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.

Clinical evaluation of automated quantitative MRI reports for assessment of hippocampal sclerosis.

OBJECTIVES: Hippocampal sclerosis (HS) is a common cause of temporal lobe epilepsy. Neuroradiological practice relies on visual assessment, but quantification of HS imaging biomarkers-hippocampal volume loss and T2 elevation-could improve detection. We tested whether quantitative measures, contextualised with normative data, improve rater accuracy and confidence. METHODS: Quantitative reports (QReports) were generated for 43 individuals with epilepsy (mean age ± SD 40.0 ± 14.8 years, 22 men; 15 histologically unilateral HS; 5 bilateral; 23 MR-negative). Normative data was generated from 111 healthy individuals (age 40.0 ± 12.8 years, 52 men). Nine raters with different experience (neuroradiologists, trainees, and image analysts) assessed subjects' imaging with and without QReports. Raters assigned imaging normal, right, left, or bilateral HS. Confidence was rated on a 5-point scale. RESULTS: Correct designation (normal/abnormal) was high and showed further trend-level improvement with QReports, from 87.5 to 92.5% (p = 0.07, effect size d = 0.69). Largest magnitude improvement (84.5 to 93.8%) was for image analysts (d = 0.87). For bilateral HS, QReports significantly improved overall accuracy, from 74.4 to 91.1% (p = 0.042, d = 0.7). Agreement with the correct diagnosis (kappa) tended to increase from 0.74 ('fair') to 0.86 ('excellent') with the report (p = 0.06, d = 0.81). Confidence increased when correctly assessing scans with the QReport (p < 0.001, η2p = 0.945). CONCLUSIONS: QReports of HS imaging biomarkers can improve rater accuracy and confidence, particularly in challenging bilateral cases. Improvements were seen across all raters, with large effect sizes, greatest for image analysts. These findings may have positive implications for clinical radiology services and justify further validation in larger groups. KEY POINTS: • Quantification of imaging biomarkers for hippocampal sclerosis-volume loss and raised T2 signal-could improve clinical radiological detection in challenging cases. • Quantitative reports for individual patients, contextualised with normative reference data, improved diagnostic accuracy and confidence in a group of nine raters, in particular for bilateral HS cases. • We present a pre-use clinical validation of an automated imaging assessment tool to assist clinical radiology reporting of hippocampal sclerosis, which improves detection accuracy.

Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data.

Patients treated in an intensive care unit (ICU) are critically ill and require life-sustaining organ failure support. Existing critical care data resources are limited to a select number of institutions, contain only ICU data, and do not enable the study of local changes in care patterns. To address these limitations, we developed the Critical carE Database for Advanced Research (CEDAR), a method for automating extraction and transformation of data from an electronic health record (EHR) system. Compared to an existing gold standard of manually collected data at our institution, CEDAR was statistically similar in most measures, including patient demographics and sepsis-related organ failure assessment (SOFA) scores. Additionally, CEDAR automated data extraction obviated the need for manual collection of 550 variables. Critically, during the spring 2020 COVID-19 surge in New York City, a modified version of CEDAR supported pandemic response efforts, including clinical operations and research. Other academic medical centers may find value in using the CEDAR method to automate data extraction from EHR systems to support ICU activities.

Extracting and classifying diagnosis dates from clinical notes: A case study.

Myeloproliferative neoplasms (MPNs) are chronic hematologic malignancies that may progress over long disease courses. The original date of diagnosis is an important piece of information for patient care and research, but is not consistently documented. We describe an attempt to build a pipeline for extracting dates with natural language processing (NLP) tools and techniques and classifying them as relevant diagnoses or not. Inaccurate and incomplete date extraction and interpretation impacted the performance of the overall pipeline. Existing lightweight Python packages tended to have low specificity for identifying and interpreting partial and relative dates in clinical text. A rules-based regular expression (regex) approach achieved recall of 83.0% on dates manually annotated as diagnosis dates, and 77.4% on all annotated dates. With only 3.8% of annotated dates representing initial MPN diagnoses, additional methods of targeting candidate date instances may alleviate noise and class imbalance.

A scalable method for supporting multiple patient cohort discovery projects using i2b2.

Although i2b2, a popular platform for patient cohort discovery using electronic health record (EHR) data, can support multiple projects specific to individual disease areas or research interests, the standard approach for doing so duplicates data across projects, requiring additional disk space and processing time, which limits scalability. To address this deficiency, we developed a novel approach that stored data in a single i2b2 fact table and used structured query language (SQL) views to access data for specific projects. Compared to the standard approach, the view-based approach reduced required disk space by 59% and extract-transfer-load (ETL) time by 46%, without substantially impacting query performance. The view-based approach has enabled scalability of multiple i2b2 projects and generalized to another data model at our institution. Other institutions may benefit from this approach, code of which is available on GitHub (https://github.com/wcmc-research-informatics/super-i2b2).

The spectrum of traumatic injuries at the craniocervical junction: a review of imaging findings and management.

The craniocervical junction is an area with unique biomechanical properties and injuries conferred often represent high-impact trauma. The vital structures traversing this region are susceptible to injury with frequent, only subtle findings identified on unenhanced CT, with MRI and CT angiography often revealing the full extent of injuries. This article reviews the osseous and ligamentous anatomy of the region and common injury patterns. Endovascular and neurosurgical management will also be discussed.

Implementing unique device identification in electronic health record systems: organizational, workflow, and technological challenges.

BACKGROUND: The United States Food and Drug Administration (FDA) has proposed creating a unique device identification (UDI) system for medical devices to facilitate postmarket surveillance, quality improvement, and other applications. Although a small number of health care institutions have implemented initiatives comparable with the proposed UDI system by capturing data in electronic health record (EHR) systems, it is unknown whether institutions with fewer resources will be able to similarly implement UDI. OBJECTIVE AND METHODS: This paper calls attention to organizational, workflow, and technological challenges in UDI system implementation by drawing from the literature on EHR and clinical research systems implementation. FINDINGS: Organizational challenges for UDI system implementation include coordinating multiple stakeholders to define UDI attributes and characteristics for use in EHRs, guiding organizational change within individual institutions for integrating UDI with EHRs, and guiding organizational change for reusing UDI data captured in EHRs. Workflow challenges include capturing UDI data in EHRs using keyboard entry and barcode scanning. Technological challenges involve interfacing UDI data between EHRs and surgical information systems, transforming UDI and related patient data from EHRs for research, and applying data standards to UDI within and beyond EHRs. DISCUSSION AND CONCLUSIONS: We provide recommendations for regulations, organizational sharing, and professional society engagement to raise awareness of and overcome UDI system implementation challenges. Implementation of the UDI system will require integration of people, process, and technology to achieve benefits envisioned by FDA, including improved postmarket device surveillance and quality of care.

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.

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.

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.

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.

Challenges, alternatives, and paths to sustainability for health information exchange efforts.

Health information exchange (HIE) is a promising approach to improving the cost and quality of healthcare. We sought to identify the strengths and weaknesses of organizational models to achieve exchange, and what can be done to ensure the sustainability and effectiveness of exchange efforts. We interviewed state and national health informatics policy experts (n = 17). Data were collected as part of an evaluation of the Health Care Efficiency and Affordability Law for New Yorkers (HEAL NY) program and included respondents from both the private and public sectors. Data were analyzed using a general inductive and comparative approach with open coding of themes. Interviewees generally viewed HIE as a public or societal good to be valued. However, they identified challenges with the regional health information organization (RHIO) model of facilitating exchange including: economics, organizational issues, and geography. RHIOs were contrasted against alternative methods of exchange such as Direct, enterprise HIE, and vendor-mediated exchange. HIE is a difficult undertaking due to political and economic reasons. Alternatives to the RHIO model have features that may be more attractive to participants, but may be of less public benefit. Using states as intermediaries and mandating exchange under public health law may avoid the challenges facing exchange efforts. Moving forward, policies will have to address the shortcomings of each HIE model to ensure information is effectively shared between providers to maximize health benefits.

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.

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.

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.