: Of n = 264 pwMS 236 received two courses of SClad and were included in the analysis. Median follow-up was 4.5 years (3.9, 5.3) from the first, and 3.5 years (2.9, 4.3) from the last SClad administration. Re-emerging disease activity occurred in 57/236 pwMS (24%); 22/236 received further cladribine doses (SClad or cladribine tablets) at 36.7 months [median; interquartile range (IQR): 31.7, 42.1], and 22/236 other immunotherapies 18.9 months (13.0, 30.2) after their second course of SClad, respectively. Eligibility was based on MRI activity in 29, relapse in 5, both in 13, elevated cerebrospinal fluid neurofilament light chain level in 3, deterioration unrelated to relapse in 4 and other in 3. Only 36/57 of those eligible for additional immunotherapy had received a reduced dose of SClad for their second treatment course. Association was detected between re-emerging disease activity and (i) high baseline MRI activity and (ii) low second dose of SClad. Conclusion: Re-emerging disease activity was associated with baseline MRI activity and low dose second course of SClad.

Parakinesia Brachialis Oscitans in a Patient With a First Manifestation of Multiple Sclerosis.

Objectives: Parakinesia brachialis oscitans (PBO) is the involuntary movement of an otherwise paretic upper limb triggered by yawning. We describe the first case of PBO in a patient with a first manifestation of tumefactive multiple sclerosis (MS). Methods: A 35-year-old man presented to the emergency department with a first episode of generalized seizure. Neurologic examination revealed left-sided spastic hemiparesis, predominantly affecting his upper limb. Brain MRI showed a tumefactive right hemisphere lesion consistent with demyelination. CSF did not document unmatched oligoclonal bands. Results: Two weeks after admission and, despite being unable to voluntarily raise his left arm, the patient noticed a repeated and reproducible involuntary raise of this limb upon yawning, consistent with PBO. In the following weeks, the phenomenon diminished both in frequency and movement amplitude alongside motor recovery. An MRI performed 2 months later showed progression of the demyelinating lesion load and confirmed a diagnosis of MS. Discussion: PBO is an example of autonomic voluntary motor dissociation and reflects the interplay between loss of cortical inhibition of the cerebellum in the setting of functional spinocerebellar pathways. Clinicians should be aware of this transient phenomenon which should not be mistaken as a chronic movement disorder or focal epileptic seizures.

A method to automate the discharge summary hospital course for neurology patients.

OBJECTIVE: Generation of automated clinical notes has been posited as a strategy to mitigate physician burnout. In particular, an automated narrative summary of a patient's hospital stay could supplement the hospital course section of the discharge summary that inpatient physicians document in electronic health record (EHR) systems. In the current study, we developed and evaluated an automated method for summarizing the hospital course section using encoder-decoder sequence-to-sequence transformer models. MATERIALS AND METHODS: We fine-tuned BERT and BART models and optimized for factuality through constraining beam search, which we trained and tested using EHR data from patients admitted to the neurology unit of an academic medical center. RESULTS: The approach demonstrated good ROUGE scores with an R-2 of 13.76. In a blind evaluation, 2 board-certified physicians rated 62% of the automated summaries as meeting the standard of care, which suggests the method may be useful clinically. DISCUSSION AND CONCLUSION: To our knowledge, this study is among the first to demonstrate an automated method for generating a discharge summary hospital course that approaches a quality level of what a physician would write.

Maturity in enterprise data warehouses for research operations: Analysis of a pilot study.

Enterprise data warehouses for research (EDW4R) is a critical component of National Institutes of Health Clinical and Translational Science Award (CTSA) hubs. EDW4R operations have unique needs that require specialized skills and collaborations across multiple domains which limit the ability to apply existing models of information technology (IT) performance. Because of this uniqueness, we developed a new EDW4R maturity model based on prior qualitative study of operational practices for supporting EDW4Rs at CTSA hubs. In a pilot study, respondents from fifteen CTSA hubs completed the novel EDW4R maturity index survey by rating 33 maturity statements across 6 categories using a 5-point Likert scale. Of the six categories, respondents rated workforce as most mature (4.17 [3.67-4.42]) and relationship with enterprise IT as the least mature (3.00 [2.80-3.80]). Our pilot of a novel maturity index shows a baseline quantitative measure of EDW4R functions across fifteen CTSA hubs. The maturity index may be useful to faculty and staff currently leading an EDW4R by creating opportunities to explore the index in local context and comparison to other institutions.

A RE-AIM Evaluation of a Visualization-Based Electronic Patient-Reported Outcome System.

OBJECTIVES: Health care systems are primarily collecting patient-reported outcomes (PROs) for research and clinical care using proprietary, institution- and disease-specific tools for remote assessment. The purpose of this study was to conduct a Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) evaluation of a scalable electronic PRO (ePRO) reporting and visualization system in a single-arm study. METHODS: The "mi.symptoms" ePRO system was designed using gerontechnological design principles to ensure high usability among older adults. The system enables longitudinal reporting of disease-agnostic ePROs and includes patient-facing PRO visualizations. We conducted an evaluation of the implementation of the system guided by the RE-AIM framework. Quantitative data were analyzed using basic descriptive statistics, and qualitative data were analyzed using directed content analysis. RESULTS: Reach-the total reach of the study was 70 participants (median age: 69, 31% female, 17% Black or African American, 27% reported not having enough financial resources). Effectiveness-half (51%) of participants completed the 2-week follow-up survey and 36% completed all follow-up surveys. Adoption-the desire for increased self-knowledge, the value of tracking symptoms, and altruism motivated participants to adopt the tool. Implementation-the predisposing factor was access to, and comfort with, computers. Three enabling factors were incorporation into routines, multimodal nudges, and ease of use. Maintenance-reinforcing factors were perceived usefulness of viewing symptom reports with the tool and understanding the value of sustained symptom tracking in general. CONCLUSION: Challenges in ePRO reporting, particularly sustained patient engagement, remain. Nonetheless, freely available, scalable, disease-agnostic systems may pave the road toward inclusion of a more diverse range of health systems and patients in ePRO collection and use.

Comparative Merits of Available Mortality Data Sources for Clinical Research.

Obtaining reliable data on patient mortality is a critical challenge facing observational researchers seeking to conduct studies using real-world data. As these analyses are conducted more broadly using newly-available sources of real-world evidence, missing data can serve as a rate-limiting factor. We conducted a comparison of mortality data sources from different stakeholder perspectives - academic medical center (AMC) informatics service providers, AMC research coordinators, industry analytics professionals, and academics - to understand the strengths and limitations of differing mortality data sources: locally generated data from sites conducting research, data provided by governmental sources, and commercially available data sets. Researchers seeking to conduct observational studies using extant data should consider these factors in sourcing outcomes data for their populations of interest.

Diverse imaging features of adolescent glioblastoma.

We highlight an unusual case of multifocal glioblastoma in an adolescent patient, manifesting as four discrete brain lesions, each distinct in appearance. Familiarity with the diverse imaging features of glioblastoma can reduce misdiagnosis and avoid treatment delays.

Chemogenetic modulation of sensory afferents induces locomotor changes and plasticity after spinal cord injury.

Neuromodulatory therapies for spinal cord injury (SCI) such as electrical epidural stimulation (EES) are increasingly effective at improving patient outcomes. These improvements are thought to be due, at least in part, to plasticity in neuronal circuits. Precisely which circuits are influenced and which afferent classes are most effective in stimulating change remain important open questions. Genetic tools, such as Designer Receptors Exclusively Activated by Designer Drugs (DREADDs), support targeted and reversible neuromodulation as well as histological characterization of manipulated neurons. We therefore transduced and activated lumbar large diameter peripheral afferents with excitatory (hM3Dq) DREADDs, in a manner analogous to EES, in a rat hemisection model, to begin to trace plasticity and observe concomitant locomotor changes. Chronic DREADDs activation, coupled with thrice weekly treadmill training, was observed to increase afferent fluorescent labeling within motor pools and Clarke's column when compared to control animals. This plasticity may underlie kinematic differences that we observed across stages of recovery, including an increased and less variable hindquarters height in DREADDs animals, shorter step durations, a more flexed ankle joint early in recovery, a less variable ankle joint angle in swing phase, but a more variable hip joint angle. Withdrawal of DREADDs agonist, clozapine-N-oxide (CNO) left these kinematic differences largely unaffected; suggesting that DREADDs activation is not necessary for them later in recovery. However, we observed an intermittent "buckling" phenomenon in DREADDs animals without CNO activation, that did not occur with CNO re-administration. Future studies could use more refined genetic targeted of specific afferent classes, and utilize muscle recordings to find where afferent modulation is most influential in altering motor output.

A Day-to-Day Approach for Automating the Hospital Course Section of the Discharge Summary.

Optimal solutions for abstractive summarization of electronic health record content have yet to be discovered. Although studies have applied state-of-the-art transformers in the clinical domain to radiology reports and information extraction, little is known of transformers' performance with the hospital course section of the discharge summary. This paper compares two summarization approaches for automating the hospital course section within the discharge summary: (1) a truncation approach that uses all clinical notes and (2) a day-to-day approach that segments the notes per clinical day. We pair both approaches with different transformer encoder-decoder based-models - BART, BERT2GPT2, ClinicalBERT2GPT2, and ClinicalBERT2ClinicalBERT and evaluate the transformers that work best for each approach using ROUGE metrics. The results demonstrate that the day-to-day approach can overcome the limitations of longform document summarization for the patient clinical record.

Design and implementation of an integrated data model to support clinical and translational research administration.

OBJECTIVE: Both academic medical centers and biomedical research sponsors need to understand impact of scientific funding to determine value. For the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) hubs, tracking research activities can be complex, often involving multiple institutions and continually changing federal reporting requirements. Existing research administrative systems are institution-specific and tend to focus only on parts of a greater whole. The goal of this case report is to describe a comprehensive data model that addresses this gap. MATERIALS AND METHODS: Web-based Center Administrative Management Program (WebCAMP) has been developed over a period of over 15 years in the context of CTSA hubs, with the recent addition of T32 programs. Its data model centers around the key concepts of people, projects, resources (inputs), and outcomes (outputs). RESULTS: The WebCAMP data model and associated toolset for biomedical research administration integrates multiple components of the research enterprise, has been used by our CTSA hub for over 15 years and has been adopted by more than 20 other CTSA hubs. DISCUSSION: To the best of our knowledge, this study is among the first to describe a comprehensive data model for biomedical research administration. Opportunities for future work include improved grant tracking through the development of a universal identifier that spans public and private funders, and a more generic outcomes tracking model able to rapidly incorporate new outcome types. CONCLUSION: We propose that the WebCAMP data model, or a derivative of it, could serve as a future standard for research administrative data warehousing.

Correction: Addition of angled rungs to the horizontal ladder walking task for more sensitive probing of sensorimotor changes.

[This corrects the article DOI: 10.1371/journal.pone.0246298.].

Understanding enterprise data warehouses to support clinical and translational research: enterprise information technology relationships, data governance, workforce, and cloud computing.

OBJECTIVE: Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, effective approaches for enterprise data warehouses for research (EDW4R) development, maintenance, and sustainability remain unclear. The goal of this qualitative study was to understand CTSA EDW4R operations within the broader contexts of academic medical centers and technology. MATERIALS AND METHODS: We performed a directed content analysis of transcripts generated from semistructured interviews with informatics leaders from 20 CTSA hubs. RESULTS: Respondents referred to services provided by health system, university, and medical school information technology (IT) organizations as "enterprise information technology (IT)." Seventy-five percent of respondents stated that the team providing EDW4R service at their hub was separate from enterprise IT; strong relationships between EDW4R teams and enterprise IT were critical for success. Managing challenges of EDW4R staffing was made easier by executive leadership support. Data governance appeared to be a work in progress, as most hubs reported complex and incomplete processes, especially for commercial data sharing. Although nearly all hubs (n = 16) described use of cloud computing for specific projects, only 2 hubs reported using a cloud-based EDW4R. Respondents described EDW4R cloud migration facilitators, barriers, and opportunities. DISCUSSION: Descriptions of approaches to how EDW4R teams at CTSA hubs work with enterprise IT organizations, manage workforces, make decisions about data, and approach cloud computing provide insights for institutions seeking to leverage patient data for research. CONCLUSION: Identification of EDW4R best practices is challenging, and this study helps identify a breadth of viable options for CTSA hubs to consider when implementing EDW4R services.

Assessment of structured data elements for social risk factors.

OBJECTIVES: Computable social risk factor phenotypes derived from routinely collected structured electronic health record (EHR) or health information exchange (HIE) data may represent a feasible and robust approach to measuring social factors. This study convened an expert panel to identify and assess the quality of individual EHR and HIE structured data elements that could be used as components in future computable social risk factor phenotypes. STUDY DESIGN: Technical expert panel. METHODS: A 2-round Delphi technique included 17 experts with an in-depth knowledge of available EHR and/or HIE data. The first-round identification sessions followed a nominal group approach to generate candidate data elements that may relate to socioeconomics, cultural context, social relationships, and community context. In the second-round survey, panelists rated each data element according to overall data quality and likelihood of systematic differences in quality across populations (ie, bias). RESULTS: Panelists identified a total of 89 structured data elements. About half of the data elements (n = 45) were related to socioeconomic characteristics. The panelists identified a diverse set of data elements. Elements used in reimbursement-related processes were generally rated as higher quality. Panelists noted that several data elements may be subject to implicit bias or reflect biased systems of care, which may limit their utility in measuring social factors. CONCLUSIONS: Routinely collected structured data within EHR and HIE systems may reflect patient social risk factors. Identifying and assessing available data elements serves as a foundational step toward developing future computable social factor phenotypes.