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We describe a case of intra-arterial treatment (IAT) of acute posterior circulation occlusion in a patient with a persistent primitive trigeminal artery (PPTA). The patient presented with an acute left sided hemiparesis and loss of consciousness (Glasgow coma score of 5). Computed tomography angiography showed an acute occlusion of the right internal carotid artery (ICA), the PPTA, distal basilar artery (BA), right posterior cerebral artery (PCA), and right superior cerebellar artery (SCA). Stent-retriever assisted thrombectomy was not considered possible through the hypoplastic proximal BA. After passage of the proximal ICA occlusion, the right PCA and SCA were recanalized through the PPTA, with a single thrombectomy procedure. Ten days after intervention patient was discharged scoring optimal EMV with only a mild facial and left hand paresis remaining. PPTA is a persistent embryological carotid–basilar connection. Knowledge of existing (embryonic) variants in neurovascular anatomy is essential when planning and performing acute neurointerventional procedures. [ABSTRACT FROM AUTHOR]

Background: Combining patient-generated health data and digital health platforms may improve patient experience and population health, mitigate rising health care costs, reduce clinician burnout, and enable health equity. However, lack of trust may be a notable barrier to the data-sharing required by such platforms. Understanding sociodemographic, health, and personal characteristics will enable developers and implementers of such technologies to consider these in their technical design requirements., Objective: This study aims to understand relationships between sociodemographic characteristics of caregivers of children or adolescents and trust in and willingness to use digital platforms to store and share personal health information for clinical care and research., Methods: This study used a mixed methods approach, including surveys of caregivers of youth aged <18 years living in Canada or the United States and youth aged 16 to 17 years living in Canada, as well as web-based bulletin board discussions to further explore topics of trust in data sharing. Sociodemographic and survey data were tabulated and explored using proportional odds ordinal regression models. Comments from web-based group discussions were analyzed thematically using a coding approach to identify issues important to the participants., Results: Survey data from 1128 caregivers (female participants: n=549, 48.7%; 36-50 years old: n=660, 58.5%; Canadian: n=603, 53.5%; urban population: n=494, 43.8%) were collected, of which 685 (60.7%) completed all questions. Data from 173 youth (female participants: n=73, 42.2%; urban population: n=94, 54.3%) were collected, of which 129 (74.6%) completed all questions, and data were available for analysis. Furthermore, among 40 participants, 23 (58%) caregivers contributed to the web-based discussion boards. Related to trust, living in a rural area (vs urban; odds ratio [OR] 0.66, 95% CI 0.46-0.95) resulted in lower concern for data privacy and security, while having an undergraduate (OR 1.82, 95% CI 1.30-2.55) or graduate degree (vs secondary or trade school; OR 2.50, 95% CI 1.68-3.73) resulted in higher levels of concern. Living with a chronic disease (OR 1.81, 95% CI 1.35-2.44) increased levels of concern regarding data privacy and security. Interestingly, those with chronic disease were more willing to use digital platforms for clinical care and share personal health information for not-for-profit research. Caregivers were most concerned about data breaches involving data from their children but also highlighted that digital platforms would allow for better coordination of care for their children., Conclusions: Our research confirms the willingness of caregivers and youth to use digital platforms for both clinical care delivery and research and suggests that the value of a digital platform may outweigh the risks of its use. Engagement of end users in co-designing such platforms has the potential to enhance digital trust. However, digital trust varies across sociodemographic groups; therefore, diverse end user engagement is necessary when designing digital applications., (©Eric Chow, Alice Virani, Susan Pinkney, Fatema S Abdulhussein, Tibor van Rooij, Matthias Görges, Wyeth Wasserman, Jeffrey Bone, Holly Longstaff, Shazhan Amed. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.10.2024.)

Objective: Hospital-to-home (H2H) transitions challenge families of children with medical complexity (CMC) and healthcare professionals (HCP). This study aimed to gain deeper insights into the H2H transition process and to work towards eHealth interventions for its improvement, by applying an iterative methodology involving both CMC families and HCP as end-users., Methods: For 20-weeks, the Dutch Transitional Care Unit consortium collaborated with the Amsterdam University of Applied Sciences, HCP, and CMC families. The agile SCREAM approach was used, merging Design Thinking methods into five iterative sprints to stimulate creativity, ideation, and design. Continuous communication allowed rapid adaptation to new information and the refinement of solutions for subsequent sprints., Results: This iterative process revealed three domains of care - care coordination, social wellbeing, and emotional support - that were important to all stakeholders. These domains informed the development of our final prototype, 'Our Care Team', an application tailored to meet the H2H transition needs for CMC families and HCP., Conclusion: Complex processes like the H2H transition for CMC families require adaptive interventions that empower all stakeholders in their respective roles, to promote transitional care that is anticipatory, rather than reactive., Innovation: A collaborative methodology is needed, that optimizes existing resources and knowledge, fosters innovation through collaboration while using creative digital design principles. This way, we might be able to design eHealth solutions with end-users, not just for them., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Objective: The European Registries for Rare Endocrine Conditions (EuRRECa, eurreb.eu) includes an e-reporting registry (e-REC) used to perform surveillance of conditions within the European Reference Network (ERN) for rare endocrine conditions (Endo-ERN). The aim of this study was to report the experience of e-REC over the 3.5 years since its launch in 2018., Methods: Electronic reporting capturing new encounters of Endo-ERN conditions was performed monthly through a bespoke platform by clinicians registered to participate in e-REC from July 2018 to December 2021., Results: The number of centres reporting on e-REC increased to a total of 61 centres from 22 countries. A median of 29 (range 11, 45) paediatric and 32 (14, 51) adult centres had reported cases monthly. A total of 9715 and 4243 new cases were reported in adults (age ≥18 years) and children, respectively. In children, sex development conditions comprised 40% of all reported conditions and transgender cases were most frequently reported, comprising 58% of sex development conditions. The median number of sex development cases reported per centre per month was 0.6 (0, 38). Amongst adults, pituitary conditions comprised 44% of reported conditions and pituitary adenomas (69% of cases) were most commonly reported. The median number of pituitary cases reported per centre per month was 4 (0.4, 33)., Conclusions: e-REC has gained increasing acceptability over the last 3.5 years for capturing brief information on new encounters of rare conditions and shows wide variations in the rate of presentation of these conditions to centres within a reference network. Significance statement Endocrinology includes a very wide range of rare conditions and their occurrence is often difficult to measure. By using an electronic platform that allowed monthly reporting of new clinical encounters of several rare endocrine conditions within a defined network that consisted of several reference centres in Europe, the EuRRECa project shows that a programme of e-surveillance is feasible and acceptable. The data that have been collected by the e-reporting of rare endocrine conditions (e-REC) can allow the continuous monitoring of rare conditions and may be used for clinical benchmarking, designing new studies or recruiting to clinical trials.

Acute kidney injury (AKI) is frequently seen in patients with hemorrhagic shock due to hypotension, tissue hypoxia, and inflammation despite adequate resuscitation. There is a lack of information concerning the alteration of renal microcirculation and perfusion during shock and resuscitation. The aim of this study was to investigate the possible role of renal microcirculatory alterations on development of renal dysfunction in a pig model of non-traumatic hemorrhagic shock (HS) induced AKI.Fully instrumented female pigs were divided into the two groups as Control (n = 6) and HS (n = 11). HS was achieved by withdrawing blood until mean arterial pressure (MAP) reached around 50 mmHg. After an hour cessation period, fluid resuscitation with balanced crystalloid was started for the duration of 1 h. The systemic and renal hemodynamics, renal microcirculatory perfusion (contrast-enhanced ultrasound (CEUS)) and the sublingual microcirculation were measured.CEUS peak enhancement was significantly increased in HS during shock, early-, and late resuscitation indicating perfusion defects in the renal cortex (p < 0.05 vs. baseline, BL) despite a stable renal blood flow (RBF) and urine output. Following normalization of systemic hemodynamics, we observed persistent hypoxia (high lactate) and high red blood cell (RBC) velocity just after initiation of resuscitation resulting in further endothelial and renal damage as shown by increased plasma sialic acid (p < 0.05 vs. BL) and NGAL levels. We also showed that total vessel density (TVD) and functional capillary density (FCD) were depleted during resuscitation (p < 0.05).In this study, we showed that the correction of systemic hemodynamic variables may not be accompanied with the improvement of renal cortical perfusion, intra-renal blood volume and renal damage following fluid resuscitation. We suggest that the measurement of renal injury biomarkers, systemic and renal microcirculation can be used for guiding to the optimization of fluid therapies., (© 2023. The Author(s).)

Background: Digital health apps are becoming increasingly available for people living with diabetes, yet data silos continue to exist. This requires health care providers (HCPs) and patients to use multiple digital platforms to access health data., Objective: In this study, we gathered the perspectives of caregivers of children and youths living with type 1 diabetes (T1D) and pediatric diabetes HCPs in the user-centered design of TrustSphere, a secure, single-point-of-access, integrative digital health platform., Methods: We distributed web-based surveys to caregivers of children and youths living with T1D and pediatric diabetes HCPs in British Columbia, Canada. Surveys were designed using ordinal scales and had free-text questions. Survey items assessed key challenges, perceptions about digital trust and security, and potential desirable features for a digital diabetes platform., Results: Similar challenges were identified between caregivers of children and youths living with T1D (n=99) and HCPs (n=49), including access to mental health support, integration of diabetes technology and device data, and the ability to collaborate on care plans with their diabetes team. Caregivers and HCPs identified potential features that directly addressed their challenges, such as more accessible diabetes data and diabetes care plans. Caregivers had more trust in sharing their child's data digitally than HCPs. Most caregivers and HCPs stated that an integrative platform for T1D would support collaborative patient care., Conclusions: Caregiver and HCP perspectives gathered in this study will inform the early prototype of an integrative digital health platform. This prototype will be presented and iterated upon through a series of usability testing sessions with caregivers and HCPs to ensure the platform meets end users' needs., (©Fatema S Abdulhussein, Susan Pinkney, Matthias Görges, Tibor van Rooij, Shazhan Amed. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org), 13.07.2023.)

Objective: To develop 2 distinct preoperative and intraoperative risk scores to predict postoperative pancreatic fistula (POPF) after distal pancreatectomy (DP) to improve preventive and mitigation strategies, respectively., Background: POPF remains the most common complication after DP. Despite several known risk factors, an adequate risk model has not been developed yet., Methods: Two prediction risk scores were designed using data of patients undergoing DP in 2 Italian centers (2014-2016) utilizing multivariable logistic regression. The preoperative score (calculated before surgery) aims to facilitate preventive strategies and the intraoperative score (calculated at the end of surgery) aims to facilitate mitigation strategies. Internal validation was achieved using bootstrapping. These data were pooled with data from 5 centers from the United States and the Netherlands (2007-2016) to assess discrimination and calibration in an internal-external validation procedure., Results: Overall, 1336 patients after DP were included, of whom 291 (22%) developed POPF. The preoperative distal fistula risk score (preoperative D-FRS) included 2 variables: pancreatic neck thickness [odds ratio: 1.14; 95% confidence interval (CI): 1.11-1.17 per mm increase] and pancreatic duct diameter (OR: 1.46; 95% CI: 1.32-1.65 per mm increase). The model performed well with an area under the receiver operating characteristic curve of 0.83 (95% CI: 0.78-0.88) and 0.73 (95% CI: 0.70-0.76) upon internal-external validation. Three risk groups were identified: low risk (<10%), intermediate risk (10%-25%), and high risk (>25%) for POPF with 238 (18%), 684 (51%), and 414 (31%) patients, respectively. The intraoperative risk score (intraoperative D-FRS) added body mass index, pancreatic texture, and operative time as variables with an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.74-0.85)., Conclusions: The preoperative and the intraoperative D-FRS are the first validated risk scores for POPF after DP and are readily available at: http://www.pancreascalculator.com . The 3 distinct risk groups allow for personalized treatment and benchmarking., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Improving the prediction of blood glucose concentration may improve the quality of life of people living with type 1 diabetes by enabling them to better manage their care. Given the anticipated benefits of such a prediction, numerous methods have been proposed. Rather than attempting to predict glucose concentration, a deep learning framework for prediction is proposed in which prediction is performed using a scale for hypo- and hyper-glycemia risk. Using the blood glucose risk score formula proposed by Kovatchev et al., models with different architectures were trained, including, a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short-term memory (LSTM) network, and an encoder-like convolutional neural network (CNN). The models were trained using the OpenAPS Data Commons data set, comprising 139 individuals, each with tens of thousands of continuous glucose monitor (CGM) data points. The training set was composed of 7% of the data set, while the remaining was used for testing. Performance comparisons between the different architectures are presented and discussed. To evaluate these predictions, performance results are compared with the last measurement (LM) prediction, through a sample-and-hold approach continuing the last known measurement forward. The results obtained are competitive when compared to other deep learning methods. A root mean squared error (RMSE) of 16 mg/dL, 24 mg/dL, and 37 mg/dL were obtained for CNN prediction horizons of 15, 30, and 60 min, respectively. However, no significant improvements were found for the deep learning models compared to LM prediction. Performance was found to be highly dependent on architecture and the prediction horizon. Lastly, a metric to assess model performance by weighing each prediction point error with the corresponding blood glucose risk score is proposed. Two main conclusions are drawn. Firstly, going forward, there is a need to benchmark model performance using LM prediction to enable the comparison between results obtained from different data sets. Secondly, model-agnostic data-driven deep learning models may only be meaningful when combined with mechanistic physiological models; here, it is argued that neural ordinary differential equations may combine the best of both approaches. These findings are based on the OpenAPS Data Commons data set and are to be validated in other independent data sets.