Multiple Clinical Sites Research Articles

Variation in Opioid Agonist Dosing in Clinical Trials by Race and Ethnicity

Racial and ethnic disparities in access to treatment and quality of treatment for opioid use disorder (OUD) have been identified in usual care settings. In contrast, disparities in treatment quality within clinical trials are relatively unexamined. To estimate racial and ethnic differences in the dose of opioid agonist treatment for OUD in the first 4 weeks of treatment in clinical trials. This cohort study performed analysis of the methadone and buprenorphine treatment arms of 3 trials conducted by the National Institute on Drug Abuse Clinical Trials Network between May 2006, and January 31, 2017, at multiple Clinical Trials Network sites across the US. Trial participants who were randomized to and initiated buprenorphine or methadone treatment and who identified as Hispanic, non-Hispanic Black, or non-Hispanic White were included in the present study. Data were analyzed from November 1, 2023, to August 5, 2024. Combined race and ethnicity as self-classified by the patient at trial enrollment. The maximum daily dose of buprenorphine or methadone received in each week for the first 4 weeks of treatment. The mean dose and the percentage of patients receiving a higher dose (buprenorphine ≥16 mg and methadone ≥60 mg) were compared across race and ethnicity groups. A total of 1748 patients (1263 who initiated buprenorphine and 485 who initiated methadone treatment) were included in the analysis (1168 [66.8%] male; median age, 33 [IQR, 26-45] years). Of these, 138 patients (7.9%) identified as Black, 273 (15.6%) as Hispanic, and 1337 (76.5%) as White. In week 4, Black patients received buprenorphine doses 2.5 (95% CI -4.6 to -0.5) mg lower and methadone doses 16.7 (95% CI, -30.7 to -2.7) mg lower compared with White patients, after standardizing by age and sex. In week 4, the percentage of patients receiving a higher dose of medication (buprenorphine ≥16 mg; methadone ≥60 mg) was 16.9 (95% CI, -31.9 to -1.9) points lower for Black patients compared with White patients. Hispanic and White patients received similar buprenorphine doses; Hispanic patients received lower methadone doses than White patients. In this cohort study of data from 3 clinical trials, White patients generally received higher doses of medication than Black patients. Future research is needed to understand the mechanisms of and interventions to reduce disparities in OUD treatment quality and how such disparities impact generalizability of trial results.

NEAT: Nurse Effort Assessment Tool—Human Factors Considerations in Designing for Appropriate Staffing

This research has developed an algorithm to measure behavioral health acuity that relies predominantly on patient-level information from electronic health records (EHRs) but also has nurse-input-driven parameters contributing to the tool, known as the Nurse Effort Assessment Tool (NEAT). The overall objective is to create the NEAT to address the increasing acuity of behavioral patients by moving away from subjective patient assessment to using an automated algorithm that draws predominantly from the rich data in EHRs. Data from over 9,000 patient records provided statistically significant results that the subjective rating both correlates and serves as significant predictors for the NEAT score. Future research looks to expand the algorithm and validate it across multiple clinical sites.

Clinical Learning and LGBTQ+ Health: Summary of a LGBTQ+ Health Clinical Rotation.

People who identify as lesbian, gay, bisexual, transgender, queer, and people who identify as something else (LGBTQ+) experience barriers to health care including a lack of health care providers adequately prepared to care for them. The literature describing LGBTQ+-focused didactic learning activities has increased in recent years, however remains limited with respect to clinical learning experiences. This publication describes the development and implementation of a clinical rotation for Physician Assistant (PA) students focused on caring for LGBTQ+ patients. A 4-week LGBTQ+ Health clinical rotation was developed for PA students through collaboration with multiple clinical sites. Students (n = 6) were surveyed before and after completion of the clinical rotation on their knowledge, comfort, and confidence in caring for LGBTQ+ patients and to identify learning priorities. Paired t tests were used to compare quantitative pre and post data. Two reviewers independently reviewed qualitative data. Improvement in students' knowledge, comfort, and confidence were statistically significant from prerotation to postrotation surveys. Student ratings on standard course evaluations showed positive ratings for all course elements. Qualitative analysis showed that all students identified learning more about trans-related health care as a learning priority. Clinical rotations focused on the care of LGBTQ+ patients are valuable learning experiences which can support increased student knowledge, comfort, and confidence. Clinically based educational interventions may be one strategy in addressing the health disparities experienced by LGBTQ+ people.

Machine learning and deep learning for classifying the justification of brain CT referrals.

To train the machine and deeplearning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts. Adult brain computed tomography (CT) referrals from scans performed in three CT centres in Ireland in 2020 and 2021 were retrospectively collected. Two radiographers analysed the justification of 3000 randomly selected referrals using iGuide, with two consultant radiologists analysing the referrals with disagreement. Insufficient or duplicate referrals were discarded. The inter-rater agreement among radiographers and consultants was computed. A random split (4:1) was performed to apply machine learning (ML) and deeplearning (DL) techniques to unstructured clinical indications to automate retrospective justification auditing with multi-class classification. The accuracy and macro-averaged F1 score of the best-performing classifier of each type on the training set were computed on the test set. 42 referrals were ignored. 1909 (64.5%) referrals were justified, 811 (27.4%) were potentially justified, and 238 (8.1%) were unjustified. The agreement between radiographers (κ = 0.268) was lower than radiologists (κ = 0.460). The best-performing ML model was the bag-of-words-based gradient-boosting classifier achieving a 94.4% accuracy and a macro F1 of 0.94. DL models were inferior, with bi-directional long short-term memory achieving 92.3% accuracy, a macro F1 of 0.92, and outperforming multilayer perceptrons. Interpreting unstructured clinical indications is challenging necessitating clinical decision support. ML and DL can generalise across multiple clinical sites, outperform human experts, and be used as an artificial intelligence-based iGuide interpreter when retrospectively vetting radiology referrals. Healthcare vendors and clinical sites should consider developing and utilising artificial intelligence-enabled systems for justifying medical exposures. This would enable better implementation of imaging referral guidelines in clinical practices and reduce population dose burden, CT waiting lists, and wasteful use of resources. Significant variations exist among human experts in interpreting unstructured clinical indications/patient presentations. Machine and deep learning can automate the justification analysis of radiology referrals according to iGuide categorisation. Machine and deep learning can improve retrospective and prospective justification auditingfor better implementation of imaging referral guidelines.

Expanding Integrated Physiatric Care: A Method to Building Capacity in Physical Medicine and Rehabilitation.

Siloed and episodic care delivery is often not equitable, high-quality, or sustainable. Transitioning from separate care settings, with potentially divergent care models, to an integrated care model is not always straightforward. Some experiences in expanding collaborative care between physiatrists and other healthcare providers for a variety of patient populations and care settings within a university physical medicine and rehabilitation (PM&R) division are shared as a means to inspire the uptake of care integration initiatives more broadly within the specialty. After an initial survey of care integration across multiple clinical sites, the university division: highlighted successful integrated care models; discussed integrated care models at every divisional retreat; reached out to clinicians in other specialties to collaboratively explore expansion; developed a "one-pager" on what physiatrists do; and invited collaborative specialists from integrated clinics to PM&R national and/or international meetings. Since 2019, divisional activity in integrated care has grown and evolved substantially. Future work will focus on: further expansion of integrated clinical care; scholarly evaluation of integrated care models; expansion of academic activity resulting from integration; and advocacy to healthcare providers, hospital administrators, and health system funders about the potential value of care integration in improving rehabilitation outcomes.

Informal judgement of delirium status underestimates delirium prevalence: World Delirium Awareness Day point prevalence results from Ireland

Introduction Delirium is an encephalopathy characterized by acute onset, fluctuation, and prominent deficits in attention and alertness, due to one or more physical disorders, interventions or medications. It is associated with increased mortality and morbidity, and with incidence and worsening of dementia; thus, delirium prevention and timely detection and appropriate management are crucial. Objective This study aimed to determine delirium prevalence in Irish clinical sites. Methods The overall study was an observational, cohort study of the point prevalence of delirium (and current delirium practice) in multiple clinical sites on a single day (i.e. World Delirium Awareness Day, March 2023). In this study, the point prevalence of delirium is reported in Irish sites. Results In total, 132 wards from 15 hospitals across Ireland participated in this study, including general medical, surgical and specialised wards, and some long-term care and rehabilitation wards. Overall, 27% of patients on wards which reported using a formal tool had not been assessed for delirium. Delirium prevalence, using a formal assessment tool, was 15.9% overall, with the highest rate in geriatric wards (20.5%). However, on wards where ‘personal judgment’ to detect delirium was utilized, the prevalence rate was 11.5%, implying that delirium may have been under-detected on these wards. Conclusions Delirium screening can quickly rule out delirium or lead to more formal assessment where screening is positive or equivocal. Delirium is prevalent in Irish hospitals and appears to be under-detected unless a formal screening tool is used; equally, delirium screening is not universally applied on wards reporting that they use a formal tool. Together, this indicates that delirium is still under-diagnosed in Irish hospitals.

Breaking barriers for cancer clinical trials: A multi-tiered interventional approach to boost racial and ethnic minority participation in early-phase studies.

TPS1639 Background: Racial and ethnic minority populations are underrepresented in cancer clinical trials. The USC Norris Comprehensive Cancer Center (USC NCCC) has multiple clinical trial recruitment sites, including the Los Angeles General Medical Center (LAG), the largest safety-net hospital in the western USA. LAG partners with USC to provide cancer care to the underserved minority population of LAC, including clinical trials. As a result, between 2015-2022 a total of 120 LAG patients were accrued to phase I clinical trials; enrolled subjects were 66% Hispanic, 21% Asian, 4% Black, and 9% other. An interventional study was designed to further enhance the referral and participation of racial and ethnic minority patients from other Los Angeles County (LAC) safety-net facilities. The study is funded by the Stand up to Cancer Diversity in Early Development Clinical Trials Research Grants Program. Methods: This interventional study has 3 specific aims. First, to identify barriers to referral for minority and underserved patients: Qualitative interviews will be conducted with key stakeholders in the LAC safety net system which will uncover barriers to referring minority and underserved patients. A screening tool developed by the NCI to characterize barriers will be used. Second, to implement a multi-component program to enhance the referral of patients to early-phase clinical trials. The intervention addresses system-level, provider-level, and patient-level factors to improve the process, with a demonstration site compared to a non-demonstration site. Key features include a streamlined referral process for healthcare professionals with a singular centrally operated email managed by a specialized facilitator, weekly meetings to assign trials to prospective patients, and facilitation of approval and scheduling for referrals. We will educate providers regarding trials and exchange feedback on the referral process. We will extend resources to patients to assist with financial and transportation limitations. We will compare change in referral and enrollment rates at baseline and after the intervention for both the demonstration site and non-demonstration site. Third, to explore the impact of remotely conducted clinical trials: A clinical trial (NCT05340309) administers study drug subcutaneously and uses telemedicine visits, wearable sensors, and a smartphone app for monitoring. This pilot trial will be analyzed to determine the model’s impact on barriers to enrollment. The above interventions may provide a model that can be adapted in other healthcare delivery settings to optimize referral and enrollment of racial and ethnic minorities.

Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training

Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct therapeutic strategy. Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. Obtaining sufficient data from a single clinical site is challenging and does not address the heterogeneous need for model robustness. Conversely, the collection of data from multiple sites introduces data privacy concerns and potential label noise due to varying annotation standards. To address this dilemma, we explore the use of the federated learning framework while considering label noise. Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction. Specifically, we introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions, enabling the correction of false annotations based on prediction confidence. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites, enhancing the reliability of the correction process. Extensive experiments conducted on two multi-site datasets demonstrate the effectiveness and robustness of our proposed methods, indicating their potential for clinical applications in multi-site collaborations to train better deep learning models with lower cost in data collection and annotation.

Student challenges during third-year perioperative clerkships through the lens of faculty and residents: A qualitative study.

Studies of medical students suggest they often find the transition from the pre-clinical curriculum to clinical rotations particularly challenging during perioperative clerkships. Educators could add a new perspective into students' clerkship experiences and potential interventions to improve them. The purpose of this study was to examine the educator perspective on students' experiences in perioperative clerkships. The findings could inform potential curricular interventions to facilitate student transition from a didactic environment into perioperative clerkships. Semi-structured qualitative interviews were conducted with 16 faculty and residents in the departments of anaesthesia, obstetrics and gynaecology (OBGYN), and general surgery across multiple clinical teaching sites at one institution. Interview questions explored their perceptions of the challenges students face during their transition into perioperative clerkships and probed thoughts on curriculum interventions they believed would be the most beneficial. Interviews were recorded, transcribed and analysed thematically. Three themes were identified. Faculty and residents perceive that student experiences on perioperative clerkships are shaped by (1) students' ability to adapt to the specialty and operating room norms on these clerkships, (2) students' understanding of how they can meaningfully contribute to the clinical team, and (3) dedicated teaching time constraints. Interventions were suggested to address educator expectations and student gaps, such as implementing a pre-clerkship orientation across anaesthesia, general surgery and OBGYN. To facilitate the medical student transition to perioperative clerkships, interventions should aid students in adapting to clerkship norms for these specialties and clarifying their role and expectations within the care team.

Robustness of Deep Networks for Mammography: Replication Across Public Datasets

Deep neural networks have demonstrated promising performance in screening mammography with recent studies reporting performance at or above the level of trained radiologists on internal datasets. However, it remains unclear whether the performance of these trained models is robust and replicates across external datasets. In this study, we evaluate four state-of-the-art publicly available models using four publicly available mammography datasets (CBIS-DDSM, INbreast, CMMD, OMI-DB). Where test data was available, published results were replicated. The best-performing model, which achieved an area under the ROC curve (AUC) of 0.88 on internal data from NYU, achieved here an AUC of 0.9 on the external CMMD dataset (N = 826 exams). On the larger OMI-DB dataset (N = 11,440 exams), it achieved an AUC of 0.84 but did not match the performance of individual radiologists (at a specificity of 0.92, the sensitivity was 0.97 for the radiologist and 0.53 for the network for a 1-year follow-up). The network showed higher performance for in situ cancers, as opposed to invasive cancers. Among invasive cancers, it was relatively weaker at identifying asymmetries and was relatively stronger at identifying masses. The three other trained models that we evaluated all performed poorly on external datasets. Independent validation of trained models is an essential step to ensure safe and reliable use. Future progress in AI for mammography may depend on a concerted effort to make larger datasets publicly available that span multiple clinical sites.

Pilocarpine HCl 1.25% for treatment of presbyopia after laser vision correction: pooled analysis of two phase 3 randomized trials (GEMINI 1 and 2).

To evaluate the efficacy of topical pilocarpine HCl 1.25% (Pilo) in treating presbyopia in individuals with or without a history of laser vision correction (laser-assisted in situ keratomileusis [LASIK] or photorefractive keratectomy [PRK]). Multiple clinical sites. Pooled analysis of 2 identically designed prospective, randomized, vehicle-controlled studies (GEMINI 1 and 2). Adults aged 40 to 55 years with presbyopia received once-daily Pilo or vehicle bilaterally for 30 days. Responder rates for ≥3-line improvement in mesopic, high-contrast, binocular distance-corrected near visual acuity (DCNVA) were determined on day 30. Among participants with a history of LASIK/PRK (n = 39 in the Pilo group, n = 41 in the vehicle group), responder rates for ≥3-line improvement in DCNVA on day 30 at hours 0.25, 0.5, 1, 3, 6, 8, and 10, respectively, were 16.7%, 38.9%, 41.7%, 37.8%, 16.2%, 13.9%, and 8.3% with Pilo and 0.0%, 2.6%, 10.5%, 5.1%, 7.7%, 2.6%, and 0.0% with vehicle. Responder rates in the LASIK/PRK subgroup were significantly higher with Pilo than vehicle at hours 0.25 ( P = .0087), 0.5 ( P = .0001), 1 ( P = .0022), and 3 ( P = .0005). In contrast, there were no significant differences in responder rates between Pilo-treated participants with and without LASIK/PRK. Among non-LASIK/PRK participants in the Pilo group (n = 336), responder rates for ≥3-line improvement in DCNVA on day 30 at hours 0.25, 0.5, 1, 3, 6, 8, and 10, respectively, were 16.8%, 32.7%, 39.0%, 28.0%, 17.4%, 12.6%, and 10.5%. Pilo treatment effectively and similarly improved DCNVA in presbyopes with or without a history of laser vision correction.

Clinical outcomes in the biomarkers of ketamine (Bio-K) study of open-label IV ketamine for refractory depression

ObjectiveWe conducted an open-label clinical trial (“Bio-K”) using IV ketamine for treatment-resistant depression to identify biomarkers linked to remission. Here, we report the clinical efficacy and side effect outcomes of Bio-K. MethodsAcross 4 US sites, 75 patients ages 18–65 with treatment-refractory unipolar or bipolar depression received 3 IV ketamine infusions over an 11-day period. Key exclusion criteria were psychotic symptoms, significant substance abuse, unstable medical conditions, and any use of cannabis. Pre-existing antidepressant medication was maintained. Primary outcome was remission as measured by Montgomery-Asberg Depression Rating Scale (MADRS), with secondary outcome of 50 % reduction in Beck Suicide Scale score. Safety monitoring and varying durations of infusions were also key parameters. ResultsUsing remission as MADRS score <10, after 3 infusions 52 % achieved remission, with 67 % achieving response. Of those achieving response after a single infusion, 66 % (22 of 33) reached remission after 3 infusions, while 40 % (16 of 40) non-responders after the first infusion went on to achieve remission after 3 infusions. Only 20 % of non-responders after 2 infusions achieved remission. Most (81 %) participants had significant suicidal ideation at baseline; of these, two-thirds (67 %) experienced at least a 50 % reduction in suicidality. Side effects were minimal. Uniquely, we had three different types of infusion categories, with individuals receiving: (1) slow (100-min) infusions only or (2) regular (40-min) infusions only or (3) a mix of infusion durations. These three infusion groups showed comparable safety and efficacy. Exploration of clinical factors revealed no link between BMI, age, or gender to remission. ConclusionsThe consistency of outcomes across 4 clinical sites and across multiple instruments, suggests high acute efficacy and safety of IV ketamine for serious depressive episodes. Duration of infusion did not alter outcomes. Meaningfully, 40 % of non-responders after a single infusion did reach remission subsequently, while only 20 % of non-responders after 2 infusions achieved remission, suggesting early response is suggestive for eventual remission. Our data on varying ketamine infusion duration adds novel insights into the clinical administration of this new treatment for refractory and severe patients. Our limitations included a lack of a control group, necessitating caution about conclusions of efficacy, balanced by the utility of reporting “real-world” outcomes across multiple clinical sites. We could also not separately analyze results for bipolar disorder due to small numbers. Together, the Bio-K clinical results are promising and provide significant sample sizes for forthcoming biological markers analyses.

CLASSICA: Validating artificial intelligence in classifying cancer in real time during surgery.

Treatment pathways for significant rectal polyps differ depending on the underlying pathology, but pre-excision profiling is imperfect. It has been demonstrated that differences in fluorescence perfusion signals following injection of indocyanine green (ICG) can be analysed mathematically and, with the assistance of artificial intelligence (AI), used to classify tumours endoscopically as benign or malignant. This study aims to validate this method of characterization across multiple clinical sites regarding its generalizability, usability and accuracy while developing clinical-grade software to enable it to become a useful method. The CLASSICA study is a prospective, unblinded multicentre European observational study aimed to validate the use of AI analysis of ICG fluorescence for intra-operative tissue characterization. Six hundred patients undergoing transanal endoscopic evaluation of significant rectal polyps and tumours will be enrolled in at least five clinical sites across the European Union over a 4-year period. Video recordings will be analysed regarding dynamic fluorescence patterns centrally as software is developed to enable analysis with automatic classification to happen locally. AI-based classification and subsequently guided intervention will be compared with the current standard of care including biopsies, final specimen pathology and patient outcomes. CLASSICA will validate the use of AI in the analysis of ICG fluorescence for the purposes of classifying significant rectal polyps and tumours endoscopically. Follow-on studies will compare AI-guided targeted biopsy or, indeed, AI characterization alone with traditional biopsy and AI-guided local excision versus traditional excision with regard to marginal clearance and recurrence.

Good practices for the automated production of 18F-SiFA radiopharmaceuticals

BackgroundThe positron emitting isotope fluorine-18 (18F) possesses almost ideal physicochemical properties for the development of radiotracers for diagnostic molecular imaging employing positron emission tomography (PET). 18F in its nucleophilic anionic 18F− form is usually prepared by bombarding an enriched 18O water target with protons of various energies between 5 and 20 MeV depending on the technical specifications of the cyclotron. Large thick-target yields between 5 and 14 GBq/µA can be obtained, enough to prepare large batches of radiotracers capable to serve a considerable contingent of patients (50 + per clinical batch). The overall yield of the radiotracer however depends on the efficiency of the 18F labeling chemistry. The Silicon Fluoride Acceptor chemistry (SiFA) has introduced a convenient and highly efficient way to provide clinical peptide-based 18F-radiotracers in a kit-like procedure matching the convenience of 99mTc radiopharmaceuticals.Main bodyA radiotracer’s clinical success primarily hinges on whether its synthesis can be automated. Due to its simplicity, the SiFA chemistry, which is based on isotopic exchange (18F for 19F), does not only work in a manual setup but has been proven to be automatable, yielding large batches of 18F-radiotracers of high molar activity (Am). The production of SiFA radiotracer can be centralized and the radiopharmaceutical be distributed via the “satellite” principle, where one production facility economically serves multiple clinical application sites. Clinically validated tracers such as [18F]SiTATE and [18F]Ga-rhPSMA-7/-7.3 have been synthesized in an automated synthesis unit under good manufacturing practice conditions and used in large patient cohorts. Communication of common guidelines and practices is warranted to further the dissemination of SiFA radiopharmaceuticals and to give easy access to this technology.ConclusionThis current review highlights the most recent achievements in SiFA radiopharmaceutical automation geared towards large batch production for clinical application. Best practice advice and guidance towards a facilitated implementation of the SiFA technology into new and already operating PET tracer production facilities is provided. A brief outlook spotlights the future potential of SiFA radiochemistry within the landscape of non-canonical labeling chemistries.

Qualification of the differential leukocyte count and immunophenotyping in cryopreserved ex vivo whole blood assay.

We developed a flow cytometry-based assay, termed Differential Leukocyte Counting and Immunophenotyping in Cryopreserved Ex vivo whole blood (DLC-ICE), that allows quantification of absolute counts and frequencies of leukocyte subsets and measures expression of activation, phenotypic and functional markers. We evaluated the performance of the DLC-ICE assay by determining inter-operator variability for processing fresh whole blood (WB) from healthy donors collected at multiple clinical sites. In addition, we assessed inter-operator variability for staining of fixed cells and robustness across different anticoagulants. Accuracy was evaluated by comparing DLC-ICE measurements to real-time cell enumeration using an accredited hematology analyzer. Finally, we developed and tested the performance of a 27-colour immunophenotyping panel on cryopreserved fixed WB and compared results to matched fresh WB. Overall, we observed <20% variability in absolute counts and frequencies of granulocytes, monocytes and lymphocytes (T, B and NK cells) when fresh WB was collected in different anti-coagulant tubes, processed or stained by independent operators. Absolute cell counts measured across operators and anti-coagulants using the DLC-ICE method exhibited excellent correlation with the reference method, complete blood count (CBC) with differential, measured using a hematology analyzer (r2 > 0.9 for majority of measurements). A comparison of leukocyte immunophenotyping on fresh WB versus DLC-ICE processed blood yielded equivalent and linear results over a wide dynamic range (r2 = 0.94 over 10-104 cells/μL). These results demonstrate low variability across trained operators, high robustness, linearity and accuracy, supporting utility of the DLC-ICE assay for large cohort studies involving multiple clinical research sites.

Indian Real-world Evidence of Rivaroxaban Use in Coronary Artery Disease – A Prospective Observational Cohort Study (IN-RECORD): Study Design and Baseline Characteristics

Abstract Background: Antiplatelet therapy (APT) is the cornerstone for cardiovascular (CV) prevention in patients with coronary artery disease (CAD). However, there is potential for greater effectiveness in vascular prevention for patients with stable CAD or peripheral artery disease (PAD) through the use of rivaroxaban alone or in combination with aspirin, surpassing the benefits offered by aspirin alone. Materials and Methods: INdian Real world EvidenCe Of Rivaroxaban use in coronary artery Disease – A prospective observational cohort study (IN-RECORD) study is designed as a prospective, observational study. The objective of this study is to compare the real-world effectiveness and safety of rivaroxaban in combination with APT (dual pathway inhibition therapy [DPIT]) versus dual APT (DAPT) in Indian patients with CAD and peripheral arterial disease (PAD). The study will encompass multiple clinical sites across the Indian geography. The study’s primary outcome is the comparison of net clinical benefit between the two groups which is defined as the composite of effectiveness (reduction in incidence of CV death, stroke, and myocardial infarction [MI]) and safety (life-threatening or fatal bleeding). Results: From June 2021 to December 2022, we enrolled 374 patients from the participating sites from India. We enrolled 225 and 149 patients in the DAPT and DPIT groups, respectively. The mean observed age and weight at the baseline were 63.05 ± 10.09 years and 64.15 ± 11.43 kg, respectively. There was a male preponderance observed in the enrolled patients (males: 86.63%, females: 13.37%). The most frequent comorbidity observed in CAD patients was hypertension, followed by diabetes and MI. At baseline, a significant percentage of patients were taking antiplatelet medications besides aspirin. Conclusion: IN-RECORD will provide information on the efficacy and safety of rivaroxaban in combination with APT versus APT in the Indian patient population in real-life clinical scenario.

Investigating Physician Training, Knowledge, and Response to Human Trafficking at an Academic Medical Center in Texas

ABSTRACT Health-care settings are critical for identifying and assisting persons who have experienced human trafficking. Studies estimate that victims have been cared for at multiple clinical sites, but the majority of victimization remains undetected due to lack of awareness, training, and time. This study examined physician human-trafficking knowledge, training, usual practice regarding response, and education modality preferences at an academic medical center. A 24-item survey was distributed to residents and faculty in family medicine, emergency medicine, psychiatry, pediatrics, oral and maxillofacial surgery, and orthopedics. Univariate and bivariate analysis were used to measure associations, demographics, and training across human trafficking (HT) knowledge, training, and usual practice. Respondents were largely identified as female (71%) and trainees (68%). Most respondents specialized in family medicine (32%), pediatrics (30%), or emergency medicine (19%). Overall, 85% of participants agreed that recognizing HT is a physician’s responsibility, however only 31% felt comfortable discussing HT with patients. Knowledge of HT was generally higher among attendings compared to residents (p-value = .04). More than half of participants (60%) reported never receiving HT or trauma-informed care training, but most reported that standard training should include HT (86% agree or strongly agree). Sixty-four percent of clinicians reported a preference of training administered through webinars or during didactic sessions. Physicians across various training levels agree that recognizing HT is a key responsibility. Standard medical training should incorporate HT and trauma-informed care and address knowledge gaps in order to equip clinicians to better detect, treat, and refer victims for the care they need. This study supports progress toward the UN Sustainable Development Goal 16 promoting just, peaceful and inclusive societies.

Exome Sequencing and the Identification of New Genes and Shared Mechanisms in Polymicrogyria

Polymicrogyria is the most commonly diagnosed cortical malformation and is associated with neurodevelopmental sequelae including epilepsy, motor abnormalities, and cognitive deficits. Polymicrogyria frequently co-occurs with other brain malformations or as part of syndromic diseases. Past studies of polymicrogyria have defined heterogeneous genetic and nongenetic causes but have explained only a small fraction of cases. To survey germline genetic causes of polymicrogyria in a large cohort and to consider novel polymicrogyria gene associations. This genetic association study analyzed panel sequencing and exome sequencing of accrued DNA samples from a retrospective cohort of families with members with polymicrogyria. Samples were accrued over more than 20 years (1994 to 2020), and sequencing occurred in 2 stages: panel sequencing (June 2015 to January 2016) and whole-exome sequencing (September 2019 to March 2020). Individuals seen at multiple clinical sites for neurological complaints found to have polymicrogyria on neuroimaging, then referred to the research team by evaluating clinicians, were included in the study. Targeted next-generation sequencing and/or exome sequencing were performed on probands (and available parents and siblings) from 284 families with individuals who had isolated polymicrogyria or polymicrogyria as part of a clinical syndrome and no genetic diagnosis at time of referral from clinic, with sequencing from 275 families passing quality control. The number of families in whom genetic sequencing yielded a molecular diagnosis that explained the polymicrogyria in the family. Secondarily, the relative frequency of different genetic causes of polymicrogyria and whether specific genetic causes were associated with co-occurring head size changes were also analyzed. In 32.7% (90 of 275) of polymicrogyria-affected families, genetic variants were identified that provided satisfactory molecular explanations. Known genes most frequently implicated by polymicrogyria-associated variants in this cohort were PIK3R2, TUBB2B, COL4A1, and SCN3A. Six candidate novel polymicrogyria genes were identified or confirmed: de novo missense variants in PANX1, QRICH1, and SCN2A and compound heterozygous variants in TMEM161B, KIF26A, and MAN2C1, each with consistent genotype-phenotype relationships in multiple families. This study's findings reveal a higher than previously recognized rate of identifiable genetic causes, specifically of channelopathies, in individuals with polymicrogyria and support the utility of exome sequencing for families affected with polymicrogyria.

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

SARS-CoV-2 Detection and Culture in Different Biological Specimens from Immunocompetent and Immunosuppressed COVID-19 Patients Infected with Two Different Viral Strains.

Introduction-The dynamics of SARS-CoV-2 shedding and replication in humans remain incompletely understood. Methods-We analyzed SARS-CoV-2 shedding from multiple sites in individuals with an acute COVID-19 infection by weekly sampling for five weeks in 98 immunocompetent and 25 immunosuppressed individuals. Samples and culture supernatants were tested via RT-PCR for SARS-CoV-2 to determine viral clearance rates and in vitro replication. Results-A total of 2447 clinical specimens were evaluated, including 557 nasopharyngeal swabs, 527 saliva samples, 464 urine specimens, 437 anal swabs and 462 blood samples. The SARS-CoV-2 genome sequences at each site were classified as belonging to the B.1.128 (ancestral strain) or Gamma lineage. SARS-CoV-2 detection was highest in nasopharyngeal swabs regardless of the virus strain involved or the immune status of infected individuals. The duration of viral shedding varied between clinical specimens and individual patients. Prolonged shedding of potentially infectious virus varied from 10 days up to 191 days, and primarily occurred in immunosuppressed individuals. Virus was isolated in culture from 18 nasal swab or saliva samples collected 10 or more days after onset of disease. Conclusions-Our findings indicate that persistent SARS-CoV-2 shedding may occur in both competent or immunosuppressed individuals, at multiple clinical sites and in a minority of subjects is capable of in vitro replication.