Real Patient Research Articles

Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy.

The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.

GridlockED as an Intervention for Nurses (GAAIN) Study.

GridlockED (The Game Crafter, LLC) is a serious game that was developed to teach challenges that face nursing and medical professionals in the emergency department (ED). However, few studies have explored nurses' perceptions of the utility, fidelity, acceptability, and applicability of the serious game modality. This study examined how ED nurses view GridlockED as a continuing education platform. This single-center observational study explored how nurses engage with and respond to Grid-lockED. The convenience sample included participants recruited from a local continuing nursing education day. Participants completed a presurvey, engaged in a full game play session with the GridlockED game for approximately 45 minutes, and immediately completed a post-game play survey. Of the 48 participants (11 male, 37 female; 44 of 48 were RNs), most (91%) agreed that the workflow reflected in the game was equivalent to the flow in a typical ED. Almost all (96%) found the cases in the game reflective of real ED patients, and most (92%) found the game a useful educational tool to prepare new nurses to transition into the ED environment. The GridlockED game shows potential as a serious game to support nursing education, particularly for new ED nurse orientation and transition to ED practice. [J Contin Educ Nurs. 2024;55(5):231-238.].

Review of methods and systems for generation of synthetic training data

It is impossible to imagine the advancement of modern artificial intelligence systems without neural network technologies. During the design process researchers are often faced with the fact that there is not enough data to train modern neural network models, these data may be unbalanced or highly sparse. Often it happens that real data simply does not exist, as the research field is still emerging. A relevant problem is ensuring the confidentiality of real personal or patient medical data, which is used in the exchange between researchers or in the testing of various neural network systems. In many subject areas, the cost of collecting and marking up real data can be very high. Synthetic data is increasingly being used to solve these problems. The purpose of this publication is to introduce readers to advances in the generation and use of synthetic data. The paper presents a description of various methods, systems and software tools used to generate synthetic data, which can help to improve neural network models. Since an entire industry for synthetic data production has already formed, the leading data synthesis technology platforms are presented. The paper is of an overview nature, so it contains an extensive bibliography. The value of the article lies in the fact that this review will help readers broaden their understanding of the use of synthetic data in solving a wide range of neural network problems, as well as to become more familiar with the methods and tools for their generation.

Nonfluoroscopic 3D Image Guidance System Reduces Navigation Times Through Five 3D-Printed Iliofemoral Vascular Phantoms

Purpose: To assess the potential adjunctive role of a 3D electromagnetic (EM) navigational system for use in above-knee vessels afflicted with peripheral artery disease (PAD). Peripheral artery disease can be challenging to operators encountering significant vessel tortuosity, calcium, and stenoses, which may require prolonged procedure times and excessive use of nephrotoxic iodinated contrast when performed with conventional fluoroscopy. Materials and Methods: Following appropriate ethical oversight, five 3D-printed bench phantoms modeling tortuous calcified PAD were created based on source CTA (computed tomography angiography) data sets from real patients. Investigational software was developed based on a commercially available aortic EM navigation platform (Intraoperative Positioning System [IOPS]; Centerline Biomedical, Inc., Cleveland, Ohio), with patient-specific structural maps of vessel lumens and calcification. Using a sensorized prototype 6 French (Fr) catheter and 0.035” guidewire, 15 interventionalists traversed each phantom using the EM platform as well as 2D simulated fluoroscopy-like image guidance and the times were recorded. Participants completed a 10-item standard system usability scale (SUS) questionnaire (score 1–5, 5= strongly agree) evaluating system usability and user satisfaction. Navigation times and SUS scores were compared with a 1-tailed statistical t test. Results: Participants demonstrated a statistically significant reduction in navigation times using EM guidance, performing 0.7 minutes (42 seconds) faster on average ( P < .001), corresponding to a 25% average relative reduction. Participants reported sufficiently high levels of usability satisfaction, with a mean SUS score of 4.29 ( P < .001), exceeding the acceptance criterion (score ≥3.5). Conclusion: This preclinical phantom study highlights the future potential of Centerline Biomedical’s EM navigation technology as a possible adjunct to fluoroscopy for highly precise visualization and navigation of PAD-afflicted vasculature. Clinical Impact This preclinical proof-of-concept study highlights the feasibility of EM navigation not only for branch vessel cannulation, but also for inline navigation of peripheral vessels afflicted with calcified plaques via benchtop iliofemoral phantom simulations. The navigation platform studied addresses the need for improvements in EM technology through modelling algorithms that facilitate 3D visualization of calcified plaque in any projection in real time, in addition to sensorization of both catheter and guidewire in a compact 6Fr system.

Automating the Detection of IV Fluid Contamination Using Unsupervised Machine Learning.

Intravenous (IV) fluid contamination is a common cause of preanalytical error that can delay or misguide treatment decisions, leading to patient harm. Current approaches for detecting contamination rely on delta checks, which require a prior result, or manual technologist intervention, which is inefficient and vulnerable to human error. Supervised machine learning may provide a means to detect contamination, but its implementation is hindered by its reliance on expert-labeled training data. An automated approach that is accurate, reproducible, and practical is needed. A total of 25 747 291 basic metabolic panel (BMP) results from 312 721 patients were obtained from the laboratory information system (LIS). A Uniform Manifold Approximation and Projection (UMAP) model was trained and tested using a combination of real patient data and simulated IV fluid contamination. To provide an objective metric for classification, an "enrichment score" was derived and its performance assessed. Our current workflow was compared to UMAP predictions using expert chart review. UMAP embeddings from real patient results demonstrated outliers suspicious for IV fluid contamination when compared with the simulated contamination's embeddings. At a flag rate of 3 per 1000 results, the positive predictive value (PPV) was adjudicated to be 0.78 from 100 consecutive positive predictions. Of these, 58 were previously undetected by our current clinical workflows, with 49 BMPs displaying a total of 56 critical results. Accurate and automatable detection of IV fluid contamination in BMP results is achievable without curating expertly labeled training data.

Evaluation of tracheostomy suctioning procedure among nursing and respiratory therapy students: wearable manikin vs. standard manikin.

This study aims to evaluate cognitive load (CL), emotional levels (EL), and stress levels (SL) of students when using a wearable manikin vs. a standard manikin for tracheostomy suctioning (TS). This study was approved by the Institutional Review Board. Subjects were recruited by email. Subjects completed a baseline demographics questionnaire, then they were randomized into two groups: wearable manikin group (WMG) or standard manikin group (SMG). For the WMG, an actor simulated a patient by wearing the device. In phase I, both groups were educated on how to perform TS by video and offered hands-on practice. Then I put through a tracheostomy suctioning clinical simulation and completed a post sim-survey. In phase II, the same survey was repeated after encountering a real patient as part of their clinical rotation. A total of 30 subjects with a mean age 26.0 ± 5.5 years participated. 20 (66.7%) were respiratory care students and 10 (33.3%) were nursing students. In the WMG, the median stress level dropped significantly post phase II compared to post phase I [2(1,4) vs.3(1,5), p = 0.04]. There were no significant changes in median CL, confidence, and satisfaction levels between post phase II and post phase I (p > 0.05). In the SMG, the satisfaction level increased significantly post phase II compared to post phase I [5(4,5) vs.4(2,5), p = 0.004], but there were no significant changes in CL, SL, and confidence levels between post phase I and phase II. There was no significant difference in mean EL scores over time and these changes did not differ by group. Subjects in the WMG showed a higher mean competency score than those in the SMG (85.5 ± 13.6 vs. 78.5 ± 20.8, p = 0.14, Cohen's d = 0.4), yet not significant. Our results showed that the WMG is beneficial in helping bridge the gap of learning TS from the sim setting to the real-world clinical setting. More studies with higher sample size and use of other CL scales that assesses the different types of CL are needed to validate our findings.

How Can the Clinical Aptitude of AI Assistants Be Assayed?

Large language models (LLMs) are exhibiting remarkable performance in clinical contexts, with exemplar results ranging from expert-level attainment in medical examination questions to superior accuracy and relevance when responding to patient queries compared to real doctors replying to queries on social media. The deployment of LLMs in conventional health care settings is yet to be reported, and there remains an open question as to what evidence should be required before such deployment is warranted. Early validation studies use unvalidated surrogate variables to represent clinical aptitude, and it may be necessary to conduct prospective randomized controlled trials to justify the use of an LLM for clinical advice or assistance, as potential pitfalls and pain points cannot be exhaustively predicted. This viewpoint states that as LLMs continue to revolutionize the field, there is an opportunity to improve the rigor of artificial intelligence (AI) research to reward innovation, conferring real benefits to real patients.

Mathematical-based morphological classification of skin eruptions corresponding to the pathophysiological state of chronic spontaneous urticaria

BackgroundChronic spontaneous urticaria (CSU) is one of the most intractable human-specific skin diseases. However, as no experimental animal model exists, the mechanism underlying disease pathogenesis in vivo remains unclear, making the establishment of a curative treatment challenging.MethodsA novel approach combining mathematical modelling, in vitro experiments and clinical data analysis was used to infer the pathological state of CSU patients from geometric features of the skin eruptions.ResultsBased on our hierarchical mathematical modelling, the eruptions of CSU were classified into five categories, each with distinct histamine, basophils, mast cells and coagulation factors network signatures. The analysis of 105 real CSU patients with this classification by six individual dermatologists achieved 87.6% agreement. Furthermore, our network analysis revealed that the coagulation status likely determines boundary/area pattern of wheals, while the state of spontaneous histamine release from mast cells may contribute to the divergence of size and outline of the eruptions.ConclusionsOur multi-faceted approach was accurate in defining pathophysiological states of disease based on geometric features offering the potential to improve the accuracy of CSU diagnosis and better management of the disease in the clinic.

IGF-1 as screening tool for acromegaly and adult-onset growth hormone deficiency in the Netherlands.

Insulin-like growth factor 1 (IGF-1) measurements play a central role in the diagnosis and follow-up of acromegaly and growth hormone deficiency. However, improving health care outcomes for these patients involves an intricate process of laboratory diagnostics and skilled health care professionals. The integrated effects of IGF-1 reports on diagnosis and treatment decisions are yet unknown. Extended quality assessment, distributing the description of five (real) patient cases with accompanying blood samples. Patients suspected or during follow up for acromegaly or adult onset of growth hormone deficiency were included. Laboratory specialists and endocrinologists in the same centre were asked to interpret their centre-specific IGF-1 results by using a laboratory and medical questionnaire. This way, insight could be obtained into the combined effects of different assays, assay harmonisation, reference value sets, and individual physician interpretation in relation to guidelines, thus reviewing the entire diagnostic and management process. Limited variation (CV 13.8 ± 2.8) was found in IGF-1 concentrations despite different use of the harmonization sample and factor among laboratories. This interlaboratory variation increased upon conversion to SD scores (CV 15.7 ± 40.7) as a consequence of the use of different reference value sets. Furthermore, there was a lack of adherence to international guidelines among endocrinologists. Highly variable diagnostic and treatment outcomes in acromegaly and AGHD in the Netherlands can be attributed to increased variability of IGF-1 upon conversion to SD scores and low adherence to clinical guidelines.

Decision making in emergency laparotomy: updated results of an international multi disciplinary judgement analysis study

Abstract Aims The aim of our study is to assess how professionals take decisions when evaluating suitability of patients for emergency laparotomy or palliation. Methods We created and validated a dataset of 90 case vignettes based on real patients admitted with surgical pathology correctable only via an emergency laparotomy. We removed information concerning real life outcomes. We submitted the vignettes to consultants and trainees in surgery, anaesthesia and intensive care, asking them if they would offer an emergency laparotomy. Concordance between assessors and real life decisions, accuracy scores (the lower the value the higher the likelihood of operating on patients that survived) and inconsistency scores were calculated. Judgement Analysis methodology (binomial logistic regression) was used to identify variables relevant to the decision making process for each participant. Results This report includes results for the first 110 participants. Thirteen centres provided data (4 from Italy, 8 from the UK, 1 from Greece). 54 consultants, 39 registrars, 13 core trainees participated. 68 were surgeons, 26 anaesthetists and 9 intensivists. Median concordance between decision makers and real life was 78.9%. Median accuracy was 17 (IQR 11-34.3). Median inconsistency was 13% (IQR 7%-20%). Concordance, accuracy and consistency strongly correlated with each other and were better for consultants and worse for trainees. Judgment Analysis demonstrated differences between grades, specialities and institutions. Conclusions Different clinicians, when faced with the same information, make different decisions. Our updated data supports the adoption of multidisciplinary decision making protocols as well as senior involvement.

Comparing the Effectiveness of Hands-on vs. Observational Training of Residents in Interlaminar Epidural Steroid Injections (ILESI) Using a High-Fidelity Spine Simulator.

Introduction The Accreditation Council for Graduate Medical Education (ACGME) requires that residents in the Physical Medicine and Rehabilitation (PM&R) residency observe or perform certain interventional procedures, one of which is an interlaminar epidural steroid injection(ILESI). While the traditional learning model relying heavily on observation is commonplace, it leaves the practice phase of learning to happen on real patients. High-fidelity simulation may be a worthwhile alternative as a training approach to increase physician comfort with the procedure and improve patient safety. Methods Current PM&R residents from two programs between their second and fourth year, inclusively, who lacked prior training experience in ILESI attended one hour of either: (1) an experimental arm of supervised hands-on training on a simulation deviceor (2) a control arm observing the procedures performed by an attending on the same device. Assignments were made based on resident schedule availability. Pre-training knowledge, training, and post-training knowledge were assessed at the Multidisciplinary Pain Clinic at Montefiore Medical Center. Participants were assessed on their procedural competence using an adapted version of a previously published grading checklist before the session. Participants also evaluated their confidence in performing the procedure prior to and after training. Data was analyzed using the Wilcoxon signed-rank test and the Wilcoxon rank-sum test. SAS Version 9.4 was used for analysis. Results Fifteen residents initially participated, but three residents dropped out at the 15-week follow-up. There was a significant increase in test scores in both arms immediately after the intervention (p=0.008 in control, p=0.016 in the experiment), with greater improvement shown in the hands-on training group (p=0.063). At the 15-week follow-up, there was no significant change in test scores in the control arm (p=0.969) while there was a decrease in the experiment arm (p<0.001). Conclusion Hands-on learning with high-fidelity simulation demonstrated more improvement for short-term motor-skill acquisition, while observational learning with repetition showed more benefits for long-term retention. Optimal procedural training should employ both educational modalities for best short- and long-term results.

Investigation on the limits of type 1 diabetes therapy automation using insulin pumps

Type 1 diabetes is an incurable chronic disease that usually has its onset in childhood. The disease means that the patient’s body irreversibly loses its ability to produce the insulin hormone. Since the lack of insulin in the organism always leads to death, the therapy primarily consists in supplying the hormone from the outside inright doses and in the right time. Administration errors can result in poor mood, health complications or even death. The introduction of insulin pumps for common use as basic devices for insulin delivery has significantly improved the quality of the therapy, and what follows, the physical and mental comfort of patients. It is still easy to get an opinion that these devices completely automate the therapy process, but this is not true. The aim of this research was to determine the possible level of the automation of the type 1 diabetes therapy solely based on an insulin pump. First, the principle of operation of typical modern insulin pumps was described. This became the basis for the development of a special biocybernetic model of a patient with diabetes. Then, the model was used in numerous computer simulations whose output was confronted with historical data obtained from real patients. The research confirms that the use of a typical modern insulin pump alone does not fully automate the therapy of type 1 diabetes. Nevertheless, possible methods could be identified to achieve a higher quality of treatment and to find opportunities for additional automation. These methods can be considered as an alternative to the socalled artificial pancreas.

A Review on the Use of Modern Computational Methods in Alzheimer's Disease-Detection and Prediction.

Discoveries in the field of medical sciences are blooming rapidly at the cost of voluminous efforts. Presently, multidisciplinary research activities have been especially contributing to catering cutting-edge solutions to critical problems in the domain of medical sciences. The modern age computing resources have proved to be a boon in this context. Effortless solutions have become a reality, and thus, the real beneficiary patients are able to enjoy improved lives. One of the most emerging problems in this context is Alzheimer's disease, an incurable neurological disorder. For this, early diagnosis is made possible with benchmark computing tools and schemes. These benchmark schemes are the results of novel research contributions being made intermittently in the timeline. In this review, an attempt is made to explore all such contributions in the past few decades. A systematic review is made by categorizing these contributions into three folds, namely, First, Second, and Third Generations. However, priority is given to the latest ones as a handful of literature reviews are already available for the classical ones. Key contributions are discussed vividly. The objectives set for this review are to bring forth the latest discoveries in computing methodologies, especially those dedicated to the diagnosis of Alzheimer's disease. A detailed timeline of the contributions is also made available. Performance plots for certain key contributions are also presented for better graphical understanding.

Abstract A064: My Choices: A multilevel, multicomponent intervention to improve informed decision-making about clinical trial participation among cancer patients

Abstract Background: Knowledge gained through cancer clinical trials (CTs) has been proven critical to preventing, diagnosing and treating the disease, and providing the evidence base for clinical practice. Major advances in cancer treatment, which are essential for improving patients’ outcomes, come from investigations of new therapeutic agents in CTs. Despite the large number of available studies and improvements in public awareness about CTs, participation of underrepresented minorities in clinical research has been persistently low, with only 2-5% of Latinos and African Americans participating in cancer treatment trials. Barriers to participation are multilevel, complex, and multifactorial, including study design, healthcare system barriers, and patient- and medical team-related factors. Structural inequities, social determinants of health (SDoH), distrust of government, patient-doctor communications, cultural and language barriers, and lower levels of health literacy are barriers for Latino and African American populations. Purpose: To improve informed decision-making about cancer CT participation among cancer patients and community members through a bilingual multilevel, multi-communication approach, including: 1) a 2-group parallel randomized controlled educational trial, and 2) a community awareness campaign. Methods: The randomized study involves 400 patients from the Mays Cancer Center. The intervention group receives 1) a bilingual educational video on CTs with patients’ testimonials, 2) a low literacy booklet, 3) support from a patient navigator (PN), and 4) Salud America! weekly emails with links to online/social media posts on CT. The control group receives a general fact sheet on CTs. Healthcare providers involved in clinical research are invited to participate in Webinars to raise awareness of implicit bias and the importance of inclusive research. The community intervention features a prospective single-group pre/post design, where participants (400) act as their own controls. They receive an educational session on CTs provided by a community health educator + a low literacy booklet. Results: Focus groups guided the development of the video script, booklet, and educational materials. The short video features real cancer patients sharing their experiences with CTs and how they overcame common barriers. Study recruitment began in February 2023. So far, we have recruited 36 cancer patients with a mean age of 51 years, 80% White and 61% Hispanic/Latino, 64% married, 44% had Medicare/Medicaid; 36% &amp;lt; high school education; and 33% earned &amp;lt;$40K. Preliminary results will be presented. Conclusions: Multilevel interventions involving culturally tailored decision aids (i.e., video, booklet) in combination with PN support can effectively address common barriers influencing patient decision-making, raise awareness of CTs, and increase positive attitudes, intentions, and participation in CTs among groups experiencing SDoH. Citation Format: Patricia Chalela, Vivian Cortez, Armida Flores, Sandra Sivak, Edgar Munoz, Cliff Despres, Callie Rainosek, Alyssa Gonzalez, Mio Kitano, Virginia Kaklamani, Kate Lathrop, Sukeshi Arora, Andrew Brenner, Deepak K. Pruthi, Amelie G. Ramirez. My Choices: A multilevel, multicomponent intervention to improve informed decision-making about clinical trial participation among cancer patients [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A064.

Abstract C017: Comparing prostate specific antigen dynamics between Black patients and White patients

Abstract Black men have the highest prostate cancer (PCa) mortality across all US population groups. We hypothesize that this may be the result of low participation from black men in clinical trials and socioeconomic facotrs. We use a simple mathematical model to investigate differences in PSA dynamics between black and white men undergoing androgen deprivation therapy (ADT). The model was calibrated to longitudinal PSA data from 57 PCa patients (N = 47 white, N = 10 black) receiving continuous androgen deprivation therapy (ADT). To compensate for the lack of data for black patients, propensity score matching was used to select 15 white patients that were most alike to the 10 black patients based on their Eastern Cooperative Oncology Group (ECOG) score, age, and tumor burden. Using a previously developed model of stem cell, non-stem, and prostate-specific antigen (PSA) dynamics, parameter analysis and leave-one-out process were used to determine which parameters should be uniform among all patients and which should be patient-specific to determine if any parameters showed a significant difference. Each patient’s parameter values were used to see if intermittent ADT (IADT) or adaptive ADT could extend the time to progression and narrow disparity gaps. We then sampled from the parameter distributions to create 50 black and 50 white in-silico patients to simulate continuous and intermittent ADT to further validate our results. The leave-one-out analysis found that a uniform ADT cytotoxicity resulted in the best Akaike Information Criteria score for black and white patients. A nested optimization was subsequently used to determine race-specific uniform cytotoxicity rates, while the remaining patient-specific parameters were determined. Statistical analysis found that black patients had a significantly higher stem cell self-renewal rate than the white patients. We hypothesize that this is primarily responsible for earlier progression in black patients. Simulations of continuous and intermittent ADT for real and in-silico patients showed that IADT and adaptive ADT can extend the time to progression (TTP) for both black and white patients. The TTP was extended by 50% for the black patients, and 25% for the white patients, thus the black patients had a greater benefit. However, the median TTP for black patients on IADT was less than that for white patients on continuous ADT. This suggests that further investigation of different treatments needs to be done to close disparity gaps. Our results show that stem cell self-renewal is driving differences in PSA dynamics between white and black patients. Intermittent Androgen Deprivation Therapy could potentially be a step in addressing the higher stem-cell self-renewal rate in black patients and narrowing the health disparity gap between black and white Pca patients. This valuable information can be used in the future to design race-specific treatment modalities to maximally delay time to progression. Citation Format: Alexandria Johnson, Renee Brady. Comparing prostate specific antigen dynamics between Black patients and White patients [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C017.

Applied improvisation and transdisciplinary simulation: a necessity for any health curriculum?

From practising a procedure, such as a lumbar puncture, to explaining the aim and method and listening to concerns, the practice of health professionals requires a range of skills, often classified into technical and non-technical skills. Just as gestures and procedures can be taught, so can empathy and communication skills. This article introduces an innovative approach that unites both necessary types of skills. The specific framework of improvisational theatre ("improv") has widespread application, including the training of health professionals (health training improv). By sharing close contexts and skills, health training improv provides a valuable, safe, and effective learning environment that allows practitioners to practice exercises and situations that align with particular objectives. We created a transdisciplinary team to develop a programme of Health Professional Training Improv (HPTI), bringing together the fields of health, psychology, simulation, and arts. Since 2019, various health student groups (nurses, midwives, medical doctors, and speech therapists) have participated in a 16-h applied improv training workshop under the supervision of a professional improv facilitator. Additionally, drama students completed applied improv for health courses, which trained them to act as simulated patients, with a view to the implementation of transdisciplinary improv simulation sessions at SimUSanté (a multidisciplinary health simulation facility located in France). Students' feedback emphasized their interest in HPTI, the realism of the simulation sessions, and the skills they felt had improved. This feedback needs to be supplemented with quantitative data from standardised assessments. The development of this rich pedagogical and research framework, based on a transdisciplinary approach, has brought different fields together to prepare students for real patient encounters. It is essential to continue this training and conduct research to evaluate the curricula developed.

Three-dimensional modelling and hemodynamic simulation of the closure of multiple entry tears in type B aortic dissection.

Stanford type B aortic dissection (TB-AD) is a life-threatening vascular condition with high rates of morbidity and mortality. Currently, thoracic endovascular aortic repair (TEVAR) is widely performed to treat TB-AD, and some studies have analyzed the influence of stents on hemodynamics using computational fluid dynamics (CFD) models. However, the accuracy of TB-AD simulation models are not satisfactory, they are often constructed as a regular ideal model. Furthermore, it is unclear which tear should be closed for the best treatment when there are multi entry tears. The aims of this paper were to provide an assessment method for the selection of the surgical closure location for type B aortic dissection. Five 3D models of multiple entry tears in type B aortic dissection were produced using real patient computed tomography (CT) images to perform hemodynamic analyses of flow velocity streamlines, wall pressure, and wall shear stress. A Boolean operation was adopted to establish 3D models with multiple entry tears in type B aortic dissection based on patient-specific CT images. The Mimics and Ansys plug-in The Integrated Computer Engineering and Manufacturing code for Computational Fluid Dynamics (ICEM CFD) software were applied to mesh the 3D models. The flow velocity streamlines, wall pressures, and wall shear stresses were then analyzed in the finite element analysis software Fluent. Five 3D models were produced to compare the hemodynamic characteristics of different entry tear numbers, as well as the changes of different closure positions before and after closure. The false lumen of the model with two entry tears had a higher wall pressure than that of model with multiple entry tears, which may tend to squeeze the true lumen and expand the false lumen. The load distribution of the vessel in the model with multiple entry tears had a more balanced flow velocity, and its wall pressure and shear stress were lower than that of model with two entry tears. For aortic dissection with two entry tears, the closure of the proximal entry tear was recommended, which helped to isolate and thrombose the false lumen, thereby improving the blood supply function of the true lumen. Because the postoperative vascular flow velocity and mechanical load performance of the vascular wall were still higher than those of normal blood vessels, the postoperative blood vessels remained pathological, and TEVAR did not restore the blood vessels to their original healthy state. Type B aortic dissection with two entry tears tend to squeeze the true lumen and expand the false lumen, resulting in a new entry tear and deterioration into multiple entry type B aortic dissection. The model of the vessel with multiple entry tears had a more balanced distribution in flow velocity and a smaller wall pressure and shear stress than that of the vessel with two entry tears. The closure of the proximal entry tear was considered an ideal solution for type B aortic dissection with two entry tears.

Biological Evaluation of the Osteoinductive Potential of Dry Teeth after Chemical Demineralization Treatment Using the Tooth Transformer Device.

Several studies have already demonstrated the biocompatibility of a tooth as a grafting material in the regeneration of bone tissue, showing its osteoconductive potential, while no studies have verified whether the osteoinductive potential of a tooth remains constant or is altered after its treatment with the Tooth Transformer (TT) device. The aim of the study was to demonstrate that the treatment with the TT device did not alter the osteoinductivity of an extracted tooth that was stored dry. Twelve extracted human teeth were collected from real patients. Caries, tartar and filling materials were removed from each tooth; each tooth was coarsely cut and stored at room temperature (RT) until use. Each sample was shredded, demineralized and disinfected, using the TT device. Protein extraction was carried out for each sample, and Western Blot analysis was performed to test the presence of mineralization protein LIM-1 and transforming growth factor-β. The presence of the human Bone Morphogenetic Protein 2 (BMP-2) and human collagen Type I (COL-I) was found in dry tooth samples processed with the TT device and subjected to Enzyme-Linked Immunosorbent Assay (ELISA) testing. The treatment of chemical demineralization using the TT device does not alter the osteoinductive potential of a dry tooth.

Medical encounters with patients diagnosed with cancer: The association between physicians' behavior and perceived patient centered care and anxiety

PurposeTo evaluate the association between physicians' behavior and cancer patients’ perceived patient-centered care (PCC) and anxiety following medical encounters. MethodsA prospective study design with 100 encounters, including 100 cancer patients and 22 oncology/surgery physicians, was performed between November 2019 and July 2021. Before the medical encounters, patients were asked to complete the validated State-Trait Anxiety Inventory (STAI), and physicians and patients completed sociodemographic and clinical data. During the medical encounters, structured ‘real-time’ observations of the physicians' behaviors were performed using the Four Habits Coding Scheme (4HCS). Following the medical encounters, patients were asked to re-complete the STAI and to fill the validated Perceived PCC questionnaire. ResultsMean 4HCS was positively associated with perceived PCC (β = 0.351, p < 0.001) and contributed 10.5% to the total 25.3% explained variance beyond the sociodemographic and clinical variables. Of the 4HCS sub scales, ‘Demonstrate Empathy’ displayed the lowest correlation with perceived PCC as compared to informational behaviors. In contrast, mean 4HCS was not associated with post-meeting anxiety (p > 0.05). ConclusionOur ‘in-vivo’ observations of medical encounters expands on previous studies in educational settings in showing how physicians' behaviors impact real patients' experience. The findings may provide a more accurate picture of physicians' supportive and unsupportive behaviors that impact on perceived PCC and anxiety. Patients may prefer their physicians to focus on the informational content related to their disease trajectory rather than focusing on empathy with their emotions. Physicians should be trained in ways to support patients on how to regain emotional control in stressful medical situations.

The Million Patient Labset: A large, de-identified dataset of longitudinal clinical laboratory results from one million patients

Abstract Laboratory testing is integral to the diagnosis and treatment of disease. Advances in electronic medical records and computation have initiated an era of ‘big data’ for laboratory medicine. However, most health data continues to exist in isolated and inaccessible information silos, limiting public utility. To address this gap, we have developed a large, de-identified, longitudinal dataset of laboratory results from over one million patients, called the Million Patient Labset (MPL), which is free and open to the public. The MPL was produced from the combination of two large healthcare systems with IRB and information security approvals. It includes 1,112,550 patients from across 1,920 healthcare sites in two states from 2017 through 2021, with 4,404 different clinical assays represented. Sites included primary care clinics, secondary care facilities for dialysis, infusions, rehabilitation, and other subspecialties, and tertiary centers for major surgery, trauma care, cancer care, and other complex medical services. Assays having fewer than 100 results were excluded. Limited patient demographic and encounter metadata are also included. Protected health information was de-identified in accordance with Health Insurance Portability and Accountability Act of 1996 (HIPAA) safe harbor standards. Service dates and times were altered to obscure absolute timestamps, while approximately preserving interval lengths between test results of the same patient, and general age and time of testing averages across patients. Time of day, day of week, and seasonality were approximately preserved. Data were validated through randomly selected comparisons against the original patient records, prior to destroying re-identification keys. In conclusion, we have produced a large de-identified open-access dataset of clinical laboratory results from over one million real patients. We hope this resource will be valuable for researchers exploring human clinical variation, medical artificial intelligence, reference interval optimization, and other data-driven studies.