A Quality Improvement Initiative Using EPIC Smartsets to Improve Adherence to AAP Guidelines for Obesity Management at a Primary Care Pediatrics Clinic

Purpose/Objectives: Effective management of childhood obesity is critical to prevent long-term medical and psychosocial sequelae. In 2015, the AAP issued guidelines on monitoring body mass index (BMI) and providing comprehensive obesity care based on risk factors. However, literature demonstrates that physician adherence to these guidelines is often poor. Electronic health record (EHR) clinical decision support tools can be effective in standardizing weight management. Utilizing EPIC SmartSets to improve physician adherence to AAP obesity management guidelines, we aimed to increase by 30% in 6 months the following: formal diagnosis of elevated BMI, frequency of weight follow-up visits, adherence to recommended lab screening, and subspecialty referrals. Design/Methods: Pre- and post-intervention surveys were distributed to residents/faculty at an academic primary care clinic to identify variability in practice and barriers to guideline adherence, which informed intervention designs. Cycle 1: SmartSets were implemented in July 2020 with diagnosis codes, note templates, readiness to change surveys, recommended lab and referral orders, patient handouts/questionnaires, and follow-up visit suggestions. Education was completed for providers. Cycle 2: Based on end-user input, SmartSets were integrated into preexisting well-visit templates rather than requiring separate workflow. Analysis metrics included the percentage of: well-visits with an appropriate diagnosis of elevated BMI, acute visits designated as weight follow-ups, and weight or well-visits in which labs were ordered or subspecialty referrals placed. All patients with BMI 85-94.9%ile (overweight) and BMI ≥95%ile (obese) ages 2-17.9 years old seen from 7/1/2019 to 3/31/2021 were included. Data was plotted on run/control charts to assess trends after implementation and revision. Results: A total of 748 overweight patients and 669 patients with obesity were seen during this timeframe. There was a sustained increase in appropriate diagnosis of elevated BMI from an average of 49% pre-intervention to 71% postintervention (Fig. 1), surpassing our aim. There were no significant trends in the percentage of weight visits, labs, or referrals. Appropriate utilization of the implemented EHR tools for well-visits improved after second cycle revisions (39% to 88%). Provider-perceived barriers to AAP guideline adherence included lack of family willingness to participate in management, lack of visit time, and socioeconomic factors out of the provider's control (Fig. 2). Conclusion/Discussion: The first step to instigate practice changes is through problem identification. By utilizing end-user feedback and preserving clinical workflows, the incorporation of AAP guidelines into EPIC SmartSets improved the diagnosis of elevated BMI during well-visits. However, due to COVID-19, it is unclear whether lab orders, referrals, or weight follow-ups improved. Additional EPIC modifications, such as auto-populated lab results, could minimize the need to chart review and thus improve these behaviors. While we demonstrated improved physician recognition, more studies are warranted to address the complex challenges primary care providers and families face regarding weight management. - Control Chart for BMI Diagnoses Made at Applicable Well Child Checks (WCC) by Month Percent of patients with elevated BMI seen at a well-visit from July 2019 through March 2021 who were formally given the diagnosis of elevated BMI. Goal to increase appropriate diagnoses by 30%. -Pareto Chart of Perceived Barriers to Adherence to AAP Guidelines for Weight Management Based upon surveys of residents and faculty at the academic pediatrics clinic studied.

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses;however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health. The COVID-19 pandemic sparked the need for international collaboration in using clinical data for rapid development of diagnosis and treatment methods. But the sensitive nature of medical data requires special care and ideally potentially sensitive data would not leave the organization which collected it. Xiang Bai and colleagues present a privacy-preserving AI framework for CT-based COVID-19 diagnosis and demonstrate it on data from 23 hospitals in China and the United Kingdom.

Reactive force field-based molecular dynamics simulation of the interaction between plasma reactive oxygen species and the receptor-binding domain of the spike protein in the capsid protein of SARS-CoV-2

Under the pressures of the current global pandemic, researchers have been working hard to find a reliable way to suppress infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and prevent the spread of COVID-19. Studies have shown that the recognition and binding of human angiotensin-converting enzyme 2 by the receptor-binding domain (RBD) of the spike protein on the surface of SARS-CoV-2 is a crucial step in viral invasion of human receptor cells, and blocking this process could inhibit the virus from invading normal human cells. Plasma treatment can disrupt the structure of the RBD and effectively block the binding process. However, the mechanism by which plasma blocks recognition and binding is not clear. In this study, the reaction between reactive oxygen species (ROS) in plasma and a molecular model of the RBD was simulated using a reactive molecular dynamics method. The results showed that the destruction of the RBD by ROS was triggered by hydrogen ion reactions: O and OH ed H atoms from the RBD, while the H atoms of H2O2 and HO2 were ed by the RBD. This hydrogen ion resulted in the breakage of C-H, N-H, O-H and C=O bonds and the formation of C=C and C=N bonds. The addition reaction of OH increased the number of O-H bonds and caused the formation of C-O, N-O and O-H bonds. The dissociation of N-H bonds led to the destruction of the original peptide bond structure and amino acid residues, changed the type of amino acid residues and caused the conversion of N-C and N=C and C=O and C-O. The simulation partially elucidated the microscopic mechanism of the interaction between ROS in plasma and the capsid protein of SARS-CoV-2, providing theoretical support for the control of SARS-CoV-2 infection by plasma, a contribution to overcoming the global pandemic.

Screening strategy of TMPRSS2 inhibitors by FRET-based enzymatic activity for TMPRSS2-based cancer and COVID-19 treatment

Transmembrane serine protease (TMPRSS2) plays an oncogenic role in prostate cancer as the fusion gene with ERG, and has also been demonstrated to be essential for the cellular entry of severe acute respiratory syndrome coronaviruses (SARS-CoV). Thus, targeting TMPRSS2 is a promising strategy for therapies against both prostate cancer and coronavirus infection. Although Nafamostat and Camostat have been identified as TMPRSS2 inhibitors, severe side effects such as cerebral hemorrhage, anaphylactoid reaction, and cardiac arrest shock greatly hamper their clinical use. Therefore, more potent and safer drugs against this serine protease should be further developed. In this study, we developed a fluorescence resonance energy transfer (FRET)-based platform for effectively screening of inhibitors against TMPRSS2 protease activity. The disruption of FRET between green and red fluorescent proteins conjugated with the substrate peptide, which corresponds to the cleavage site of SARS-CoV-2 Spike protein, was measured to determine the enzymatic activity of TMPRSS2. Through an initiate pilot screening with around 100 compounds, Flupirtine, a selective neuronal potassium channel opener, was identified as a potential TMPRSS2 inhibitor from an FDA-approved drug library by using this screening platform, and showed inhibitory effect on the TMPRSS-dependent infection of SARS-CoV-2 Spike-pseudotyped lentiviral particles. This study describes a platform proven effective for rapidly screening of TMPRSS2 inhibitors, and suggests that Flupirtine may be worthy of further consideration of repurposing to treat COVID-19 patients.

Building an Online Learning Question Map Through Mining Discussion Content

Information and communication technology (ICT) has been widely accepted in education since the COVID-19 outbreak. Today, the convenience that ICT provides in education makes learning independent of time and place. However, compared to face-to-face learning, ICT online learning has the difficulty of finding student questions efficiently. One of the ways to solve this problem is through finding their questions from the online discussion content. With online learning, teachers and students usually send out questions and receive answers on a discussion board without the limitations of time or place. However, because liquid learning is quite convenient, people tend to solve problems in short online texts with a lack of detailed information to express ideas in an online environment. Therefore, the ICT online education environment may result in misunderstandings between teachers and students. For teachers and students to better understand each other’s views, this study aims to classify discussions into a hierarchical structure, named a question map, with several types of learning questions to clarify the views of teachers and students. In addition, this study attempts to extend the description of possible omissions in short texts by using external resources prior to classification. In brief, by applying short text hierarchical classification, this study constructs a question map that can highlight each student’s learning problems and inform the instructor where the main focus of the future course should be, thus improving the ICT education environment. © 2020, Springer Nature Switzerland AG.

Considerations on the clinical trials of mesenchymal stem cells for the treatment of novel coronavirus infected pneumonia in patients

Coronavirus disease 2019 (COVID-19) occurred in several countries since the end of 2019. Some infected patients have severe complications, such as acute respiratory distress syndrome and multiple organ dysfunction syndrome. Effective treatment methods are urgently needed. Several clinician groups are attempting to apply stem cells in seriously or critically ill patients with novel coronavirus infected pneumonia. In this paper, we briefly discuss some related issues, such as subject selection, safety and efficacy evaluation, as well as risk management, which should be concerned during the clinical trials based on the defects in clinical trial protocols, and thus provided some advice for investigators. © 2020, Chinese Journal of New Drugs Co. Ltd. All right reserved.