A rapid non-invasive wearable device for assessing cardiac troponin.

BACKGROUND: Chest pain, a common emergency department 35 (ED) presentation, requires rapid evaluation. Optical technology-based non-invasive wearable devices (Infrasensor, RCE, Carlsbad, CA) rapidly and transcutaneously assesses cardiac Troponin I (cTnI). OBJECTIVES: To perform a pilot study describing the performance of the Infrasensor in cTnI defined cohorts. METHODS: This was a 10-hospital prospective observational study in healthy US subjects with a normal cTnI, and in patients with an elevated local cTnI. Healthy subjects were without disease, defined by a negative questionnaire and bloodwork, had a 3-minute Infrasensor measurement and blood samples for high-sensitivity cardiac troponin I (hs-cTnI), n-terminal pro-B-type natriuretic peptide (NTproBNP), creatinine, and glycosylated hemoglobin (HbA1c). Elevated cTnI's patients had the same Infrasensor and blood sample measurements. Using a cross validation technique, a cTnI based binary classification model that did, and did not, include age was trained with 80%, and validated on 20% (n=168; elevated hs-cTnI equally distributed across 5 folds) of the overall cohort. RESULTS: Of 840 patients, 727 (87.5%) were non-elevated cTnI controls and the remainder, n=113, had elevated cTnI. Median (25th, 75th percentiles) age was 61 (52, 71) and 48 (32, 57) years for the elevated and healthy control cohorts, respectively. Overall, 50.5% were female, with 29.2% and 52.7% in the elevated and non-elevated troponin cohorts respectively. Overall, the sensitivity, specificity, negative and positive predictive values of the Infrasensor for identifying an elevated cTnI were 0.9, 0.7, 0.98 and 0.48 respectively, with a C-statistic of 0.90 (0.89-0.99). CONCLUSIONS: The Infrasensor identifies elevated cTnI within 3 minutes of application.

Prompt engineering with a large language model to assist providers in responding to patient inquiries: a real-time implementation in the electronic health record.

Background: Large language models (LLMs) can assist providers in drafting responses to patient inquiries. We examined a prompt engineering strategy to draft responses for providers in the electronic health record. The aim was to evaluate the change in usability after prompt engineering. Materials and Methods: A pre-post study over 8 months was conducted across 27 providers. The primary outcome was the provider use of LLM-generated messages from Generative Pre-Trained Transformer 4 (GPT-4) in a mixed-effects model, and the secondary outcome was provider sentiment analysis. Results: Of the 7605 messages generated, 17.5% (n = 1327) were used. There was a reduction in negative sentiment with an odds ratio of 0.43 (95% CI, 0.36-0.52), but message use decreased (P < .01). The addition of nurses after the study period led to an increase in message use to 35.8% (P < .01). Discussion: The improvement in sentiment with prompt engineering suggests better content quality, but the initial decrease in usage highlights the need for integration with human factors design. Conclusion: Future studies should explore strategies for optimizing the integration of LLMs into the provider workflow to maximize both usability and effectiveness.

Morphology of Copper Nanofoams for Radiation Hydrodynamics and Fusion Applications Investigated by 3D Ptychotomography.

The performance of metal and polymer foams used in inertial confinement fusion (ICF), inertial fusion energy (IFE), and high-energy-density (HED) experiments is currently limited by our understanding of their nanostructure and its variation in bulk material. We utilized an X-ray-free electron laser (XFEL) together with lensless X-ray imaging techniques to probe the 3D morphology of copper foams at nanoscale resolution (28 nm). The observed morphology of the thin shells is more varied than expected from previous characterizations, with a large number of them distorted, merged, or open, and a targeted mass density 14% less than calculated. This nanoscale information can be used to directly inform and improve foam modeling and fabrication methods to create a tailored material response for HED experiments.

Call me Dr Ishmael: trends in electronic health record notes available at emergency department visits and admissions.

Objectives: Numerous studies have identified information overload as a key issue for electronic health records (EHRs). This study describes the amount of text data across all notes available to emergency physicians in the EHR, trended over the time since EHR establishment. Materials and Methods: We conducted a retrospective analysis of EHR data from a large healthcare system, examining the number of notes and a corresponding number of total words and total tokens across all notes available to physicians during patient encounters in the emergency department (ED). We assessed the change in these metrics over a 17-year period between 2006 and 2023. Results: The study cohort included 730 968 ED visits made by 293 559 unique patients and a total note count of 132 574 964. The median note count for all encounters in 2006 was 5 (IQR 1-16), accounting for 1735 (IQR 447-5521) words. By the last full year of the study period, 2022, the median number of notes had grown to 359 (IQR 84-943), representing 359 (IQR 84-943) words. Note and word counts were higher for admitted patients. Discussion: The volume of notes available for review by providers has increased by over 30-fold in the 17 years since the implementation of the EHR at a large health system. The task of reviewing these notes has become commensurately more difficult. These data point to the critical need for new strategies and tools for filtering, synthesizing, and summarizing information to achieve the promise of the medical record.

Influence of time-to-diagnosis on time-to-percutaneous coronary intervention for emergency department ST-elevation myocardial infarction patients: Time-to-electrocardiogram matters.

Objectives: Earlier electrocardiogram (ECG) acquisition for ST-elevation myocardial infarction (STEMI) is associated with earlier percutaneous coronary intervention (PCI) and better patient outcomes. However, the exact relationship between timely ECG and timely PCI is unclear. Methods: We quantified the influence of door-to-ECG (D2E) time on ECG-to-PCI balloon (E2B) intervention in this three-year retrospective cohort study, including patients from 10 geographically diverse emergency departments (EDs) co-located with a PCI center. The study included 576 STEMI patients excluding those with a screening ECG before ED arrival or non-diagnostic initial ED ECG. We used a linear mixed-effects model to evaluate D2E's influence on E2B with piecewise linear terms for D2E times associated with time intervals designated as ED intake (0-10 min), triage (11-30 min), and main ED (>30 min). We adjusted for demographic and visit characteristics, past medical history, and included ED location as a random effect. Results: The median E2B interval was longer (76 vs 68 min, p < 0.001) in patients with D2E >10 min than in those with timely D2E. The proportion of patients identified at the intake, triage, and main ED intervals was 65.8%, 24.9%, and 9.7%, respectively. The D2E and E2B association was statistically significant in the triage phase, where a 1-minute change in D2E was associated with a 1.24-minute change in E2B (95% confidence interval [CI]: 0.44-2.05, p = 0.003). Conclusion: Reducing D2E is associated with a shorter E2B. Targeting D2E reduction in patients currently diagnosed during triage (11-30 min) may be the greatest opportunity to improve D2B and could enable 24.9% more ED STEMI patients to achieve timely D2E.

Shorter Door-to-ECG Time Is Associated with Improved Mortality in STEMI Patients.

Background: Delayed intervention for ST-segment elevation myocardial infarction (STEMI) is associated with higher mortality. The association of door-to-ECG (D2E) with clinical outcomes has not been directly explored in a contemporary US-based population. Methods: This was a three-year, 10-center, retrospective cohort study of ED-diagnosed patients with STEMI comparing mortality between those who received timely (<10 min) vs. untimely (>10 min) diagnostic ECG. Among survivors, we explored left ventricular ejection fraction (LVEF) dysfunction during the STEMI encounter and recovery upon post-discharge follow-up. Results: Mortality was lower among those who received a timely ECG where one-week mortality was 5% (21/420) vs. 10.2% (26/256) among those with untimely ECGs (p = 0.016), and in-hospital mortality was 6.0% (25/420) vs. 10.9% (28/256) (p = 0.028). Data to compare change in LVEF metrics were available in only 24% of patients during the STEMI encounter and 46.5% on discharge follow-up. Conclusions: D2E within 10 min may be associated with a 50% reduction in mortality among ED STEMI patients. LVEF dysfunction is the primary resultant morbidity among STEMI survivors but was infrequently assessed despite low LVEF being an indication for survival-improving therapy. It will be difficult to assess the impact of STEMI care interventions without more consistent LVEF assessment.

Multicenter Development and Prospective Validation of eCARTv5: A Gradient Boosted Machine Learning Early Warning Score.

Rationale: Early detection of clinical deterioration using early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective internal validation, and were not tested in important patient subgroups. Objectives: To develop a gradient boosted machine model (eCARTv5) for identifying clinical deterioration and then validate externally, test prospectively, and evaluate across patient subgroups. Methods: All adult patients hospitalized on the wards in seven hospitals from 2008- 2022 were used to develop eCARTv5, with demographics, vital signs, clinician documentation, and laboratory values utilized to predict intensive care unit transfer or death in the next 24 hours. The model was externally validated retrospectively in 21 hospitals from 2009-2023 and prospectively in 10 hospitals from February to May 2023. eCARTv5 was compared to the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS) using the area under the receiver operating characteristic curve (AUROC). Measurements and Main Results: The development cohort included 901,491 admissions, the retrospective validation cohort included 1,769,461 admissions, and the prospective validation cohort included 46,330 admissions. In retrospective validation, eCART had the highest AUROC (0.835; 95%CI 0.834, 0.835), followed by NEWS (0.766 (95%CI 0.766, 0.767)), and MEWS (0.704 (95%CI 0.703, 0.704)). eCART's performance remained high (AUROC ≥0.80) across a range of patient demographics, clinical conditions, and during prospective validation. Conclusions: We developed eCARTv5, which accurately identifies early clinical deterioration in hospitalized ward patients. Our model performed better than the NEWS and MEWS retrospectively, prospectively, and across a range of subgroups.

Academic Detailing as a Health Information Technology Implementation Method: Supporting the Design and Implementation of an Emergency Department-Based Clinical Decision Support Tool to Prevent Future Falls.

BACKGROUND: Clinical decision support (CDS) tools that incorporate machine learning-derived content have the potential to transform clinical care by augmenting clinicians' expertise. To realize this potential, such tools must be designed to fit the dynamic work systems of the clinicians who use them. We propose the use of academic detailing-personal visits to clinicians by an expert in a specific health IT tool-as a method for both ensuring the correct understanding of that tool and its evidence base and identifying factors influencing the tool's implementation. OBJECTIVE: This study aimed to assess academic detailing as a method for simultaneously ensuring the correct understanding of an emergency department-based CDS tool to prevent future falls and identifying factors impacting clinicians' use of the tool through an analysis of the resultant qualitative data. METHODS: Previously, our team designed a CDS tool to identify patients aged 65 years and older who are at the highest risk of future falls and prompt an interruptive alert to clinicians, suggesting the patient be referred to a mobility and falls clinic for an evidence-based preventative intervention. We conducted 10-minute academic detailing interviews (n=16) with resident emergency medicine physicians and advanced practice providers who had encountered our CDS tool in practice. We conducted an inductive, team-based content analysis to identify factors that influenced clinicians' use of the CDS tool. RESULTS: The following categories of factors that impacted clinicians' use of the CDS were identified: (1) aspects of the CDS tool's design (2) clinicians' understanding (or misunderstanding) of the CDS or referral process, (3) the busy nature of the emergency department environment, (4) clinicians' perceptions of the patient and their associated fall risk, and (5) the opacity of the referral process. Additionally, clinician education was done to address any misconceptions about the CDS tool or referral process, for example, demonstrating how simple it is to place a referral via the CDS and clarifying which clinic the referral goes to. CONCLUSIONS: Our study demonstrates the use of academic detailing for supporting the implementation of health information technologies, allowing us to identify factors that impacted clinicians' use of the CDS while concurrently educating clinicians to ensure the correct understanding of the CDS tool and intervention. Thus, academic detailing can inform both real-time adjustments of a tool's implementation, for example, refinement of the language used to introduce the tool, and larger scale redesign of the CDS tool to better fit the dynamic work environment of clinicians.

Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical Diagnoses.

In the evolving landscape of clinical Natural Language Generation (NLG), assessing abstractive text quality remains challenging, as existing methods often overlook generative task complexities. This work aimed to examine the current state of automated evaluation metrics in NLG in healthcare. To have a robust and well-validated baseline with which to examine the alignment of these metrics, we created a comprehensive human evaluation framework. Employing ChatGPT-3.5-turbo generative output, we correlated human judgments with each metric. None of the metrics demonstrated high alignment; however, the SapBERT score-a Unified Medical Language System (UMLS)- showed the best results. This underscores the importance of incorporating domain-specific knowledge into evaluation efforts. Our work reveals the deficiency in quality evaluations for generated text and introduces our comprehensive human evaluation framework as a baseline. Future efforts should prioritize integrating medical knowledge databases to enhance the alignment of automated metrics, particularly focusing on refining the SapBERT score for improved assessments.

Automating risk stratification for geriatric syndromes in the emergency department.

BACKGROUND: Geriatric emergency department (GED) guidelines endorse screening older patients for geriatric syndromes in the ED, but there have been significant barriers to widespread implementation. The majority of screening programs require engagement of a clinician, nurse, or social worker, adding to already significant workloads at a time of record-breaking ED patient volumes, staff shortages, and hospital boarding crises. Automated, electronic health record (EHR)-embedded risk stratification approaches may be an alternate solution for extending the reach of the GED mission by directing human actions to a smaller subset of higher risk patients. METHODS: We define the concept of automated risk stratification and screening using existing EHR data. We discuss progress made in three potential use cases in the ED: falls, cognitive impairment, and end-of-life and palliative care, emphasizing the importance of linking automated screening with systems of healthcare delivery. RESULTS: Research progress and operational deployment vary by use case, ranging from deployed solutions in falls screening to algorithmic validation in cognitive impairment and end-of-life care. CONCLUSIONS: Automated risk stratification offers a potential solution to one of the most pressing problems in geriatric emergency care: identifying high-risk populations of older adults most appropriate for specific GED care. Future work is needed to realize the promise of improved care with less provider burden by creating tools suitable for widespread deployment as well as best practices for their implementation and governance.

Dashboarding to Monitor Machine-Learning-Based Clinical Decision Support Interventions.

BACKGROUND: Existing monitoring of machine-learning-based clinical decision support (ML-CDS) is focused predominantly on the ML outputs and accuracy thereof. Improving patient care requires not only accurate algorithms but also systems of care that enable the output of these algorithms to drive specific actions by care teams, necessitating expanding their monitoring. OBJECTIVES: In this case report, we describe the creation of a dashboard that allows the intervention development team and operational stakeholders to govern and identify potential issues that may require corrective action by bridging the monitoring gap between model outputs and patient outcomes. METHODS: We used an iterative development process to build a dashboard to monitor the performance of our intervention in the broader context of the care system. RESULTS: Our investigation of best practices elsewhere, iterative design, and expert consultation led us to anchor our dashboard on alluvial charts and control charts. Both the development process and the dashboard itself illuminated areas to improve the broader intervention. CONCLUSION: We propose that monitoring ML-CDS algorithms with regular dashboards that allow both a context-level view of the system and a drilled down view of specific components is a critical part of implementing these algorithms to ensure that these tools function appropriately within the broader care system.

Practical applications of three-dimensional printing for process improvement in the cytopathology laboratory.

With the increased availability of three-dimensional (3D) printers, innovative teaching and training materials have been created in medical fields. For pathology, the use of 3D printing has been largely limited to anatomic representations of disease processes or the development of supplies during the coronavirus disease 2019 pandemic. Herein, an institution's 3D printing laboratory and staff with expertise in additive manufacturing illustrate how this can address design issues in cytopathology specimen collection and processing. The authors' institutional 3D printing laboratory, along with students and trainees, used computer-aided design and 3D printers to iterate on design, create prototypes, and generate final usable materials using additive manufacturing. The program Microsoft Forms was used to solicit qualitative and quantitative feedback. The 3D-printed models were created to assist with cytopreparation, rapid on-site evaluation, and storage of materials in the preanalytical phase of processing. These parts provided better organization of materials for cytology specimen collection and staining, in addition to optimizing storage of specimens with multiple sized containers to optimize patient safety. The apparatus also allowed liquids to be stabilized in transport and removed faster at the time of rapid on-site evaluation. Rectangular boxes were also created to optimally organize all components of a specimen in cytopreparation to simplify and expedite the processes of accessioning and processing, which can minimize errors. These practical applications of 3D printing in the cytopathology laboratory demonstrate the utility of the design and printing process on improving aspects of the workflow in cytopathology laboratories to maximize efficiency, organization, and patient safety.

Creation of a data commons for substance misuse related health research through privacy-preserving patient record linkage between hospitals and state agencies.

Objectives: Substance misuse is a complex and heterogeneous set of conditions associated with high mortality and regional/demographic variations. Existing data systems are siloed and have been ineffective in curtailing the substance misuse epidemic. Therefore, we aimed to build a novel informatics platform, the Substance Misuse Data Commons (SMDC), by integrating multiple data modalities to provide a unified record of information crucial to improving outcomes in substance misuse patients. Materials and Methods: The SMDC was created by linking electronic health record (EHR) data from adult cases of substance (alcohol, opioid, nonopioid drug) misuse at the University of Wisconsin hospitals to socioeconomic and state agency data. To ensure private and secure data exchange, Privacy-Preserving Record Linkage (PPRL) and Honest Broker services were utilized. The overlap in mortality reporting among the EHR, state Vital Statistics, and a commercial national data source was assessed. Results: The SMDC included data from 36 522 patients experiencing 62 594 healthcare encounters. Over half of patients were linked to the statewide ambulance database and prescription drug monitoring program. Chronic diseases accounted for most underlying causes of death, while drug-related overdoses constituted 8%. Our analysis of mortality revealed a 49.1% overlap across the 3 data sources. Nonoverlapping deaths were associated with poor socioeconomic indicators. Discussion: Through PPRL, the SMDC enabled the longitudinal integration of multimodal data. Combining death data from local, state, and national sources enhanced mortality tracking and exposed disparities. Conclusion: The SMDC provides a comprehensive resource for clinical providers and policymakers to inform interventions targeting substance misuse-related hospitalizations, overdoses, and death.

Effectiveness of an Emergency Department-Based Machine Learning Clinical Decision Support Tool to Prevent Outpatient Falls Among Older Adults: Protocol for a Quasi-Experimental Study.

BACKGROUND: Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether automation of screening and referrals can reduce the risk of future falls among older patients. OBJECTIVE: The goal of this paper is to describe a research protocol for evaluating the effectiveness of an automated screening and referral intervention. These findings will inform ongoing discussions about the use of ML and artificial intelligence to augment medical decision-making. METHODS: To assess the effectiveness of our program for patients receiving the falls risk intervention, our primary analysis will be to obtain referral completion rates at 3 different EDs. We will use a quasi-experimental design known as a sharp regression discontinuity with regard to intent-to-treat, since the intervention is administered to patients whose risk score falls above a threshold. A conditional logistic regression model will be built to describe 6-month fall risk at each site as a function of the intervention, patient demographics, and risk score. The odds ratio of a return visit for a fall and the 95% CI will be estimated by comparing those identified as high risk by the ML-based CDS (ML-CDS) and those who were not but had a similar risk profile. RESULTS: The ML-CDS tool under study has been implemented at 2 of the 3 EDs in our study. As of April 2023, a total of 1326 patient encounters have been flagged for providers, and 339 unique patients have been referred to the mobility and falls clinic. To date, 15% (45/339) of patients have scheduled an appointment with the clinic. CONCLUSIONS: This study seeks to quantify the impact of an ML-CDS intervention on patient behavior and outcomes. Our end-to-end data set allows for a more meaningful analysis of patient outcomes than other studies focused on interim outcomes, and our multisite implementation plan will demonstrate applicability to a broad population and the possibility to adapt the intervention to other EDs and achieve similar results. Our statistical methodology, regression discontinuity design, allows for causal inference from observational data and a staggered implementation strategy allows for the identification of secular trends that could affect causal associations and allow mitigation as necessary. TRIAL REGISTRATION: ClinicalTrials.gov NCT05810064; https://www.clinicaltrials.gov/study/NCT05810064. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48128.

Management of Complex Duodenal Injuries After Penetrating Trauma.

Penetrating injuries to the duodenum can present a complex case for trauma or acute care surgeons. The associated injuries and complications can have devastating results. This report presents the case of a 41-year-old male who presented with a gunshot wound to his abdomen and suffered a gastric injury, transverse colon injury, duodenal injury, renal injury, and pancreatic tail injury. In this case, the patient underwent a complex Roux-en-Y reconstruction. The patient had a good outcome and continues to recover at home.

Direct observation of coherence transfer and rotational-to-vibrational energy exchange in optically centrifuged CO2 super-rotors.

Optical centrifuges are laser-based molecular traps that can rotationally accelerate molecules to energies rivalling or exceeding molecular bond energies. Here we report time and frequency-resolved ultrafast coherent Raman measurements of optically centrifuged CO2 at 380 Torr spun to energies beyond its bond dissociation energy of 5.5 eV (Jmax = 364, Erot = 6.14 eV, Erot/kB = 71, 200 K). The entire rotational ladder from J = 24 to J = 364 was resolved simultaneously which enabled a more accurate measurement of the centrifugal distortion constants for CO2. Remarkably, coherence transfer was directly observed, and time-resolved, during the field-free relaxation of the trap as rotational energy flowed into bending-mode vibrational excitation. Vibrationally excited CO2 (ν2 > 3) was observed in the time-resolved spectra to populate after 3 mean collision times as a result of rotational-to-vibrational (R-V) energy transfer. Trajectory simulations show an optimal range of J for R-V energy transfer. Dephasing rates for molecules rotating up to 5.5 times during one collision were quantified. Very slow decays of the vibrational hot band rotational coherences suggest that they are sustained by coherence transfer and line mixing.

Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults.

BACKGROUND: The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing NLP pipelines at the bedside for health care delivery. OBJECTIVE: We aimed to detail a hospital-wide, operational pipeline to implement a real-time NLP-driven CDS tool and describe a protocol for an implementation framework with a user-centered design of the CDS tool. METHODS: The pipeline integrated a previously trained open-source convolutional neural network model for screening opioid misuse that leveraged EHR notes mapped to standardized medical vocabularies in the Unified Medical Language System. A sample of 100 adult encounters were reviewed by a physician informaticist for silent testing of the deep learning algorithm before deployment. An end user interview survey was developed to examine the user acceptability of a best practice alert (BPA) to provide the screening results with recommendations. The planned implementation also included a human-centered design with user feedback on the BPA, an implementation framework with cost-effectiveness, and a noninferiority patient outcome analysis plan. RESULTS: The pipeline was a reproducible workflow with a shared pseudocode for a cloud service to ingest, process, and store clinical notes as Health Level 7 messages from a major EHR vendor in an elastic cloud computing environment. Feature engineering of the notes used an open-source NLP engine, and the features were fed into the deep learning algorithm, with the results returned as a BPA in the EHR. On-site silent testing of the deep learning algorithm demonstrated a sensitivity of 93% (95% CI 66%-99%) and specificity of 92% (95% CI 84%-96%), similar to published validation studies. Before deployment, approvals were received across hospital committees for inpatient operations. Five interviews were conducted; they informed the development of an educational flyer and further modified the BPA to exclude certain patients and allow the refusal of recommendations. The longest delay in pipeline development was because of cybersecurity approvals, especially because of the exchange of protected health information between the Microsoft (Microsoft Corp) and Epic (Epic Systems Corp) cloud vendors. In silent testing, the resultant pipeline provided a BPA to the bedside within minutes of a provider entering a note in the EHR. CONCLUSIONS: The components of the real-time NLP pipeline were detailed with open-source tools and pseudocode for other health systems to benchmark. The deployment of medical artificial intelligence systems in routine clinical care presents an important yet unfulfilled opportunity, and our protocol aimed to close the gap in the implementation of artificial intelligence-driven CDS. TRIAL REGISTRATION: ClinicalTrials.gov NCT05745480; https://www.clinicaltrials.gov/ct2/show/NCT05745480.