Pediatric Delirium Educational Tool Development With Intensive Care Unit Clinicians and Caregivers in Canada: Focus Group Study.

Background: Pediatric intensive care unit (PICU)-associated delirium contributes to a decline in postdischarge quality of life, with worse outcomes for individuals with delayed identification. As delirium screening rates remain low within PICUs, caregivers may be able to assist with early detection, for which they need more education, as awareness of pediatric delirium among caregivers remains limited. Objective: This study aimed to develop an educational tool for caregivers to identify potential delirium symptoms during their child's PICU stay, educate them on how to best support their child if they experience delirium, and guide them to relevant family resources. Methods: Web-based focus groups were conducted at a tertiary pediatric hospital with expected end users of the tool (ie, PICU health care professionals and caregivers of children with an expected PICU length of stay of over 48 h) to identify potential educational information for inclusion in a family resource guide and to identify strategies for effective implementation. Data were analyzed thematically to generate requirements to inform prototype development. Participants then provided critical feedback on the initial prototype, which guided the final design. Results: In all, 24 participants (18 health care professionals and 6 caregivers) attended 7 focus groups. Participants identified five informational sections for inclusion: (1) delirium definition, (2) key features of delirium (signs and symptoms), (3) postdischarge outcomes associated with delirium, (4) tips to inform family-centered care, and (5) education or supportive resources. Participants identified seven design requirements: information should (1) be presented in an order that resembles the structure of the clinical discussion around delirium; (2) increase accessibility, recall, and preparedness by providing multiple formats; (3) aim to reduce stress by implementing positive framing; (4) minimize cognitive load to ensure adequate information processing; (5) provide supplemental electronic resources via QR codes; (6) emphasize collaboration between caregivers and the health care team; and (7) use prompting questions to act as a call to action for caregivers. Conclusions: Key design requirements derived from end-user feedback were established and guided the development of a novel pediatric delirium education tool. Implementing this tool into regular practice has the potential to reduce distress and assist in the early recognition and treatment of delirium in the PICU domain. Future evaluation of its clinical utility is necessary.

An Individualized Postoperative Pain Risk Communication Tool for Use in Pediatric Surgery: Co-Design and Usability Evaluation.

BACKGROUND: Risk identification and communication tools have the potential to improve health care by supporting clinician-patient or family discussion of treatment risks and benefits and helping patients make more informed decisions; however, they have yet to be tailored to pediatric surgery. User-centered design principles can help to ensure the successful development and uptake of health care tools. OBJECTIVE: We aimed to develop and evaluate the usability of an easy-to-use tool to communicate a child's risk of postoperative pain to improve informed and collaborative preoperative decision-making between clinicians and families. METHODS: With research ethics board approval, we conducted web-based co-design sessions with clinicians and family participants (people with lived surgical experience and parents of children who had recently undergone a surgical or medical procedure) at a tertiary pediatric hospital. Qualitative data from these sessions were analyzed thematically using NVivo (Lumivero) to identify design requirements to inform the iterative redesign of an existing prototype. We then evaluated the usability of our final prototype in one-to-one sessions with a new group of participants, in which we measured mental workload with the National Aeronautics and Space Administration (NASA) Task Load Index (TLX) and user satisfaction with the Post-Study System Usability Questionnaire (PSSUQ). RESULTS: A total of 12 participants (8 clinicians and 4 family participants) attended 5 co-design sessions. The 5 requirements were identified: (A) present risk severity descriptively and visually; (B) ensure appearance and navigation are user-friendly; (C) frame risk identification and mitigation strategies in positive terms; (D) categorize and describe risks clearly; and (E) emphasize collaboration and effective communication. A total of 12 new participants (7 clinicians and 5 family participants) completed a usability evaluation. Tasks were completed quickly (range 5-17 s) and accurately (range 11/12, 92% to 12/12, 100%), needing only 2 requests for assistance. The median (IQR) NASA TLX performance score of 78 (66-89) indicated that participants felt able to perform the required tasks, and an overall PSSUQ score of 2.1 (IQR 1.5-2.7) suggested acceptable user satisfaction with the tool. CONCLUSIONS: The key design requirements were identified, and that guided the prototype redesign, which was positively evaluated during usability testing. Implementing a personalized risk communication tool into pediatric surgery can enhance the care process and improve informed and collaborative presurgical preparation and decision-making between clinicians and families of pediatric patients.

Enhanced recovery support for people with eating disorders during the COVID-19 pandemic: quality improvement using a web-based, stepped-care programme in Canada.

BACKGROUND: Throughout the COVID-19 pandemic, the number of individuals struggling with eating disorders (EDs) increased substantially. Body Brave (a not-for-profit) created and implemented a web-based stepped-care Recovery Support Programme (RSP) to improve access to community-based ED services. This quality improvement study describes the RSP and assesses its ability to deliver timely access to treatment and platform engagement. METHODS: We conducted a retrospective cohort study comparing access to, and use of Body Brave services 6 months before and 12 months after implementation of the RSP platform (using 6-month increments for two postimplementation periods). Primary programme quality measures included registration requests, number of participants onboarded and time to access services; secondary measures included use of RSP action plans, attendance for recovery sessions and workshops, number of participants accessing treatment and text-based patient experience data. RESULTS: A substantial increase in registration requests was observed during the first postimplementation period compared with the preimplementation period (176.5 vs 85.5; p=0.028). When compared with the preimplementation period, the second postimplementation observed a significantly larger percentage of successfully onboarded participants (76.6 vs 37.9; p<0.01) and a reduction in the number of days to access services (2 days vs 31 days; p<0.01). Although participant feedback rates were low, many users found the RSP helpful, easy to access, user-friendly and were satisfied overall. Users provided suggestions for improvement (eg, a platform instructional video, offer multiple times of day for live sessions and drop-in hours). CONCLUSIONS: Although clinical benefit needs to be assessed, our findings demonstrate that the RSP enabled participants to quickly onboard and access initial services and have informed subsequent improvements. Understanding initial programme effects and usage will help assess the feasibility of adapting and expanding the RSP across Canada to address the urgent need for low-barrier, patient-centred ED care.

Dashboard of Short-Term Postoperative Patient Outcomes for Anesthesiologists: Development and Preliminary Evaluation.

BACKGROUND: Anesthesiologists require an understanding of their patients' outcomes to evaluate their performance and improve their practice. Traditionally, anesthesiologists had limited information about their surgical outpatients' outcomes due to minimal contact post discharge. Leveraging digital health innovations for analyzing personal and population outcomes may improve perioperative care. BC Children's Hospital's postoperative follow-up registry for outpatient surgeries collects short-term outcomes such as pain, nausea, and vomiting. Yet, these data were previously not available to anesthesiologists. OBJECTIVE: This quality improvement study aimed to visualize postoperative outcome data to allow anesthesiologists to reflect on their care and compare their performance with their peers. METHODS: The postoperative follow-up registry contains nurse-reported postoperative outcomes, including opioid and antiemetic administration in the postanesthetic care unit (PACU), and family-reported outcomes, including pain, nausea, and vomiting, within 24 hours post discharge. Dashboards were iteratively co-designed with 5 anesthesiologists, and a department-wide usability survey gathered anesthesiologists' feedback on the dashboards, allowing further design improvements. A final dashboard version has been deployed, with data updated weekly. RESULTS: The dashboard contains three sections: (1) 24-hour outcomes, (2) PACU outcomes, and (3) a practice profile containing individual anesthesiologist's case mix, grouped by age groups, sex, and surgical service. At the time of evaluation, the dashboard included 24-hour data from 7877 cases collected from September 2020 to February 2023 and PACU data from 8716 cases collected from April 2021 to February 2023. The co-design process and usability evaluation indicated that anesthesiologists preferred simpler designs for data summaries but also required the ability to explore details of specific outcomes and cases if needed. Anesthesiologists considered security and confidentiality to be key features of the design and most deemed the dashboard information useful and potentially beneficial for their practice. CONCLUSIONS: We designed and deployed a dynamic, personalized dashboard for anesthesiologists to review their outpatients' short-term postoperative outcomes. This dashboard facilitates personal reflection on individual practice in the context of peer and departmental performance and, hence, the opportunity to evaluate iterative practice changes. Further work is required to establish their effect on improving individual and department performance and patient outcomes.

Designing a Collaborative Patient-Centered Digital Health Platform for Pediatric Diabetes Care in British Columbia: Formative Needs Assessment by Caregivers of Children and Youths Living With Type 1 Diabetes and Health Care Providers.

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.

Using Human-Centered Design and Cocreation to Create the Live 5-2-1-0 Mobile App to Promote Healthy Behaviors in Children: App Design and Development.

BACKGROUND: The prevalence of obesity among Canadian children is rising, partly because of increasingly obesogenic environments that limit opportunities for physical activity and healthy nutrition. Live 5-2-1-0 is a community-based multisectoral childhood obesity prevention initiative that engages stakeholders to promote and support the message of consuming ≥5 servings of vegetables and fruits, having <2 hours of recreational screen time, participating in ≥1 hour of active play, and consuming 0 sugary drinks every day. A Live 5-2-1-0 Toolkit for health care providers (HCPs) was previously developed and piloted in 2 pediatric clinics at British Columbia Children's Hospital. OBJECTIVE: This study aimed to co-create, in partnership with children, parents, and HCPs, a Live 5-2-1-0 mobile app that supports healthy behavior change and could be used as part of the Live 5-2-1-0 Toolkit for HCPs. METHODS: Three focus groups (FGs) were conducted using human-centered design and participatory approaches. In FG 1, children (separately) and parents and HCPs (together) participated in sessions on app conceptualization and design. Researchers and app developers analyzed and interpreted qualitative data from FG 1 in an ideation session, and key themes were subsequently presented separately to parents, children, and HCPs in FG-2 (co-creation) sessions to identify desired app features. Parents and children tested a prototype in FG 3, provided feedback on usability and content, and completed questionnaires. Thematic analysis and descriptive statistics were used for the qualitative and quantitative data, respectively. RESULTS: In total, 14 children (mean age 10.2, SD 1.3 years; 5/14, 36% male; 5/14, 36% White), 12 parents (9/12, 75% aged 40-49 years; 2/12, 17% male; 7/12, 58% White), and 18 HCPs participated; most parents and children (20/26, 77%) participated in ≥2 FGs. Parents wanted an app that empowered children to adopt healthy behaviors using internal motivation and accountability, whereas children described challenge-oriented goals and family-based activities as motivating. Parents and children identified gamification, goal setting, daily steps, family-based rewards, and daily notifications as desired features; HCPs wanted baseline behavior assessments and to track users' behavior change progress. Following prototype testing, parents and children reported ease in completing tasks, with a median score of 7 (IQR 6-7) on a 7-point Likert scale (1=very difficult; 7=very easy). Children liked most suggested rewards (28/37, 76%) and found 79% (76/96) of suggested daily challenges (healthy behavior activities that users complete to achieve their goal) realistic to achieve. Participant suggestions included strategies to maintain users' interest and content that further motivates healthy behavior change. CONCLUSIONS: Co-creating a mobile health app with children, parents, and HCPs was feasible. Stakeholders desired an app that facilitated shared decision-making with children as active agents in behavior change. Future research will involve clinical implementation and assessment of the usability and effectiveness of the Live 5-2-1-0 app.

Prediction of Blood Risk Score in Diabetes Using Deep Neural Networks.

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.

Identifying Risk Factors, Patient-Reported Experience and Outcome Measures, and Data Capture Tools for an Individualized Pain Prediction Tool in Pediatrics: Focus Group Study.

BACKGROUND: The perioperative period is a data-rich environment with potential for innovation through digital health tools and predictive analytics to optimize patients' health with targeted prehabilitation. Although some risk factors for postoperative pain following pediatric surgery are already known, the systematic use of preoperative information to guide personalized interventions is not yet widespread in clinical practice. OBJECTIVE: Our long-term goal is to reduce the incidence of persistent postsurgical pain (PPSP) and long-term opioid use in children by developing personalized pain risk prediction models that can guide clinicians and families to identify targeted prehabilitation strategies. To develop such a system, our first objective was to identify risk factors, outcomes, and relevant experience measures, as well as data collection tools, for a future data collection and risk modeling study. METHODS: This study used a patient-oriented research methodology, leveraging parental/caregiver and clinician expertise. We conducted virtual focus groups with participants recruited at a tertiary pediatric hospital; each session lasted approximately 1 hour and was composed of clinicians or family members (people with lived surgical experience and parents of children who had recently undergone a procedure requiring general anesthesia) or both. Data were analyzed thematically to identify potential risk factors for pain, as well as relevant patient-reported experience and outcome measures (PREMs and PROMs, respectively) that can be used to evaluate the progress of postoperative recovery at home. This guidance was combined with a targeted literature review to select tools to collect risk factor and outcome information for implementation in a future study. RESULTS: In total, 22 participants (n=12, 55%, clinicians and n=10, 45%, family members) attended 10 focus group sessions; participants included 12 (55%) of 22 persons identifying as female, and 12 (55%) were under 50 years of age. Thematic analysis identified 5 key domains: (1) demographic risk factors, including both child and family characteristics; (2) psychosocial risk factors, including anxiety, depression, and medical phobias; (3) clinical risk factors, including length of hospital stay, procedure type, medications, and pre-existing conditions; (4) PREMs, including patient and family satisfaction with care; and (5) PROMs, including nausea and vomiting, functional recovery, and return to normal activities of daily living. Participants further suggested desirable functional requirements, including use of standardized and validated tools, and longitudinal data collection, as well as delivery modes, including electronic, parent proxy, and self-reporting, that can be used to capture these metrics, both in the hospital and following discharge. Established PREM/PROM questionnaires, pain-catastrophizing scales (PCSs), and substance use questionnaires for adolescents were subsequently selected for our proposed data collection platform. CONCLUSIONS: This study established 5 key data domains for identifying pain risk factors and evaluating postoperative recovery at home, as well as the functional requirements and delivery modes of selected tools with which to capture these metrics both in the hospital and after discharge. These tools have been implemented to generate data for the development of personalized pain risk prediction models.

Identification of Requirements for a Postoperative Pediatric Pain Risk Communication Tool: Focus Group Study With Clinicians and Family Members.

BACKGROUND: Pediatric surgery is associated with a risk of postoperative pain that can impact the family's quality of life. Although some risk factors for postoperative pain are known, these are often not consistently communicated to families. In addition, although tools for risk communication exist in other domains, none are tailored to pediatric surgery. OBJECTIVE: As part of a larger project to develop pain risk prediction tools, we aimed to design an easy-to-use tool to effectively communicate a child's risk of postoperative pain to both clinicians and family members. METHODS: With research ethics board approval, we conducted virtual focus groups (~1 hour each) comprising clinicians and family members (people with lived surgical experience and parents of children who had recently undergone surgery/medical procedures) at a tertiary pediatric hospital to understand and evaluate potential design approaches and strategies for effectively communicating and visualizing postoperative pain risk. Data were analyzed thematically to generate design requirements and to inform iterative prototype development. RESULTS: In total, 19 participants (clinicians: n=10, 53%; family members: n=9, 47%) attended 6 focus group sessions. Participants indicated that risk was typically communicated verbally by clinicians to patients and their families, with severity indicated using a descriptive or a numerical representation or both, which would only occasionally be contextualized. Participants indicated that risk communication tools were seldom used but that families would benefit from risk information, time to reflect on the information, and follow-up with questions. In addition, 9 key design requirements and feature considerations for effective risk communication were identified: (1) present risk information clearly and with contextualization, (2) quantify the risk and contextualize it, (3) include checklists for preoperative family preparation, (4) provide risk information digitally to facilitate recall and sharing, (5) query the family's understanding to ensure comprehension of risk, (6) present the risk score using multimodal formats, (7) use color coding that is nonthreatening and avoids limitations with color blindness, (8) present the most significant factors contributing to the risk prediction, and (9) provide risk mitigation strategies to potentially decrease the patient's level of risk. CONCLUSIONS: Key design requirements for a pediatric postoperative pain risk visualization tool were established and guided the development of an initial prototype. Implementing a risk communication tool into clinical practice has the potential to bridge existing gaps in the accessibility, utilization, and comprehension of personalized risk information between health care professionals and family members. Future iterative codesign and clinical evaluation of this risk communication tool are needed to confirm its utility in practice.

Has Virtual Care Arrived? A Survey of Rural Canadian Providers During the Early Stages of the COVID-19 Pandemic.

We investigated the uptake and perceptions of virtual care solutions by rural Canadian primary and specialist providers during the early phase (May-June 2020) of the COVID-19 pandemic. A web-based, cross-sectional survey of rural primary and specialty care providers examined types of virtual care platforms used (eg, phone, video), appointment length, experience and satisfaction with the solution used, plans for future use of virtual care, and patients' use of virtual care services. Targeted participants were actively-practicing providers in rural Western Canada who were emailed an invitation for the study and its survey link. Fifty-nine providers (26% response rate) completed the survey. During the pandemic, 78% of providers reported using virtual care for more than 60% of their appointments, while only 3% did so frequently pre-pandemic. Most providers used phone consultations, despite believing that video provided a better virtual visit. Key barriers included workflow interruptions, unique concerns about quality of care, remuneration and sustainability, or poor internet access and bandwidth for both providers and patients. The key opportunity noted was improved access to care. While most virtual care visits were not conducted using video technologies, overall virtual care resulted in high provider satisfaction, while not increasing workload. Virtual care will continue to play an important role in future rural care practice; however, sustainability will require both provider-level and system-level changes.

Empowering Patients Through Virtual Care Delivery: Qualitative Study With Micropractice Clinic Patients and Health Care Providers.

BACKGROUND: Prior to the wider adoption of digital health technologies during the COVID-19 pandemic, applications of virtual care were largely limited to specialist visits and remote care using telehealth (phone or video) applications. Data sharing approaches using tethered patient portals were mostly built around hospitals and larger care systems. These portals offer opportunities for improved communication, but despite a belief that care has improved, they have so far shown few outcome improvements beyond medication adherence. Less is known about use of virtual care and related tools in the outpatient context and particularly in rural community contexts. OBJECTIVE: This study aims to reflect on the opportunities and barriers for sustainable virtual care through an example of a digitally enabled rural micropractice, which has provided 10%-15% virtual care since 2016 and 70% virtual care since March 2020. METHODS: Three focus groups, 1 with providers (physician and medical office manager) and 2 with a total of 8 patients from a rural micropractice in British Columbia, were conducted in November 2020 and December 2020. Virtual care delivery was explored through the topics of communication approach, mixing virtual and in-person care, the practice team's journey in developing these approaches, and provider and patient satisfaction with the care model. Interviews were transcribed, checked for accuracy against recordings, and thematically analyzed. RESULTS: Both patients and providers reported ease of communication and high satisfaction. Either could initiate communication, and patients found the ability to share health information asynchronously through the portal allowed time to reflect and prepare their thoughts. Patients were highly engaged and reported feeling empowered and true partners in their health care, although they noted limited care coordination with specialists. The mix of virtual and in-person visits was highly regarded by patients and providers, and patients reported feeling safe and cared for 24/7, although both expressed concern about work spilling into the provider's home life. The physician worried about missed diagnoses with virtual care. With respect to establishing the micropractice, solutions took about 5 years to optimize, with providers noting a learning curve requiring technical support for both themselves and their patients and a willingness to respond to patient feedback to identify the best solutions. Despite a mature virtual practice, patients reported deferred care due to COVID-19. CONCLUSIONS: The micropractice's hybrid care model encouraged patients to be true partners in their care and resulted in high patient engagement and satisfaction; yet, success may rely on the patient population being willing to engage and being comfortable with technology. Barriers lie in gaps in care coordination and provider fear that signs or symptoms more evident with an in-person exam could be missed. Even in this setting, deferral of care in light of COVID-19 was present, and opportunities to address care gaps should be sought.

Effectiveness of Mobile Apps in Promoting Healthy Behavior Changes and Preventing Obesity in Children: Systematic Review.

BACKGROUND: Mobile apps have been increasingly incorporated into healthy behavior promotion interventions targeting childhood obesity. However, their effectiveness remains unclear. OBJECTIVE: This paper aims to conduct a systematic review examining the effectiveness of mobile apps aimed at preventing childhood obesity by promoting health behavior changes in diet, physical activity, or sedentary behavior in children aged 8 to 12 years. METHODS: MEDLINE, Embase, PsycINFO, CINAHL, and ERIC were systematically searched for peer-reviewed primary studies from January 2008 to July 2021, which included children aged 8 to 12 years; involved mobile app use; and targeted at least one obesity-related factor, including diet, physical activity, or sedentary behavior. Data extraction and risk of bias assessments were conducted by 2 authors. RESULTS: Of the 13 studies identified, most used a quasi-experimental design (n=8, 62%). Significant improvements in physical activity (4/8, 50% studies), dietary outcomes (5/6, 83% studies), and BMI (2/6, 33% studies) were reported. All 6 multicomponent interventions and 57% (4/7) of standalone interventions reported significant outcomes in ≥1 behavioral change outcome measured (anthropometric, physical activity, dietary, and screen time outcomes). Gamification, behavioral monitoring, and goal setting were common features of the mobile apps used in these studies. CONCLUSIONS: Apps for health behavior promotion interventions have the potential to increase the adoption of healthy behaviors among children; however, their effectiveness in improving anthropometric measures remains unclear. Further investigation of studies that use more rigorous study designs, as well as mobile apps as a standalone intervention, is needed.

Blood pressure nomograms for children undergoing general anesthesia, stratified by age and anesthetic type, using data from a retrospective cohort at a tertiary pediatric center.

Reference values for non-invasive blood pressure (NIBP) are available for children undergoing general anesthesia, but have not been analyzed by type of anesthetic. This study establishes age-specific pediatric NIBP reference values, stratified by anesthetic type: inhalational anesthesia (IHA), total intravenous anesthesia (TIVA), and mostly intravenous anesthesia (MIVA, an inhalational induction followed by intravenous maintenance of anesthesia). NIBP measurements were extracted from a de-identified vital signs database for children < 19 years undergoing anesthesia between Jan/2013-Dec/2016, excluding cardiac surgery. We automatically rejected artifacts and randomly sampled 20 NIBP values per case. Anesthetic phase (induction/maintenance) was identified using operating room booking times for procedure start, and anesthetic types were identified based on intraoperative minimum alveolar concentration values in the different phases of the anesthetic. From 36,347 cases in our operating room booking system, we matched 24,457 cases with available vital signs. Of these, 20,613 (84%) had valid NIBP data and could be assigned to one anesthetic type: TIVA 11,819 [57%], IHA 4,752 [23%], and MIVA 4,042 [20%]. Mean NIBP during anesthesia increased with age, from median values of 48 mmHg (TIVA), 45 mmHg (IHA), and 41 mmHg (MIVA) in neonates, to 70 mmHg (TIVA), 68 mmHg (IHA), and 64 mmHg (MIVA) in 18-year-olds, respectively. In children < 1 year, mean NIBP values were 4 mmHg higher with TIVA than IHA (p < 0.001). These pediatric NIBP reference values contribute to ongoing debate about alarm limits based on age and anesthetic type, and may motivate prospective studies into the effects of different anesthesia regimes on vital signs.

Prediction Model Performance With Different Imputation Strategies: A Simulation Study Using a North American ICU Registry.

OBJECTIVES: To evaluate the performance of pragmatic imputation approaches when estimating model coefficients using datasets with varying degrees of data missingness. DESIGN: Performance in predicting observed mortality in a registry dataset was evaluated using simulations of two simple logistic regression models with age-specific criteria for abnormal vital signs (mentation, systolic blood pressure, respiratory rate, WBC count, heart rate, and temperature). Starting with a dataset with complete information, increasing degrees of biased missingness of WBC and mentation were introduced, depending on the values of temperature and systolic blood pressure, respectively. Missing data approaches evaluated included analysis of complete cases only, assuming missing data are normal, and multiple imputation by chained equations. Percent bias and root mean square error, in relation to parameter estimates obtained from the original data, were evaluated as performance indicators. SETTING: Data were obtained from the Virtual Pediatric Systems, LLC, database (Los Angeles, CA), which provides clinical markers and outcomes in prospectively collected records from 117 PICUs in the United States and Canada. PATIENTS: Children admitted to a participating PICU in 2017, for whom all required data were available. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Simulations demonstrated that multiple imputation by chained equations is an effective strategy and that even a naive implementation of multiple imputation by chained equations significantly outperforms traditional approaches: the root mean square error for model coefficients was lower using multiple imputation by chained equations in 90 of 99 of all simulations (91%) compared with discarding cases with missing data and lower in 97 of 99 (98%) compared with models assuming missing values are in the normal range. Assuming missing data to be abnormal was inferior to all other approaches. CONCLUSIONS: Analyses of large observational studies are likely to encounter the issue of missing data, which are likely not missing at random. Researchers should always consider multiple imputation by chained equations (or similar imputation approaches) when encountering even only small proportions of missing data in their work.

Perceptions of Personal Health Records in Rural Primary Health Clinics in Canada - Patient Perspectives.

Personal health records are increasingly being deployed in healthcare settings. In this study we explored patients' perceptions of personal health records in a rural community in Canada where a primary health network is being deployed. A focus group was held and data were thematically analysed. All patients used technology on a regular basis. Themes included communication and information sharing, issues with access to prior health records, data content and data control and features and functions for continuity of care. Participants expressed desire to be owners of their own record, but described instances where they might be too ill to do so. Participants were hopeful that the functions of a personal health record might help to overcome frustrations with current fragmented information and open to using technologies as part of their care process. Personal health records are promising technologies to overcome fragmented care in rural communities.

An Artificial Neural Network-Based Pediatric Mortality Risk Score: Development and Performance Evaluation Using Data From a Large North American Registry.

BACKGROUND: In the pediatric intensive care unit (PICU), quantifying illness severity can be guided by risk models to enable timely identification and appropriate intervention. Logistic regression models, including the pediatric index of mortality 2 (PIM-2) and pediatric risk of mortality III (PRISM-III), produce a mortality risk score using data that are routinely available at PICU admission. Artificial neural networks (ANNs) outperform regression models in some medical fields. OBJECTIVE: In light of this potential, we aim to examine ANN performance, compared to that of logistic regression, for mortality risk estimation in the PICU. METHODS: The analyzed data set included patients from North American PICUs whose discharge diagnostic codes indicated evidence of infection and included the data used for the PIM-2 and PRISM-III calculations and their corresponding scores. We stratified the data set into training and test sets, with approximately equal mortality rates, in an effort to replicate real-world data. Data preprocessing included imputing missing data through simple substitution and normalizing data into binary variables using PRISM-III thresholds. A 2-layer ANN model was built to predict pediatric mortality, along with a simple logistic regression model for comparison. Both models used the same features required by PIM-2 and PRISM-III. Alternative ANN models using single-layer or unnormalized data were also evaluated. Model performance was compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC) and their empirical 95% CIs. RESULTS: Data from 102,945 patients (including 4068 deaths) were included in the analysis. The highest performing ANN (AUROC 0.871, 95% CI 0.862-0.880; AUPRC 0.372, 95% CI 0.345-0.396) that used normalized data performed better than PIM-2 (AUROC 0.805, 95% CI 0.801-0.816; AUPRC 0.234, 95% CI 0.213-0.255) and PRISM-III (AUROC 0.844, 95% CI 0.841-0.855; AUPRC 0.348, 95% CI 0.322-0.367). The performance of this ANN was also significantly better than that of the logistic regression model (AUROC 0.862, 95% CI 0.852-0.872; AUPRC 0.329, 95% CI 0.304-0.351). The performance of the ANN that used unnormalized data (AUROC 0.865, 95% CI 0.856-0.874) was slightly inferior to our highest performing ANN; the single-layer ANN architecture performed poorly and was not investigated further. CONCLUSIONS: A simple ANN model performed slightly better than the benchmark PIM-2 and PRISM-III scores and a traditional logistic regression model trained on the same data set. The small performance gains achieved by this two-layer ANN model may not offer clinically significant improvement; however, further research with other or more sophisticated model designs and better imputation of missing data may be warranted.

Association of dexmedetomidine with recovery room and hospital discharge times: A retrospective cohort analysis.

BACKGROUND: Dexmedetomidine is a useful anesthetic adjunct, increasingly popular during pediatric surgery and procedural sedation. Its half-life of 2-3 hours might prolong recovery and discharge times when compared with an un-supplemented propofol anesthetic. This may create an additional burden in a busy post-anesthetic care unit (PACU). AIM: To investigate whether intraoperative adjuvant dexmedetomidine delays PACU discharge in patients undergoing propofol anesthesia for day surgery or procedural investigations with minimal anticipated post-procedural pain. METHODS: We conducted a retrospective review of outpatient procedures performed during a six-month period including pediatric patients, ASA physical status I-III, who underwent intravenous anesthesia with propofol and remifentanil for magnetic resonance imaging (MRI), strabismus repair, upper gastrointestinal endoscopy, or combined upper/lower gastrointestinal endoscopy. Patients receiving a sedative premedication, long-acting opioids, or volatile anesthetics for maintenance of anesthesia, were excluded. Duration of PACU stay was compared for patients who did or did not receive intraoperative dexmedetomidine in the four procedure groups. RESULTS: Charts were reviewed for 359 patients; 130 (36%) received dexmedetomidine. Median differences in duration of PACU stay for dexmedetomidine versus non-dexmedetomidine cases were: 5 minutes (95%CI 0 to 10, p=0.037) for MRI; 5 minutes (95%CI -3 to 15, p=0.258) for strabismus surgery; 7 minutes (95%CI 3 to 10, p<0.001) for upper endoscopy; and 5 minutes (95%CI 1 to 12, p=0.021) for combined upper/lower endoscopy. Linear regression (F=61.1, adjusted R2 =0.40) indicated a significant relationship between dexmedetomidine dose (estimate 14.6 minutes per µg/kg, 95%CI 8.2 to 21.1, p<0.001) and duration of PACU stay. CONCLUSION: We found evidence for a small association of intraoperative dexmedetomidine with duration of recovery from propofol anesthesia for a set of common outpatient procedures, with a potential dose relationship equivalent to approximately 15 minutes delay per µg/kg dexmedetomidine administered. Future research into the benefits of dexmedetomidine in pediatric anesthesia should further evaluate this relationship.

A majority of uninfected adults show preexisting antibody reactivity against SARS-CoV-2.

Preexisting cross-reactivity to SARS-CoV-2 occurs in the absence of prior viral exposure. However, this has been difficult to quantify at the population level due to a lack of reliably defined seroreactivity thresholds. Using an orthogonal antibody testing approach, we estimated that about 0.6% of nontriaged adults from the greater Vancouver, Canada, area between May 17 and June 19, 2020, showed clear evidence of a prior SARS-CoV-2 infection, after adjusting for false-positive and false-negative test results. Using a highly sensitive multiplex assay and positive/negative thresholds established in infants in whom maternal antibodies have waned, we determined that more than 90% of uninfected adults showed antibody reactivity against the spike protein, receptor-binding domain (RBD), N-terminal domain (NTD), or the nucleocapsid (N) protein from SARS-CoV-2. This seroreactivity was evenly distributed across age and sex, correlated with circulating coronaviruses' reactivity, and was partially outcompeted by soluble circulating coronaviruses' spike. Using a custom SARS-CoV-2 peptide mapping array, we found that this antibody reactivity broadly mapped to spike and to conserved nonstructural viral proteins. We conclude that most adults display preexisting antibody cross-reactivity against SARS-CoV-2, which further supports investigation of how this may impact the clinical severity of COVID-19 or SARS-CoV-2 vaccine responses.

The Effect of Low-Dose Intraoperative Ketamine on Closed-Loop-Controlled General Anesthesia: A Randomized Controlled Equivalence Trial.

BACKGROUND: Closed-loop control of propofol-remifentanil anesthesia using the processed electroencephalography depth-of-hypnosis index provided by the NeuroSENSE monitor (WAVCNS) has been previously described. The purpose of this placebo-controlled study was to evaluate the performance (percentage time within ±10 units of the setpoint during the maintenance of anesthesia) of a closed-loop propofol-remifentanil controller during induction and maintenance of anesthesia in the presence of a low dose of ketamine. METHODS: Following ethical approval and informed consent, American Society of Anesthesiologist (ASA) physical status I-II patients aged 19-54 years, scheduled for elective orthopedic surgery requiring general anesthesia for >60 minutes duration, were enrolled in a double-blind randomized, placebo-controlled, 2-group equivalence trial. Immediately before induction of anesthesia, participants in the ketamine group received a 0.25 mg·kg-1 bolus of intravenous ketamine over 60 seconds followed by a continuous 5 µg·kg-1·min-1 infusion for up to 45 minutes. Participants in the control group received an equivalent volume of normal saline. After the initial study drug bolus, closed-loop induction of anesthesia was initiated; propofol and remifentanil remained under closed-loop control until the anesthetic was tapered and turned off at the anesthesiologist's discretion. An equivalence range of ±8.99% was assumed for comparing controller performance. RESULTS: Sixty patients participated: 41 males, 54 ASA physical status I, with a median (interquartile range [IQR]) age of 29 [23, 38] years and weight of 82 [71, 93] kg. Complete data were available from 29 cases in the ketamine group and 27 in the control group. Percentage time within ±10 units of the WAVCNS setpoint was median [IQR] 86.6% [79.7, 90.2] in the ketamine group and 86.4% [76.5, 89.8] in the control group (median difference, 1.0%; 95% confidence interval [CI] -3.6 to 5.0). Mean propofol dose during maintenance of anesthesia for the ketamine group was higher than for the control group (median difference, 24.9 µg·kg-1·min-1; 95% CI, 6.5-43.1; P = .005). CONCLUSIONS: Because the 95% CI of the difference in controller performance lies entirely within the a priori equivalence range, we infer that this analgesic dose of ketamine did not alter controller performance. Further study is required to confirm the finding that mean propofol dosing was higher in the ketamine group, and to investigate the implication that this dose of ketamine may have affected the WAVCNS.