title
stringlengths 1
1.19k
| keywords
stringlengths 0
668
| concept
stringlengths 0
909
| paragraph
stringlengths 0
61.8k
| PMID
stringlengths 10
11
|
|---|---|---|---|---|
Instructor satisfaction with the blended technique | Instructors teaching the blended approach reported higher satisfaction levels, with 84% preferring this over the traditional 4-step technique. This mirrors the positive attitudes towards blended learning seen in other studies [The current study noted lower candidate satisfaction in the research group, possibly due to instructor inexperience with the new approach. Some instructors struggled with the facilitator role or the standardised teaching content. | PMC10644658 |
||
Duration of the teaching session | Contrary to reports of worsened performance with reduced face-to-face time [ | PMC10644658 |
||
Limitations | Although a standardised checklist was used which closely resembled the long-used ETC assessment criteria for a pelvic binder application and found consensus of an expert ETC panel, it was not officially validated, nor evaluated for its reliability. Face validity of the checklist appeared high, as 96% of the candidates and all the instructors rated the assessment with the checklist as objective.The study results may not be generalisable to other technical skills, as effectiveness could be linked to skill complexity and specificity, and further evaluation is needed to confirm this.Finally, the results may not be applicable to the entire ETC population, as the study was only conducted in two countries, Belgium and England, which could limit the universality of the findings. | PMC10644658 |
||
Conclusions | This multicentre, randomised controlled study evaluated the role of blended learning for teaching the application of a pelvic binder in the ETC, using a blended modification of the 4-step technique [Future research should focus on the role of blended learning for skills with different complexity as the effectiveness of the 4-step technique and the blended modification, could be related to skill complexity [As the blended approach requires the instructors to change from information providers to facilitators [The findings of this study contribute to the growing body of evidence that demonstrates the effectiveness of the flipped classroom approach in transferring elements of skill learning to the virtual space [ | PMC10644658 |
||
Acknowledgements | Trauma | This manuscript is based on a dissertation for Master in Medical Education at the University of Dundee. Many thanks go to the supervisor Bonnie Lynch, for her expert advice and great support and to Judy Scopes for her valuable review. We would also like to thank the European Trauma Course Organisation for supporting this study and all candidates, faculty and course organisers of the ETC Antwerpen 14th–16th September 2022, ETC Preston 21st–23rd September 2022, ETC Birmingham 28th–30th September 2022, ETC Stoke 12th–14th October 2022 and ETC Ghent 19th–21st October 2022 for their participation and enthusiastic support. Finally, our gratitude goes to Johannes Grafe and Felix Oelrich for their support with the production of online video materials. | PMC10644658 |
|
Author contributions | EB was responsible for the study conception, design, data collection, analysis and the first draft. AB helped with the data collection and reviewed the manuscript. KT helped with the production of online materials and reviewed the manuscript. All authors read and approved the final draft. | PMC10644658 |
||
Funding | Open Access funding enabled and organized by Projekt DEAL. Open access was funded by Universität Bielefeld. | PMC10644658 |
||
Availability of data and materials | All data and additional materials can be provided by the corresponding author upon reasonable request. | PMC10644658 |
||
Declarations | PMC10644658 |
|||
Ethical approval and consent to participate | This study was approved by the Ethics Committee of the Schools of Medicine and Life Sciences Research of the University of Dundee (REC number 22/59, 28 | PMC10644658 |
||
Consent for publication | All participants gave written consent for the study data to be published. | PMC10644658 |
||
Competing interests | EB was lead educator of the ETCO from 2019 to Jan 2023. AB is educational developer of the ETCO. KT is chairman of the ETCO. | PMC10644658 |
||
References | PMC10644658 |
|||
1. Introduction | Type 2 Diabetes, T2D, CM, diabetes | TYPE 2 DIABETES, RECRUITMENT, DIABETES | In-person culinary medicine (CM) can improve health behaviors, but its translation to virtual platforms and impact on diabetes outcomes are not well described. We designed a pragmatic trial comparing the effectiveness of virtual CM (eCM) to Medical Nutrition Therapy on diabetes outcomes among patients with uncontrolled diabetes within a safety-net healthcare system. All participants were provided cooking equipment and food from a food pantry. Due to low initial eCM participation, recruitment was paused, and eight semi-structured interviews were conducted to solicit feedback on study appeal, operations, and barriers to participation. Rapid thematic analysis was used to modify study operations. We found that participants were interested in the study and motivated by health concerns. While they valued food distribution and cooking equipment, they highlighted transportation barriers and conflicts with the pick-up time/location. Some eCM participants expressed discomfort with the virtual platform or preferred to observe rather than cook along. Study operations were modified by (1) moving supply pick-up to a familiar community clinic and diversifying food pick-up locations; (2) offering an in-person orientation to the program to increase comfort with the virtual platform; (3) emphasizing the credibility and relatability of the eCM instructor and encouraging participation of family members. This redesign led to the recruitment of 79 participants, of whom 75% attended at least one class. In conclusion, participant feedback informed pragmatic changes in study operations that increased engagement in this ongoing trial and may inform future eCM program design.People with Type 2 Diabetes (T2D) face daily challenges in diabetes self-management, including healthy eating, being active, glucose monitoring, and medication management [Culinary Medicine delivered using an RD offers an alternative approach to MNT that provides both nutrition education and experiential cooking. By learning nutritional principles in a “hands-on” manner, participants can improve diet variety and healthy cooking practices and build confidence toward food-related behavior change and kitchen self-efficacy [While initial studies of culinary medicine have promising findings, randomized studies in food-insecure patients with diabetes [We designed a pragmatic trial comparing the effectiveness of virtual culinary medicine and clinic-based medical nutrition therapy on T2D outcomes. Patients with uncontrolled T2D were recruited from a local safety-net clinic system with high rates of food insecurity among the patient population. This paper describes the initial recruitment and engagement challenges within the randomized trial of virtual culinary medicine and medical nutrition therapy, the implementation of redesign strategies based on participant and stakeholder feedback, and the impact of these changes on recruitment and engagement in an ongoing RCT. | PMC10574259 |
2. Materials and Methods | PMC10574259 |
|||
2.1. Study Setting | diabetes | DIABETES | The highest incidence of estimated diabetes prevalence in North Texas is concentrated in South Dallas and is likely exacerbated by food insecurity [ | PMC10574259 |
2.2. Study Design | diabetes | DIABETES | We designed a pragmatic randomized controlled trial (RCT) to compare the effectiveness of hands-on, experiential cooking classes (culinary medicine intervention) and standard of care, clinic-based medical nutrition therapy for patients with uncontrolled diabetes. This study was approved by the UT Southwestern Medical Center Institutional Review Board (STU 200-1244). Patients were enrolled in the trial for 6 months of active intervention, and an additional 6 months of post-intervention follow-up to evaluate for sustained behavioral and biometric changes. All participants received kitchen utensils and equipment and information about local food assistance. In its original design, we proposed in-person enrollment and cooking classes at a local food pantry and teaching kitchen in a community center within 2 miles of the clinic. However, the COVID-19 pandemic necessitated a shift to virtual visits where culinary medicine classes were delivered via Zoom Cloud Meetings, v5.13.11 [ | PMC10574259 |
2.3. Study Eligibility | Diabetes, diabetes | TYPE 2 DIABETES, DIABETES, DIABETES | Patients were identified from Parkland’s Type 2 Diabetes Registry, which captures patients using a combination of ICD codes and laboratory data. Those with an assigned primary care provider at the Parkland COPC clinic in South Dallas were eligible if they (1) had a primary care visit in the past 12 months, (2) were ages 18 years or older, (3) had a duration of diabetes > 12 months, and (4) had an HbA1c ≥ 7.0% in the past 3 months. Patients who completed a nutrition visit in the past 12 months, did not speak English or Spanish, had an eGFR ≤ 30, or were on dialysis were excluded. We initially planned to identify eligible patients after completion of the Healthy Living with Diabetes Program, Parkland’s American Diabetes Association-recognized diabetes self-management education program [ | PMC10574259 |
2.4. Recruitment Strategy | RECRUITMENT | Eligible patients were sent a study invitation letter and information sheet in their preferred language (English or Spanish). A research assistant followed up by phone to confirm eligibility and complete recruitment. Due to the shift to virtual classes, a screening question about internet access was added to the inclusion criteria. Interested patients provided verbal informed consent and completed a baseline telephone survey in their preferred language. After we recruited enough patients (i.e., 30–40) to form a class, each language/class grouping was randomized 1:1 to a treatment arm. During the three-year study period, we randomized three class groupings, each exposed to the intervention for 6 months, followed by an additional 6-month follow-up period. | PMC10574259 |
|
2.5. Participant Enrollment and Food Assistance | Participants were notified by phone of their study arm assignment and scheduled to meet with the study team to complete their enrollment process at an established South Dallas community organization with an on-site food pantry and kitchen suitable for culinary medicine classes. With written informed consent to release their name, we also connected participants to food assistance services. Because food pantry clients often lack kitchen utensils and equipment to cook at home [ | PMC10574259 |
||
2.6. Development of Medical Nutrition Therapy Arm (MNT) | diabetes, Diabetes | DIABETES, DIABETES | We partnered with Parkland Clinical Nutrition to deliver the MNT intervention. Participants randomized to MNT were offered a series of six sessions based on the American Diabetes Association (ADA) recommended Medical Nutrition guidelines for diabetes education [ | PMC10574259 |
2.7. Development of Electronic Culinary Medicine Arm (eCM) | Diabetes | DIABETES | In partnership with culinary medicine-trained clinicians (JA, MDS), our study team accessed the Diabetes and Carbohydrate training module from the Health Meets Food curriculum [ | PMC10574259 |
2.8. Initial Study Launch Review | RECRUITMENT | After the study launch (November 2021), weekly team meetings to monitor progress identified recruitment difficulties and low initial attendance for the first English and Spanish eCM groups (0 and 2 participants, respectively). In January 2022, we paused the study to solicit team-based observational feedback based on phone interactions with patients, review of recruitment notes, enrollment protocols, class structure, and engagement methods. We invited enrolled participants from the first group to participate in an interview to solicit their feedback on study operations. We conducted semi-structured interviews ( | PMC10574259 |
|
3. Results | PMC10574259 |
|||
3.1. Study Recruitment and Enrollment Redesign | RECRUITMENT | Interview data and study team observational feedback on original elements of study design helped narrow and identify change opportunities and strategies (These challenges, combined with scheduling and coordination challenges for the study team, led us to implement an opt-in model for food assistance. This change negated the need for written consent and allowed us to streamline recruitment by collecting verbal consent during the initial phone recruitment call. Newly enrolled participants were provided a curated list of food assistance resources available in their ZIP Code and were encouraged to connect with resources convenient for them as needed. Initial study participants who wished to continue seeking services were encouraged to do so with our community organization directly or were provided with the same food assistance list as part of our transition plan. Since our community organization was no longer a centralized location for study onboarding, we moved onboarding procedures for eCM participants to the clinic where they received their primary care. Given the positive interview feedback about MNT, fewer changes were suggested and implemented for the MNT comparison arm aside from picking up their cooking supplies from the RDs at their clinic instead of the community organization. | PMC10574259 |
|
3.2. eCM Engagement and Redesign | Participants appreciated and reinforced the importance of reminders for eCM classes. Common reasons for non-attendance included family emergencies and internet connectivity issues. eCM participants highlighted a lack of familiarity with culinary medicine classes and suggested that the opportunity to invite and cook with peers and/or family members would make them feel more comfortable. Similarly, some expressed a preference to observe rather than cook. One participant suggested using a more familiar virtual platform instead of Zoom. Additional feedback and change opportunities are shown in We made several changes to eCM study processes, as shown in | PMC10574259 |
||
3.3. Trial Recruitment Results | MAY, RECRUITMENT, RECRUITMENT | Recruitment occurred during three periods: November–December 2021, May–June 2022, and January–February 2023. The study team applied exclusion criteria during each recruitment period, as shown in | PMC10574259 |
|
3.4. Randomization and Baseline Characteristics | Participants providing verbal consent and completing the baseline survey were randomized 1:1 to a study arm (48 MNT and 52 eCM). Twenty-one (5 MNT and 16 eCM) were unable to be reached following randomization or did not pick up study supplies and were withdrawn by the study team prior to the first class, leaving 43 MNT participants and 36 eCM participants. The baseline characteristics of the 79 enrolled participants are shown in | PMC10574259 |
||
3.5. Participant Engagement | We described how many participants attended at least one class during the six-class intervention period and, more specifically, how many participants attended the first class. Overall, 75% of participants attended at least one class (59/79), and 70% of those attended the first class (55/79). Both metrics were higher in the MNT arm compared with the eCM arm (91% vs. 56% attended at least one class, respectively; 91% vs. 44% attended the first class, respectively). There were especially low rates of first-class attendance (29%) and attending at least one class (47%) among English participants randomized to eCM ( | PMC10574259 |
||
4. Discussion | diabetes | RECRUITMENT, DIABETES | In this pragmatic trial designed to compare the effectiveness of eCM and MNT for patients with uncontrolled diabetes, we faced multiple challenges in recruiting and engaging underserved patients in virtual nutrition interventions. After pausing the study to critically evaluate operational processes and engage participants, study team members, and other key stakeholders, we identified barriers and facilitators to study recruitment and engagement and generated actionable opportunities for process redesign [Although we leveraged our prior experience with the design and delivery of in-person culinary medicine to account for the challenges of executing a virtual intervention in an underserved study population, we encountered technological and logistical barriers to recruitment and engagement. Most patients in our safety net health system have smartphones with video capabilities. However, many eligible participants lacked internet access. Moreover, among those with internet access who enrolled, many lacked a reliable connection capable of supporting the video platform. We did have some success connecting patients with local libraries for hotspot rentals to improve connectivity. Although our initial design provided detailed phone-based guidance on the virtual platform, many patients struggled to access and use the virtual platform to join the class. We were able to help address this barrier by conducting in-person, 1:1 technology tutorials for participants to download, install, and practice using the eCM virtual platform. Additionally, a study team member remained available on standby during the eCM classes to provide support for any unexpected technological difficulties. Although virtual interventions and education programs may help address transportation and childcare barriers [Although we described the culinary medicine intervention as ‘cooking classes’ to participants, participants remained uncertain about what to expect, which was a potential barrier to recruitment and engagement. While some participants were excited by the cooking classes, others were hesitant because they did not feel confident in their cooking or felt comfortable cooking along with the instructors. These findings are similar to other studies suggesting that a lack of interest or knowledge about cooking creates uncertainty and may pose a barrier to participating in cooking classes [Preliminary findings from this redesign indicate that MNT participants had higher engagement at the first class than eCM participants even after changes were implemented; however, participants who joined the first eCM class were more likely to return, especially Spanish speakers. Notably, the eCM intervention was not directly affiliated with the health system, and classes were held in the evenings, which may play a role in eCM’s lower engagement. The MNT intervention, which was delivered using health system RDs at the patient’s primary care clinic during business hours, was more familiar to participants and likely viewed as part of their healthcare. The RDs conducted their own scheduling, reminder, and outreach calls in the case of “no show” appointments, which led to a strong rapport with study participants. Such patient-dietitian relationships are key components of successful dietary interventions; making sessions feel personal and individualized, even when delivered in a group setting, is important for building interest and engagement [Although we initially planned to distribute cooking utensils, recipe binders, and pantry staples at the community organization, low show rates for food pick-up resulted in eCM participants not having the necessary items for their class. This resulted in the differential withdrawal of participants from the eCM arm relative to the MNT arm, which did not require this additional step. Participants identified transportation, lack of familiarity with the community organization, and conflicts with food pick-up times as key barriers. Our original study design was ‘opt out’ for food assistance in collaboration with our community organization. This required a signed informed consent and HIPAA authorization to share participant information with our community organization, which posed a substantial barrier to recruitment via telephone. Given this and the participant barriers to food pick-up, we transitioned to an ‘opt in’ food distribution model, which did not require written consent and gave participants greater choice and flexibility in selecting their food resource. This model also allowed us to move the in-person eCM Orientation meeting to the participant’s primary care clinic, which was familiar and more accessible. We explored the possibility of delivering supplies directly to their home, but we did not have sufficient funding or staffing to support this. Home delivery of supplies and groceries may limit the need for shopping and help alleviate barriers to participation in virtual culinary medicine classes where participants cook in their home kitchens. | PMC10574259 |
5. Conclusions | RECRUITMENT | Recruitment and engagement of underserved populations in virtual culinary medicine and nutrition studies is challenging. Although our study team had substantial experience in the delivery of in-person culinary medicine and nutrition interventions, translating these experiences to virtual platforms presented an array of technological barriers and logistical challenges. Our study is unique in that we enrolled participants in a randomized trial of culinary medicine rather than evaluating outcomes in participants who self-select into such a program. Virtual culinary medicine and nutrition trials require significant infrastructure and support from study teams to facilitate participation in interventions. By engaging study participants, study team members, and community partners, we identified actionable items to improve study processes to successfully recruit and engage participants in both eCM and virtual MNT. | PMC10574259 |
|
Author Contributions | This study was conceptualized and designed by M.E.B., J.A., S.L.P., and M.D.S. Data analysis and interpretation was conducted by M.E.B., P.M.C., C.S.-M., V.C.M., and M.F.M. All authors participated in the drafting and revision of the manuscript. M.E.B. was responsible for funding acquisition. All authors have read and agreed to the published version of the manuscript. | PMC10574259 |
||
Institutional Review Board Statement | The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Institutional Review Board of The University of Texas Southwestern Medical Center (STU2020-1244) on 4 February 2021, with the most recent approval on 27 December 2022. The trial is registered with clinicaltrials.gov NCT#05019274. | PMC10574259 |
||
Informed Consent Statement | Informed consent was obtained from all participants involved in the study. | PMC10574259 |
||
Data Availability Statement | The data are not publicly available due to the ongoing status of the study which is not de-identified at this time. | PMC10574259 |
||
Conflicts of Interest | S.L.P. declares consulting fees from Pfizer (unrelated to this work). M.E.B. declares research support from Boehringer Ingelheim (unrelated to this work). While the study design changes were disclosed and approved by the sponsor, the sponsor had no role in the design, execution, interpretation, or writing of the study. | PMC10574259 |
||
References | mistakes, allergies, Confusion, diabetes | RECRUITMENT, RECRUITMENT, ALLERGIES, EVENTS, DIABETES | Recruitment Flow Diagram, November 2021–February 2023.Participant Class Engagement Metrics After Re-Design by Study Arm and Preferred Language (Comparison of Study Arms with respect to the Role of the Facilitator, Setting, Requirements, Content, and Educational Approach.Original Design with Stakeholder Feedback and Description of Changes.Participants who miss DBC food pick-up cannot sign consent and do not pick up supplies (eCM arm).Participants cannot carry the full two-week supply of food distribution at DBC to the bus stop.Scheduling and coordinating food distribution pick-ups at DBC is cumbersome for the study team and distracts from primary recruitment tasks.Switch to a well-known, more accessible location (their clinic) for participants to meet with study staff.Change to phone-based opt-in for food assistance picks up at community organizations rather than an opt-out model.Allow participants who wish to continue going to DBC to pick up on a weekly basis to scale down the amount of food they must carry.Work with food assistance leaders (Crossroads, Sharing Life, and DBC) to provide a Food Assistance Resource List specific to ZIP code to decentralize efforts and allow participants to choose resources closer to them.DBC location is inconvenient, and participants expressed unfamiliarity/uncertainty with the process of using services.Participants who attend classes are highly motivated by individual benefits and find a way to make it happenEmphasize individual benefits of the class with enrolled participants during the notification calls and reiterate during in-person Orientation meetings (eCM only).Lack of internet-connected devices leading to exclusion of many patientsTarget recruitment to those with email addresses or MyChart access as a proxy for internet connectivity ^.Participants asking MNT staff about food pick up or asking study-specific questionsMeet periodically with RDs to ensure they are aware of study procedures and how to handle study-related questions.Introduce MNT staff RDs to DBC staff and set up a facility tour to familiarize them with local food assistance resources.High level of familiarity and relationship development with nutritionist, dedicated staff with recognizable interaction style (“patient visit”)Easier for participants to join due to flexibility (WebEx link via text, 1:1 scheduling, in-person, or virtual, unlimited reminders), but limited to working hoursGood feedback from participants, which did not require any redesign.Use this visit model to frame the redesign changes for the culinary medicine arm, including permanent instructor staffing, consistent/familiar relationship building with participants, ability to schedule make-up sessions (i.e., rescheduling), participant communication via text message, and texting the link to the class sessions.Not ready to cook when joining the class and prefer to “watch” video rather than cook in class.Would prefer to cook with their family members or friends in the class.Concerns about cooking at home and possible distractions (kids, pets, messy, etc.)Confusion about what to expect (missed pick up of supplies), what culinary medicine is, why, and how they should join.Unfamiliar with teacher/classmates, class format, timid about joining the class.Internet-connected device they have available to them is too small to see a video screen; or their internet service and/or connection is not strong enough for videoconferencing.Add a “watch only” option for the first class for those who are not ready to cook and open classes to peer and family attendance.Send video testimonials from other diabetes patients encouraging participants to do the classes ^.Revise info sheet and call scripts for clear and consistent messaging about the study goals, benefits, and expectations.Meet each participant in-person at a familiar location (i.e., their clinic) to conduct in-depth eCM Orientation meetings.Develop eCM Orientation Checklist to assess comfort level, identify household barriers, establish rapport and importance of class attendance, emphasize individual benefits and socialization, discuss comfort level with the first recipe (Tacos), and deliver a “tech module” for personalized app installation and teach-back learning of Zoom platform.Develop a Chef Biography and persona that participants can get excited to cook with and can relate to.Select “Sous Chefs” to assist with class and model desired behavior for participants, for example, asking questions, making mistakes, having messy kitchens, etc.Purchase participant tablets to ensure everyone has a large viewing screen for class participation ^.Share public library resource info sheet for internet hotspot rental program.Initial class attendance low. Participants experience events day-to-day that prevent them from joining the class, even when the day before, they voiced that they were planning to join.Time in between monthly classes is vast, and a lot can happen.Personal touch is important to participants before, during, and after intervention.Add personalized “check-in” calls in between classes to ask how they are doing, reiterate class material, see if they have attempted the recipe again, encourage them to submit a photo of the meal to share with the class, and ensure they are prepared for the upcoming class. Use this opportunity to discuss food preferences and substitutions for upcoming classes on a 1:1 basis outside of the group setting.Text the direct link with more engaging messaging: “Class starts in 2 h! Let us get cooking!—Chef Miguel”.Use WhatsApp or WebEx as more familiar platforms ^.Include a copy of the shopping list for the next class along with the grocery stipend they receive by mail to make sure they are prepared to cook and know what we are making.The written material presents a food literacy concern for participants, especially Spanish speakers, who are unfamiliar with recipes, cooking utensils, terminology.Contract bilingual, culturally competent RD for consistency across English and Spanish groups and for the duration of the class (6 months).Provide pictures of the finished meal in addition to the step-by-step recipes and ingredients list ^.Use in-person Orientation meetings to review each ingredient in the shopping list together with the participants in their native language to identify what they already have at home vs. what they need to buy before the class. Discuss their baseline understanding of the first recipe and potential obstacles to joining the class or preparing the ingredients (e.g., allergies, cheese affordability and preferences, etc.).* DBC = Dallas Bethlehem Center, our community-based partner for food pantry distribution. ^ Concepts that were suggested by stakeholders but were not implemented.Participant Characteristics at Baseline by Study Arm (* Self-Reported during baseline survey. | PMC10574259 |
Background | DISEASE, RESPIRATORY INSUFFICIENCY, INFLAMMATORY RESPONSE | Edited by: Gerardo Guillen, Center for Genetic Engineering and Biotechnology (CIGB), CubaReviewed by: Marko Lucijanic, Clinical Hospital Dubrava, Croatia; Massimiliano Bonifacio, University of Verona, Italy†These authors have contributed equally to this workThis article was submitted to Cytokines and Soluble Mediators in Immunity, a section of the journal Frontiers in ImmunologyManaging the inflammatory response to SARS-Cov-2 could prevent respiratory insufficiency. Cytokine profiles could identify cases at risk of severe disease. | PMC10151807 |
|
Methods | respiratory insufficiency | RESPIRATORY INSUFFICIENCY | We designed a randomized phase II clinical trial to determine whether the combination of ruxolitinib (5 mg twice a day for 7 days followed by 10 mg BID for 7 days) plus simvastatin (40 mg once a day for 14 days), could reduce the incidence of respiratory insufficiency in COVID-19. 48 cytokines were correlated with clinical outcome. | PMC10151807 |
Participants | DISEASE, COVID-19 INFECTION | Patients admitted due to COVID-19 infection with mild disease. | PMC10151807 |
|
Results | death, CL-1 | Up to 92 were included. Mean age was 64 ± 17, and 28 (30%) were female. 11 (22%) patients in the control arm and 6 (12%) in the experimental arm reached an OSCI grade of 5 or higher (p = 0.29). Unsupervised analysis of cytokines detected two clusters (CL-1 and CL-2). CL-1 presented a higher risk of clinical deterioration vs CL-2 (13 [33%] vs 2 [6%] cases, p = 0.009) and death (5 [11%] vs 0 cases, p = 0.059). Supervised Machine Learning (ML) analysis led to a model that predicted patient deterioration 48h before occurrence with a 85% accuracy. | PMC10151807 |
|
Conclusions | Ruxolitinib plus simvastatin did not impact the outcome of COVID-19. Cytokine profiling identified patients at risk of severe COVID-19 and predicted clinical deterioration. | PMC10151807 |
||
Trial registration | PMC10151807 |
|||
Background | cancer | VIRUS, SARS-COV-2 INFECTION, CANCER, DISEASE, CYTOKINE STORM | COVID-19 remains an important health problem worldwide three years after its initial description (Cytokine storm due to SARS-CoV-2 infection is a critical step in mild and severe disease (Janus Kinase (JAK) are essential proteins involved in immune response and could play a role in the hyperinflammatory state in patients with COVID19.To date, two JAK inhibitors, baricitinib and tofacitinib, have communicated positive results in randomized clinical trials (Ruxolitinib is a selective JAK 1/2 inhibitor that has shown conflicting results in COVID19. Even though several single arm studies have pointed towards a benefit with this drug, a phase III randomized trial has not confirmed such promising activity (Simvastatin is a lipid lowering agent with anti-inflammatory properties. Investigators of our group have shown that this compound can block virus internalization mediated by clatrin in cancer models (Finally, it has been proposed that patients taking simvastatin could present a more favorable outcome when developing COVID-19 (Thus, we hypothesized that the combination of ruxolitinib plus simvastatin could present a synergistic effect and prevent respiratory and clinical worsening in COVID-19 patients.As mentioned, cytokine storm seems to be the main cause of severe disease in COVID-19 (Thus, we used unsupervised clustering and machine learning methodologies to define a minimal number of cytokine determinations that could accurately identify patients at risk of severe COVID and predicted clinical deterioration 48 hours before occurring. | PMC10151807 |
Objectives | toxicity | DISEASE | The primary objective of this study was to compare the number of COVID19 patients who progressed to severe disease (defined as grade 5 or more of the OSCI) in the control vs the experimental arm.Secondary objectives were to compare ICU admission and length of stay, days of hospitalization, and mortality at 28 days, 6 months and 12 months after randomization between study arms. Also, we described the toxicity profile of the combination of ruxolitinib plus simvastatin.Finally, we aimed to study the evolution of cytokines in plasma along treatment, define a cytokine signature in plasma predictive of COVID-19 outcome and to develop a Machine Learning (ML) algorithm able to predict patient deterioration. | PMC10151807 |
Methods | We designed a randomized, single-center phase II clinical trial. Patients were allocated in a 1:1 ratio to the control or experimental arm.Randomization was stratified based on concurrent treatment with statins or strong CYP 340 inhibitors. | PMC10151807 |
||
Participants | DISEASE, COVID-19 INFECTION | Eligible cases were adult patients admitted to our institution due to COVID-19 infection. They must have presented with mild disease (defined as grade 3 or 4 in the WHO-Ordinal Scale for Clinical Improvement [WHO-OSCI]) and provided consent (The study was performed at the Hospital Universitario Sanchinarro, Madrid (Spain).Treatment in the control arm consisted of standard of care (SOC), including corticosteroids, tocilizumab, heparin and any other therapy considered appropriate by clinical investigators.The experimental arm consisted of SOC plus the combination of ruxolitinib 5 mg twice a day for 7 days followed by 10 mg BID for 7 days plus Simvastatin 40 mg once a day for 14 days.Early dose scalation and higher doses of ruxolitinib were permitted on physicians’ discretion. Crossover to the ruxolitinib arm was allowed and continuation of treatment beyond 14 days was also permitted if patients were considered to obtain clinical benefit by the treating physicians.Patients taking statins before hospital admission, continued with the original medication. Those assigned to the experimental arm received ruxolitinib as referred. | PMC10151807 |
|
Outcomes | respiratory insufficiency | DISEASE, RESPIRATORY INSUFFICIENCY | The primary objective was the percentage of patients progressing from mild (grade 3 or 4 in the OSCI) to severe (grade 5 or more) disease. Secondary objectives included days of hospitalization, days of admission in the intensive care unit (when required), survival at 28 days, 6 months and 12 months after study inclusion and safety profile.Based on data from our own institution, the likelihood of developing severe respiratory insufficiency was 50% for hospitalized patients (Patients were allocated in a 1:1 ratio to the control or experimental arm.Cases were stratified based on prior treatment with statins and concomitant treatment with strong CYP 3A4 inhibitors.Block randomization with a block size of 4 and 6 was used. Blocks were generated by the study statistician who was also responsible for allocating patients in a blinded manner. | PMC10151807 |
Statistical methods | Quantitative variables are expressed as mean ± standard deviation when normally distributed and median (IQR) otherwise. Normality was tested using the Shapiro test. Categorical variables are expressed as absolute (relative, %) frequencies.Analysis of the primary objective was reported by treatment arm and performed in both intentions to treat and eligible (“per protocol”) populations. Secondary objectives were only analyzed per protocol.To compare the main study variable, proportion of patients that reached an OSCI grade of 5 or higher, between control and treated group an homogeneity test was performed with the Chi-squared statistic.To compare the study variable SpFiO2 worsening below 300 and clinical improvement between study arms, the Chi-squared test was performed. Fisher's exact test was used for the analysis of the categorical variables clinical worsening, SpFiO2 recovery, number of patients requiring ICU admission and mortality rates at 28 days, 6 months and 12 months.For the continuous variables time to SpFiO2 worsening (< 300), time to clinical improvement, time from randomization to discharge and time to lymphocyte recovery, the Mann-Whitney U test was performed. The t-student test was used for time to SpFiO2 recovery. | PMC10151807 |
||
Cytokine analysis | BLOOD, VIRUS | Up to 444 blood samples from 92 patients were collected. 84 (91%) were participants of the Ruxo-Sim trial and 8 extra cases were included providing they met same eligibility criteria. Clinical information and patient demographics were obtained from the electronic medical records, and confidentiality was maintained by assigning each patient a unique identifier. The study protocol was approved by the Ethics Committee of the institution and all patients provided informed consent. Blood collected in EDTA tubes was centrifuged at 1300 x g for 10 minutes, then plasma was aliquoted and stored at −80°C until testing. To avoid additional exposure of healthcare workers to the virus, blood extractions were performed when indicated in routine practice. | PMC10151807 |
|
Cytokine characterization | 25 µl of neat serum samples from patients were tested using Human Cytokine/Chemokine/Growth Factor Panel A 48 Plex Kit (ref. HCYTA-60K-PX48, Merck KGaA, Darmstadt, Germany) according to the manufacturer’s instructions (see | PMC10151807 |
||
Statistical and machine learning analysis | REGRESSION | Hierarchical consensus clustering based on the most variable cytokines resulted in two clusters of patients with distinct cytokine profiles. A logistic regression model was used to classify patients in CL-1 or CL-2 based on a two-cytokine ratio. This model was tested on the 81 patients with a cytokine characterization in the first three days of hospital admission and the association of both groups with demographic and clinical parameters was assessed. (See SM for details on clustering, logistic regression model and validation procedures).All machine learning (ML) results correspond to two-classes balanced classification problems, tackled through Random Forest models. Input features correspond to cytokine levels, and labels for the two classes are defined according to the patient clinical course, either improving or deteriorating, as measured by laboratory and physical parameters. See SM for details on other ML models, parameter tuning, and validation procedures. | PMC10151807 |
|
Results | PMC10151807 |
|||
Participant flow | RECRUITMENT | 100 patients were included in the trial (“intention to treat” population). Eight cases were deemed ineligible after randomization (six presented an OSCI grade greater than four, one was not a COVID patient, and one was included in a competing clinical trial). Thus, the “per protocol” population included 92 patients. (Flow chart of patient inclusion.Inclusion of cases started in April 2020 and ended in November 2020 after completing the scheduled recruitment. Median follow up was 16 months (range 1-23). | PMC10151807 |
|
Baseline data | obesity, cancer, diabetes | OBESITY, CANCER, CARDIOVASCULAR DISEASE, DIABETES | Mean age was 64 (range 24-98) and 30% were female. Regarding the main clinical prognostic factors in COVID-19: 16% presented diabetes, 13% cardiovascular disease, 12% cancer and 9% obesity. No significant disbalance was observed between study arms regarding these variables (Demographics and basal clinical characteristics.OSCI: WHO-Ordinal Scale for Clinical Improvement *t-Student; ** Chi-squared test,*** Fisher test;**** Mann-Whitney U test.No patient previously treated with a JAK inhibitor was included in the trial and no JAKi, other than ruxolitinib, was administered along the study period. | PMC10151807 |
Outcomes and analysis | toxicity | ADVERSE EVENTS, EVENT, SECONDARY | Regarding the primary objective of the study in the intention to treat population, 11 (22%) patients in the control arm and 6 (12%) in the experimental arm reached an OSCI grade of 5 or higher (p = 0.29).In the “per protocol” population numbers were smaller (p = 0.0002) with 4 (9%) and 7 (16%) in the control and experimental arms respectively (p = 0.52). Worsening of SpFiO2 was observed in 11 (24%) patients in the control arm and 11 (24%) in the experimental arm (p > 0.99). Median time (days) to SpFiO2 worsening (1 [IQR 1-2.5] vs 2 [IQR 1-3]; p=0.43), time to clinical worsening (2.5 [IQR 2-10.8] vs 3.0 [IQR 2.5-10.5]; p = 0.77) and time from randomization to discharge (7.0 [IQR 5.0 - 10] vs 8.0 [IQR 5.0 - 11.8]; p=0.5) was similar in the control and experimental arms (Clinical outcomes. Cumulative distribution for different clinical outcomes (time to event in days): SpFiO2 worsening [< 300] Three patients required admission to the ICU (one in the control and 2 in the experimental arm) and four died (2 in the control and 2 in the experimental arm).Additional analysis included the number and percentage of patients with SpFiO2 recovery and clinical recovery, time to clinical improvement, time to SpFiO2 recovery and time to lymphocyte recovery, with no significant difference between groups (Overall population outcome by study arm.OSCI: WHO-Ordinal Scale for Clinical Improvement *t-Student; ** Chi-squared test, *** Fisher test; **** Mann-Whitney U test.Up to 13 cases (28%) crossed over to the ruxolitinib treatment arm.Median time on ruxolitinib treatment was 11 (9 - 12) days for patients allocated to the experimental arm and 11 (8 – 13.8) days for those who were initially assigned to SOC and crossed over to receive ruxolitinib.All patients who received ruxolitinib reached a dose of 10mg BID or greater.In the overall ‘per protocol’ population, 30% were on statins before study entry (equally distributed in both arms).Also, there were no significant differences regarding the administration of corticosteroids (77%), anticoagulants (97%), tocilizumab (29%) or any other drug (Regarding toxicity, there were no severe adverse events attributed to the experimental treatment and dose interruptions were not required. All secondary effects were equally distributed between the study arms ( | PMC10151807 |
Cytokine analysis | respiratory deterioration, CL-1 | Consensus clustering of the 61 patients who had a measurement on the first day from hospital admission, based on the 17 most variable cytokines, revealed two clusters in the data (Cytokine clustering and patient’s outcome of the study population. Orange: Cluster 1; purple: cluster 2. We used the MIP-1α/M-CSF model to classify all 81 patients with cytokine characterization in the first three days from hospital admission. 44 patients were classified as cluster 1 (CL-1) and 37 as cluster 2 (CL-2). No difference regarding cluster distribution was observed between patients on ruxolitinib plus simvastatin or SOC (To study the expression of individual cytokines in both clusters, we performed linear mixed effects models (When we applied the model to all the measurements performed during the hospital stay of each patient, we found that 8 (18%) of CL-1 did not switch to CL-2. Among those, 1 (13%) was admitted to ICU, 2 (25%) died and 1 (13%) required hospital readmission immediately after discharge due to respiratory deterioration. On the other hand, 21 (57%) of CL-2 did not switch to CL-1, all of them were discharged (We then analyzed how machine learning models can be trained to classify patients, and what information about the underlying cytokine expression they can yield. For that, a set of models have been trained over balanced sets of patients, according to their future clinical course (see SM for details). The such clinical outcome has been evaluated using three routine clinical parameters: the SpFiOAn example of the resulting classification is presented in Results of the machine learning analysis and models. Panel (b) of | PMC10151807 |
|
Discussion | SARS-Cov-2 infection, death, multiorgan dysfunction, Hyperinflammation, RDS, ill, deaths, CL-1 | RESPIRATORY FAILURE, DISEASE, COMPLICATION, RESPIRATORY DISTRESS SYNDROME, CORONAVIRUS, SEVERE ACUTE RESPIRATORY SYNDROME | We present the results of a single-center randomized phase II clinical trial with the combination of ruxolitinib and simvastatin for the treatment of COVID19. Up to 100 hospitalized patients were included and allocated in a 1:1 ratio to SOC or the experimental arm. Treatment was well tolerated but no differences were found regarding the primary objective, number of cases progressing to grade 5 or higher of the OSCI. Thus, the administration of ruxolitinib and simvastatin did not impact the outcome of COVID19.A comprehensive cytokine profiling was also completed. Up to 48 cytokines were determined in peripheral blood at baseline and during hospital stay. The ratio MIP-1α/M-CSF reliably classified cases in poor (cluster 1 [CL-1]) and good (cluster 2 [CL-2]) prognosis groups. CL-1 presented a higher likelihood of worsening SpFiOAdditionally, a decision tree was designed by ML algorithms that accurately predicted patient deterioration 48 hours before occurring. Both tools, baseline immunoprofiling, and decision tree, could greatly help in the early recognition of patients at risk of severe disease and the precise moment where more intensive care is required.Although vaccines have dramatically changed the outcomes of patients, the pandemic is far from being controlled since new variants are emerging (Hyperinflammation is an over exaggerated reaction of the immune system due to the SARS-Cov-2 infection that can lead to multiorgan dysfunction and particularly to a respiratory distress syndrome (RDS). This complication is responsible for most deaths and a major cause of long-term effects caused by COVID (In this setting, dexamethasone has demonstrated an impact on overall mortality in a randomized phase III clinical trial and has been adopted as standard of care (JAK inhibitors have also been proposed as potential alternatives in precluding hyperinflammation. Baricitinib and tofacitinib have communicated positive results in phase III randomized trials (named ACTT-2 and STOP-COVID respectively), while ruxolitinib has not (RUXCOVID trial).RUXCOVID was an international trial of ruxolitinib plus standard of care versus placebo plus standard of care in patients with COVID-19. Patients who were hospitalised but not on mechanical ventilation were randomly assigned (2:1) to oral ruxolitinib 5 mg twice per day or placebo for 14 days. 432 were included in the study. The primary objective (a composite of death, respiratory failure, or ICU care) was not met.The RUXCOVID-DEVENT trial compared the activity of two doses of ruxolitinib (5 mg or 15mg twice a day) versus placebo in more than 200 cases hospitalized with severe acute respiratory syndrome due coronavirus 2 (Finally, a single blinded phase II randomized trial that included 43 cases allocated 1:1 to ruxolitinib or placebo did not show significant differences either (Though our study did not reach positive results, some limitations must be discussed.First, our work represents a smaller population compared to prior trials (1067 in ACTT-2, 287 in STOP-COVID and 432 in RUXCOVID) and was not placebo controlled. Thus, our design could be underpowered to identify statistically significant differences.Secondly, we decided to focus on moderate disease excluding patients with an OSCI grade of 5 or higher. This is an important factor since corticosteroids, IL-6 antagonists and JAKi have shown a greater clinical impact in severely ill patients.Thirdly, any concurrent treatment was allowed at investigators discretion in Ruxo-Sim. Thus, 77% of patients received corticosteroids and 29% tocilizumab. In comparison, only 22% received corticosteroids in the ACT-2 trail and Il-6 antagonists were not allowed in either ACTT-2 or STOP-COVID. Thus, a redundant activity between different anti-inflammatory drugs is plausible.Additionally, the deterioration rate (less than 15% [11 out of 92]) and overall mortality (5% [4 out of 92]) observed in our study was significantly better than expected in a first wave of COVID-19. This could suggest an effectiveness of the control arm more than a poor performance of the experimental arm.Finally, though prior treatment with statins was a stratification factor, the presence of a component of the combination in the control arm itself determines a significant bias.Regarding the cytokine profiling analysis, several authors have classified COVID-19 patients in prognostic groups based on immunoprofiles defined with a variety of methods from multiplex cytokines to flow or mass cytometry, scRNAseq, and machine learning (As a whole, these studies confirm the interest in characterizing the immune response to predict the clinical course of COVID-19. Unfortunately, most results are far from a clinical application because of the high complexity of the methods, that are not available in most centers.In contrast, our strategy, taking into account not only the higher concentrations of predefined cytokines but also assessing the difference between up- and down-regulated cytokines, was able to reduce the number of determinations required for a reliable prediction. Since several pairs of cytokines reached an optimal threshold of accuracy, most clinical centers could use determinations already available at their institutions to implement this model. This is key in a global threat like COVID-19.Interestingly, there was no difference regarding cluster distribution between patients on ruxolitinib plus simvastatin or SOC. This result points to the notion than the extensive use of standard of care treatments, capable of altering the inflammatory state, could preclude the formal evaluation of the real therapeutic role of the experimental agents in our study.Regarding the decision tree, it has proven to accurately predict the appearance of biochemical alterations 24 hours and clinical deterioration 48 hours before occurring.This suggests a progressive deterioration of conditions, in which an alteration of the cytokine profile manifests after one day in an inflammatory process, with altered D-dimer and CRP, but with no impact yet in the blood oxygenation, which is only affected after two days.In summary, though the combination of ruxolitinib plus simvastatin did not show to be superior to standard of care in our study, design limitations like allowing statins in both arms or the better performance than expected of the control arm preclude a definitive conclusion.Several cytokines, assessed in pairs, classified COVID-19 patients into high- and low-risk groups. A decision tree predicted clinical deterioration 48 hours before occurring. Both tools could help to better tailor treatment for COVID-19 patients. | PMC10151807 |
Data availability statement | The original contributions presented in the study are included in the article/ | PMC10151807 |
||
Ethics statement | The studies involving human participants were reviewed and approved by the ethics committee at HM Hospitales and the Spanish National Agency for Drugs and Health Products (AEMPS). The patients/participants provided their written informed consent to participate in this study. | PMC10151807 |
||
Author contributions | Conception and design of study: JG-D, DM-U, KK, PV, AB, EN-V, CR, PN, PB, RM. Acquisition of data (laboratory or clinical): JG-D, DMU, KK, PV, AB, AD, JR-M, EC, RV, EN-V, MY, MO, MB, SR-L, MQ, MZ, CR, PN, PB, RM. Data analysis and/or interpretation: JG-D, DM-U, KK, PV, AB, EN-V, MZ, CR, PN, PB, RM. Drafting of manuscript and/or critical revision: JG-D, MZ, CR, PN, PB, RM. Approval of final version of manuscript: JG-D, DM-U, KK, PV, AB, AD, JR-M, EC, RV, EN-V, MY, MO, MB, SR-L, MQ, MZ, CR, PN, PB, RM. All authors contributed to the article and approved the submitted version. | PMC10151807 |
||
Acknowledgments | APICES SLP and ONCOPERSONAL SLP for their kind support. | PMC10151807 |
||
Conflict of interest | JR-M: Advisory boards and Consulting for BMS, Amgen, Novartis. Rainier, Janssen, Pierre-Fabre. Speaker honoraria from Roche, BMS, Novartis, MSD, Janssen, Pfizer, Astra-Zeneca. Travel, accommodations, expenses: Astellas, Novartis, Roche, BMS, Pfizer, MSD, Astra-Zeneca. Corporate-sponsored research: Astra- Zeneca, BMS, Amgen, Roche, Novartis, MSD, Janssen, Pfizer, Astellas, GSK, PharmaMar, lpsen, Tesaro, Abbvie, Aprea Therapeutics, Eisai, Bayer, Merck, IOVANCE, Nektar. JG-D: Advisory boards and Consulting for Janssen, Pierre-Fabre, BMS, Amgen, Novartis. Speaker honoraria from Pfizer, Janssen, Roche, BMS, Novartis, MSD, Astra-Zeneca. Travel, accommodations, expenses: Astellas, Novartis, Astra-Zeneca. Funding for research: Astra- Zeneca, BMS, Amgen, Roche, Novartis, MSD, Janssen, Pfizer, Astellas, GSK, PharmaMar, lpsen, Tesaro, Abbvie, Merck.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. | PMC10151807 |
||
Publisher’s note | All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | PMC10151807 |
||
Supplementary material | The Supplementary Material for this article can be found online at: Click here for additional data file.Click here for additional data file. | PMC10151807 |
||
References | PMC10151807 |
|||
Background | tic, tic suppression, voluntary tic suppression | TOURETTE SYNDROME IN CHILDREN | Comprehensive Behavioral Intervention for Tics (CBIT) is recommended as a first-line treatment for Tourette syndrome in children and adults. While there is strong evidence proving its efficacy, the mechanisms of reduction in tic severity during CBIT are still poorly understood. In a recent study, our group identified a functional brain network involved in tic suppression in children with TS. We reasoned that voluntary tic suppression and CBIT may share some mechanisms and thus we wanted to assess whether functional connectivity during tic suppression was associated with CBIT outcome. | PMC10755221 |
Methods | Thirty-two children with TS, aged 8 to 13 years old, participated in a randomized controlled trial of CBIT | PMC10755221 |
||
Results | vocal tic, tic | Functional connectivity during tic suppression did not change from baseline to endpoint. However, baseline tic suppression-related functional connectivity specifically predicted the decrease in vocal tic severity from baseline to endpoint in the CBIT group. Supplementary analyses revealed that the functional connectivity between the right superior frontal gyrus and the right angular gyrus was mainly driving this effect. | PMC10755221 |
|
Conclusions | vocal tic, tic, voluntary tic suppression, tics | This study revealed that functional connectivity during tic suppression at baseline predicted reduction in vocal tic severity. These results suggest probable overlap between the mechanisms of voluntary tic suppression and those of behavior therapy for tics. | PMC10755221 |
|
Keywords | PMC10755221 |
|||
Introduction | tic, tic suppression, tic suppressibility, voluntary tic suppression, Tourette syndrome, tics | TOURETTE SYNDROME | Comprehensive Behavioral Intervention for Tics (CBIT) is recommended as a first-line treatment for Tourette syndrome (TS) in children and adults (Andrén et al., Recently, Essoe, Ramsey, Singer, Grados, and McGuire (Another process that may be linked with CBIT is voluntary tic suppression. Most people with TS can voluntarily suppress their tics, though tic suppressibility varies widely across individuals (Conelea et al., While the exact mechanism underlying tic suppression is still unclear, it could, just like behavior therapy, rely on a mix of reinforcement learning, cognitive control, and habituation. Prior research has found that tic suppression can be improved with positive reinforcement (Conelea et al., In a recent EEG study, our group identified a brain network in which functional connectivity was increased during tic suppression in children with TS (Morand-Beaulieu et al., Therefore, the first objective of this study was to test whether functional connectivity associated with tic suppression would change from baseline to endpoint. We predicted that functional connectivity in the tic suppression network would increase from baseline to endpoint in the CBIT group but not in the treatment-as-usual (TAU) condition. Our second objective was to test whether functional connectivity predicted reduction in tic severity following CBIT. We hypothesized that increased brain connectivity during tic suppression at baseline would predict greater decreases in tic severity at endpoint in the CBIT group. | PMC10755221 |
Methods | PMC10755221 |
|||
Procedures | PMC10755221 |
|||
Study design, randomization, and treatment | Full details pertaining to the structure of the RCT can be found in Morand-Beaulieu et al. ( | PMC10755221 |
||
EEG recordings | Continuous EEG was recorded at a 250 Hz sampling rate from 128 electrodes (HydroCel Geodesic Sensor Net) referenced online to the vertex electrode (Cz). We used a Net Amps 200 amplifier and Net Station Acquisition software version 4.2.1 (EGI, Inc.) to monitor signal acquisition. The sensor net was soaked in a potassium chloride solution prior to the recording session. Electrode impedance was assessed at or under 40 kΩ before recording. Data were online filtered with a 0.01 Hz high-pass filter and a 100 Hz low-pass filter. | PMC10755221 |
||
Experimental task | tics, tic | EEG was recorded during three 2-min tic suppression sessions during which children were asked to suppress all tics. They were also asked to keep their eyes open while looking at the computer screen. Recordings took place in a dimly-lit room. | PMC10755221 |
|
Outcome assessment | vocal tic | Changes in motor and vocal tic severity from baseline to endpoint were assessed by a blinded rater using the Yale Global Tic Severity Scale (Leckman et al., | PMC10755221 |
|
EEG signal treatment | PMC10755221 |
|||
EEG signal preprocessing | EEG recordings were preprocessed using the Maryland Analysis of Developmental EEG (MADE) pipeline (Debnath et al., | PMC10755221 |
||
Source-based connectivity pipeline | BRAIN | Brain sources were reconstructed with minimum source imaging (wMNE) in Brainstorm (Tadel, Baillet, Mosher, Pantazis, & Leahy, The phase-locking value (PLV) was computed in Brainstorm and served as our measure of functional connectivity. The PLV reflects the absolute value of the mean phase difference between two signals (Aydore, Pantazis, & Leahy, | PMC10755221 |
|
Statistical analyses | vocal tic, tic, reductions in motor and vocal tics | To assess whether functional connectivity during tic suppression changed from baseline to endpoint, we conducted a repeated-measures ANOVA with Functional connectivity as the dependent variable, and with the between-subjects factor Treatment (CBIT and TAU) and the within-subjects factor Time (baseline and endpoint). We also conducted To test whether functional connectivity differently predicted reductions in motor and vocal tics, we included the YGTSS motor and vocal tic subscales as a factor in our prediction analyses. Thus, we conducted an ANCOVA on reductions in tic severity from baseline to endpoint with the between-subjects factor Treatment (CBIT and TAU) and the continuous predictor Baseline mean functional connectivity. | PMC10755221 |
|
Results | vocal tic, tic, tic suppression | CORTEX | Results from the RCT have been published elsewhere (Morand-Beaulieu et al., Change in tic severity from baseline to endpointThe first objective of this study was to assess if functional connectivity increased from baseline to endpoint in the CBIT group relative to TAU. Our analyses revealed no main effect of Time [CBIT did not impact tic suppression-related functional connectivity. Regarding our second objective about predicting CBIT outcome using mean connectivity within the tic suppression network, the interaction between Treatment and Baseline mean functional connectivity was not significant [Prediction of CBIT outcome using baseline functional connectivity during tic suppression. Reduction in vocal tic severity following CBIT was predicted by mean connectivity during tic suppression at baseline. However, functional connectivity did not predict the decrease in motor tic severity. CBIT, Comprehensive Behavioral Intervention for Tics; TAU, treatment-as-usual; YGTSS, Yale Global Tic Severity Scale.Supplementary analyses were performed to identify whether specific connections within the tic suppression subnetwork were mainly responsible for the relationship between baseline functional connectivity during tic suppression and decrease in vocal tic severity post-CBIT. To identify potential associations between improvement in total and motor tic severity, which could have been masked by looking at the mean connectivity of the tic suppression subnetwork, supplementary analyses were conducted with the motor and total tic scales as well. Within the CBIT group, correlations were performed between each of the 29 connections involved in the tic suppression subnetwork and the decrease in vocal, motor, and total tic severity. A Bonferroni-corrected Association between decrease in vocal tic severity post-CBIT and superior frontal gyrus – inferior parietal cortex connectivity. Supplementary analyses were performed between single connections involved in the tic suppression subnetwork and vocal tic severity decreases in the CBIT group. One connection was significant according to the Bonferroni-corrected significance threshold. Within the CBIT group, decreases in vocal tic severity from baseline to endpoint were predicted by the functional connectivity between the right superior frontal gyrus and the right inferior parietal cortex. A, anterior; L, left; P, posterior; R, right. | PMC10755221 |
Discussion | tic, vocal tic, tic suppression, voluntary tic suppression, tic suppressibility, tics, vocal tics | CORTEX, SUPPRESSION, BRAIN | In the current study, we wished to assess how functional connectivity associated with tic suppression was related to CBIT response. EEG was recorded during three 2-min tic suppression sessions at baseline and endpoint. Brain sources were then reconstructed. We assessed functional connectivity within a subnetwork involved in tic suppression (Morand-Beaulieu et al., Our analyses revealed that functional connectivity during tic suppression did not change from baseline to endpoint. In CBIT, tics are not directly suppressed but are replaced by competing responses (Piacentini et al., While functional connectivity during tic suppression is not altered by CBIT, it seems that it may predict its outcome. Indeed, baseline functional connectivity during tic suppression predicted reduction in vocal tic severity after CBIT. This suggests that strategies used in the voluntary suppression of vocal tics may be relevant during the course of the CBIT training. These results also suggest probable overlap between the mechanisms of voluntary tic suppression and those of behavior therapy for tics. However, in the present study, this effect was only present for vocal tics and not motor tics. In CBIT, competing responses for vocal tics generally consist of ‘controlled breathing’ (Woods et al., To the best of our knowledge, only one study directly assessed the link between tic suppression and CBIT. McGuire et al. (In our study, supplementary analyses revealed that the observed effect was mainly driven by the connection between the right superior frontal gyrus and the right inferior parietal cortex. In the Desikan-Killiany atlas, the anatomically-defined superior frontal gyrus encompasses the functionally-defined dorso-medial prefrontal cortex (dmPFC), and the inferior parietal cortex region includes both the inferior parietal gyrus and the angular gyrus (Desikan et al., Finding predictors of treatment response is important from at least two perspectives: identifying individuals for whom CBIT may work well and understanding by which processes reductions in tic severity occur during CBIT. So far, few baseline characteristics may predict who is more likely to strongly benefit from CBIT. Sukhodolsky et al. (The findings of this study must be interpreted in the context of some limitations. First, our sample size was small, and our results need to be replicated in larger samples. Second, while using EEG allows the assessment of the synchronization of brain oscillations in a fast frequency band (alpha; 8–13 Hz), using fMRI would allow a more precise localization of the brain regions involved in tic suppression and CBIT response, compared with source-reconstructed EEG. Third, our hypotheses were based on potential parallels between tic suppression and behavior therapy. Thus, we decided to focus on a single subnetwork which we know is involved in tic suppression. Future studies should assess other known brain networks to better understand the role of functional connectivity in CBIT. Fourth, this study did not include a tic frequency count to assess the degree of tic suppressibility. This is in contrast with studies using the Tic Suppression Task from Woods and Himle ( | PMC10755221 |
Supporting information | PMC10755221 |
|||
Morand-Beaulieu et al. supplementary material | Morand-Beaulieu et al. supplementary material | PMC10755221 |
||
Acknowledgements | We thank all children who participated in this study, as well as their family. | PMC10755221 |
||
Supplementary material | The supplementary material for this article can be found at | PMC10755221 |
||
Financial support | DGS, Tourette syndrome | BRAIN, TOURETTE SYNDROME, TOURETTE | This project was supported by NIMH grant K01MH079130 to DGS. SMB was supported by a postdoctoral fellowship award from the Canadian Institutes of Health Research (MFE164627) and by the Clinical Research Training Scholarship in Tourette syndrome from the Tourette Association of America and the American Brain Foundation, in collaboration with the American Academy of Neurology. The funding organizations played no role in collection, analysis and interpretation of data and in the writing of the manuscript. The authors have no biomedical financial interests or potential conflicts of interest to report. | PMC10755221 |
Ethical standards | The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. | PMC10755221 |
||
References | PMC10755221 |
|||
Purpose | This study was conducted to evaluate a smartphone-based online electronic logbook used to assess the clinical skills of nurse anesthesia students in Iran. | PMC10169697 |
||
Methods | This randomized controlled study was conducted after tool development at Ahvaz Jundishapur University of Medical Sciences in Ahvaz, Iran from January 2022 to December 2022. The online electronic logbook involved in this study was an Android-compatible application used to evaluate the clinical skills of nurse anesthesia students. In the implementation phase, the online electronic logbook was piloted for 3 months in anesthesia training in comparison with a paper logbook. For this purpose, 49 second- and third-year anesthesia nursing students selected using the census method were assigned to intervention (online electronic logbook) and control (paper logbook) groups. The online electronic logbook and paper logbook were compared in terms of student satisfaction and learning outcomes. | PMC10169697 |
||
Results | A total of 39 students participated in the study. The mean satisfaction score of the intervention group was significantly higher than that of the control group (P=0.027). The mean score of learning outcomes was also significantly higher for the intervention than the control group (P=0.028). | PMC10169697 |
||
Conclusion | Smartphone technology can provide a platform for improving the evaluation of the clinical skills of nursing anesthesia students, leading to increased satisfaction and improved learning outcomes. | PMC10169697 |
||
Graphical abstract | PMC10169697 |
|||
Introduction | PMC10169697 |
|||
Background/rationale | Formative assessment of the clinical performance of nurse anesthesia students during training courses is an indispensable component of their education. This assessment promotes the quick diagnosis of weaknesses and strengths, correction of performance, and improvement of clinical competence [Today, the use of electronic logbooks is rapidly increasing [ | PMC10169697 |
||
Objectives | The present study was conducted to design, implement, and evaluate a smartphone-based online electronic logbook (the AGAH app) to evaluate the clinical skills of nurse anesthesia students. We hypothesized that by providing quick access, online evaluation, and timely feedback, the design and implementation of the AGAH app would increase the nurse anesthesia students’ satisfaction and improve their learning outcomes. | PMC10169697 |
||
Methods | PMC10169697 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.