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Abstract Multi-system inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection in children, and there is a critical need to unfold its highly heterogeneous disease patterns. Our objective was to characterize the illness spectrum of MIS-C for improved recognition and management. We conducted a retrospective cohort study using data from March 1, 2020–September 30, 2022, in 8 pediatric medical centers from PEDSnet. We included 1139 children hospitalized with MIS-C and used their demographics, symptoms, conditions, laboratory values, and medications for analyses. We applied heterogeneity-adaptive latent class analyses and identified three latent classes. We further characterized the sociodemographic and clinical characteristics of the latent classes and evaluated their temporal patterns. Class 1 (47.9%) represented children with the most severe presentation, with more admission to the ICU, higher inflammatory markers, hypotension/shock/dehydration, cardiac involvement, acute kidney injury and respiratory involvement. Class 2 (23.3%) represented a moderate presentation, with 4–6 organ systems involved, and some overlapping features with acute COVID-19. Class 3 (28.8%) represented a mild presentation. Our results indicated that MIS-C has a spectrum of clinical severity ranging from mild to severe and the proportion of severe or critical MIS-C decreased over time.

Abstract Background Electronic health records (EHRs) data provide an opportunity to collect patient information rapidly, efficiently and at scale. National collaborative research networks, such as PEDSnet, aggregate EHRs data across institutions, enabling rapid identification of pediatric disease cohorts and generating new knowledge for medical conditions. To date, aggregation of EHR data has had limited applications in advancing our understanding of mental health (MH) conditions, in part due to the limited research in clinical informatics, necessary for the translation of EHR data to child mental health research. Methods In this cohort study, a comprehensive EHR-based typology was developed by an interdisciplinary team, with expertise in informatics and child and adolescent psychiatry, to query aggregated, standardized EHR data for the full spectrum of MH conditions (disorders/symptoms and exposure to adverse childhood experiences (ACEs), across 13 years (2010–2023), from 9 PEDSnet centers. Patients with and without MH disorders/symptoms (without ACEs), were compared by age, gender, race/ethnicity, insurance, and chronic physical conditions. Patients with ACEs alone were compared with those that also had MH disorders/symptoms. Prevalence estimates for patients with 1+ disorder/symptoms and for specific disorders/symptoms and exposure to ACEs were calculated, as well as risk for developing MH disorder/symptoms. Results The EHR study data set included 7,852,081 patients

Rationale & Objective: PRESERVE seeks to provide new knowledge to inform shared decision-making regarding blood pressure (BP) management for pediatric chronic kidney disease (CKD). PRESERVE will compare the effectiveness of alternative strategies for monitoring and treating hypertension on preserving kidney function; expand the National Patient-Centered Clinical Research Network (PCORnet) common data model by adding pediatric- and kidney-specific variables and linking electronic health record data to other kidney disease databases; and assess the lived experiences of patients related to BP management. Study Design: Multicenter retrospective cohort study (clinical outcomes) and cross-sectional study (patient-reported outcomes [PROs]). Setting & Participants: PRESERVE will include approximately 20,000 children between January 2009-December 2022 with mild-moderate CKD from 15 health care institutions that participate in 6 PCORnet Clinical Research Networks (PEDSnet, STAR, GPC, PaTH, CAPRiCORN, and OneFlorida+). The inclusion criteria were ≥1 nephrologist visit and ≥2 estimated glomerular filtration rate (eGFR) values in the range of 30 to 19,000 children with CKD. PRESERVE will (1) expand the PCORnet infrastructure for research in pediatric CKD by adding kidney-specific variables and linking electronic health record data to other kidney disease databases; (2) compare the effectiveness of alternative strategies for monitoring and treating hypertension on preserving kidney function; and (3) assess the lived experiences of patients and caregivers related to blood pressure management.

Abstract Background The effectiveness of biologic therapies, primarily tumor necrosis factor inhibitors (TNFi), for children with spondyloarthritis (SpA) has made inactive disease a realistic patient outcome. However, biologic therapies are costly, primarily delivered by subcutaneous or intravenous route, and have non-trivial side effects. Many patients and families want to know if biologic medications can be discontinued after inactive disease is achieved. It remains unclear whether medication dose should remain unchanged, tapered (increase the time between doses), or discontinued once when inactive disease is attained. Methods The Biologic Abatement and Capturing Kids’ Outcomes and Flare Frequency in Juvenile SpA (BACK-OFF JSpA) trial is a multicenter pragmatic trial that will randomize 198 participants ages 8–21 years old with SpA and sustained inactive disease on standard TNFi dosing to (1) continue standard TNFi dosing, (2) fixed longer dosing intervals of TNFi, or (3) stop TNFi. The trial will compare the hazard rate of protocol-defined flare and participants’ emotional health among the 3 groups over 12 months. Innovative aspects of this trial are the involvement of patient and parent stakeholders in the design and conduct of the study as well as an electronic health record-based enhanced recruitment strategy. Discussion This is the first randomized pragmatic trial to assess the efficacy of TNFi de-escalation strategies in children with JSpA with sustained inactive disease. This research will improve the evidence base that patients, caregivers, and rheumatologists use to make shared decisions about continued treatment versus de-escalation of TNFi therapy in this population. Trial registration ClinicalTrials.gov NCT04891640. Registered on 18 May 2021.

Abstract Background The Biologic Abatement and Capturing Kids’ Outcomes and Flare Frequency in Juvenile Spondyloarthritis (BACK‐OFF JSpA) study is a randomized, pragmatic trial investigating different tumour necrosis factor inhibitor de‐escalation strategies for children with sustained inactive disease. In this project, we elicited concept rankings that aided in the selection of the patient‐reported outcome (PRO) measures that should be examined as part of the BACK‐OFF JSpA trial. Methods We conducted a discrete choice experiment to evaluate individuals' preferences regarding PROs. Stakeholders assessed a discrete list of 21 outcome concepts, each of which had a Patient‐Reported Outcome Measurement Information System (PROMIS) measure associated with it. PROMIS measures are self‐ or proxy‐reported instruments that are universally applicable to the general population and all chronic conditions. Stakeholders were required to make choices instead of expressing the strength of a preference. Results Fourteen caregivers, 12 patients (9–22 years old), 16 rheumatologists and three executives from health insurance companies completed the exercise, which took approximately 10 min. The discrete choice experiment resulted in an estimate of the relative importance of each outcome and rank. All stakeholder groups agreed that the primary PRO should be ‘Pain Interference’, a measure that evaluates the effect of pain on a child's everyday activities, including its impact on social, emotional, mental and physical functioning. Patients and caregivers were mostly aligned in their top priorities, with patients valuing physical health (50% of the top 10) whereas caregivers were more interested in mental health (60% of the top 10). Rheumatologists and health insurance executives were most interested in physical health outcomes, which were ranked 80% and 60% of their top 10 PROs, respectively. Overall, the patients had the most diverse set of prioritized outcomes, including at least one of each category in their top 10 rank order of importance. Patients were also the only stakeholders to prioritize ‘social’ health. Conclusions Patients and caregivers were mostly aligned in their outcome priority rankings. The rank‐order list directly informed the creation of a profile of PRO measures for our upcoming trial. Patient or Public Contribution Stakeholder partners helped with acquisition of data and lead parent partners helped interpret data.

Abstract Many factors influence the health and well-being of children and the adults they will become. Yet there are significant gaps in how trajectories of healthy development are measured, how the potential for leading a healthy life is evaluated, and how that information can guide upstream policies and investments. The Gross Developmental Potential (GDP2) is proposed as a new capabilities-based framework for assessing threats to thriving and understanding progress in achieving lifelong health and wellbeing. Moving beyond the Gross Domestic Product’s (GDP) focus on economic productivity as a measure of progress, the GDP2 focuses on seven essential developmental capabilities for lifelong health and wellbeing. The GDP2 capability domains include Health -living a healthy life; Needs-satisfying basic human requirements; Communication-expressing and understanding thoughts and feelings; Learning-lifelong learning; Adaption -adapting to change; Connections -connecting with others; and Community -engaging in the community. The project team utilized literature reviews and meetings with the subject and technical experts to develop the framework. The framework was then vetted in focus groups of community leaders from three diverse settings. The community leaders' input refined the domains and their applications. This prototype GDP2 framework will next be used to develop specific measures and indices and guide the development of community-level GDP2 dashboards for local sense-making, learning, and application.

Abstract Background Clinical quality registries (CQRs) monitor compliance against optimal practice and provide feedback to the clinical community and wider stakeholder groups. Despite a number of CQRs having incorporated the patient perspective to support the evaluation of healthcare delivery, no recommendations for inclusion of patient-reported outcome measures (PROMs) in CQRs exist. The aim of this study was to develop a core set of recommendations for PROMs inclusion of in CQRs. Method An online two-round Delphi survey was performed among CQR data custodians, quality of life researchers, biostatisticians and clinicians largely recruited in Australia. A list of statements for the recommendations was identified from a literature and survey of the Australian registries conducted in 2019. The statements were grouped into the following domains: rationale, setting, ethics, instrument, administration, data management, statistical methods, and feedback and reporting. Eighteen experts were invited to participate, 11 agreed to undertake the first online survey (round 1). Of these, nine experts completed the online survey for round 2. Results From 117 statements presented to the Delphi panel in round 1, a total of 72 recommendations (55 from round 1 and 17 from round 2) with median importance (MI) ≥ 7 and disagreement index (DI)

Abstract Background The evidence based on the inclusion of patients and other stakeholders as partners in the clinical research process has grown substantially. However, little has been reported on how stakeholders are engaged in the governance of large‐scale clinical research networks and the infrastructure used by research networks to support engagement in network‐affiliated activities. Objectives The objective was to document engagement activities and practices emerging from Clinical Research Networks (CRNs) participating in PCORnet, the National Patient‐Centered Clinical Research Network, specifically regarding governance and engagement infrastructure. Methods We conducted an environmental scan of PCORnet CRN engagement structures, assets, and services, focusing on network oversight structures for policy development and strategic decision‐making. The scan included assets and services for supporting patient/stakeholder engagement. Data were collected by searching web‐based literature and tool repositories, review of CRN Engagement Plans, analysis of previously collected key informant interviews, and CRN‐based iterative review of structured worksheets. Results We identified 87 discrete engagement structures, assets, and services across nine CRNs. All CRNs engage patients/stakeholders in their governance, maintain workgroups and/or staff dedicated to overseeing engagement strategies, and offer one or more services to non‐CRN researchers to enhance conducting engaged clinical research. Conclusions This work provides an important resource for the research community to explore engagement across peers, reflect on progress, consider opportunities to leverage existing infrastructure, and identify new collaborators. It also serves to highlight PCORnet as a resource for non‐CRN researchers seeking to efficiently conduct engaged clinical research and a venue for advancing the science of engagement.

Abstract Clinical research networks (CRNs), made up of multiple healthcare systems each with patient data from several care sites, are beneficial for studying rare outcomes and increasing generalizability of results. While CRNs encourage sharing aggregate data across healthcare systems, individual systems within CRNs often cannot share patient-level data due to privacy regulations, prohibiting multi-site regression which requires an analyst to access all individual patient data pooled together. Meta-analysis is commonly used to model data stored at multiple institutions within a CRN but can result in biased estimation, most notably in rare-event contexts. We present a communication-efficient, privacy-preserving algorithm for modeling multi-site zero-inflated count outcomes within a CRN. Our method, a one-shot distributed algorithm for performing hurdle regression (ODAH), models zero-inflated count data stored in multiple sites without sharing patient-level data across sites, resulting in estimates closely approximating those that would be obtained in a pooled patient-level data analysis. We evaluate our method through extensive simulations and two real-world data applications using electronic health records: examining risk factors associated with pediatric avoidable hospitalization and modeling serious adverse event frequency associated with a colorectal cancer therapy. In simulations, ODAH produced bias less than 0.1% across all settings explored while meta-analysis estimates exhibited bias up to 12.7%, with meta-analysis performing worst in settings with high zero-inflation or low event rates. Across both applied analyses, ODAH estimates had less than 10% bias for 18 of 20 coefficients estimated, while meta-analysis estimates exhibited substantially higher bias. Relative to existing methods for distributed data analysis, ODAH offers a highly accurate, computationally efficient method for modeling multi-site zero-inflated count data.

Abstract Background and purpose Continuous improvement of health and healthcare system is hampered by inefficient processes of generating new evidence, particularly in the case of rare diseases and paediatrics. Currently, most evidence is generated through specific research projects, which typically require extra encounters with patients, are costly and entail long delays between the recognition of specific needs in healthcare and the generation of necessary evidence to address those needs. The Swiss Personalised Health Network (SPHN) aims to improve the use of data obtained during routine healthcare encounters by harmonizing data across Switzerland and facilitating accessibility for research. The project “Harmonising the collection of health-related data and biospecimens in paediatric hospitals throughout Switzerland (SwissPedData)” was an infrastructure development project funded by the SPHN, which aimed to identify and describe available data on child health in Switzerland and to agree on a standardised core dataset for electronic health records across all paediatric teaching hospitals. Here, we describe the results of a two-day symposium that aimed to summarise what had been achieved in the SwissPedData project, to put it in an international context, and to discuss the next steps for a sustainable future. The target audience included clinicians and researchers who produce and use health-related data on children in Switzerland. Key highlights The symposium consisted of state-of-the-art lectures from national and international keynote speakers, workshops and plenary discussions. This manuscript summarises the talks and discussions in four sections: (I) a description of the Swiss Personalized Health Network and the results of the SwissPedData project; (II) examples of similar initiatives from other countries; (III) an overview of existing health-related datasets and projects in Switzerland; and (IV) a summary of the lessons learned and future prospective from workshops and plenary discussions. Implications Streamlined processes linking initial collection of information during routine healthcare encounters, standardised recording of this information in electronic health records and fast accessibility for research are essential to accelerate research in child health and make it affordable. Ongoing projects prove that this is feasible in Switzerland and elsewhere. International collaboration is vital to success. The next steps include the implementation of the SwissPedData core dataset in the clinical information systems of Swiss hospitals, the use of this data to address priority research questions, and the acquisition of sustainable funding to support a slim central infrastructure and local support in each hospital. This will lay the foundation for a national paediatric learning health system in Switzerland.

Abstract Introduction The learning health system (LHS) aligns science, informatics, incentives, stakeholders, and culture for continuous improvement and innovation. The Agency for Healthcare Research and Quality and the Patient‐Centered Outcomes Research Institute designed a K12 initiative to grow the number of LHS scientists. We describe approaches developed by 11 funded centers of excellence (COEs) to promote partnerships between scholars and health system leaders and to provide mentored research training. Methods Since 2018, the COEs have enlisted faculty, secured institutional resources, partnered with health systems, developed and implemented curricula, recruited scholars, and provided mentored training. Program directors for each COE provided descriptive data on program context, scholar characteristics, stakeholder engagement, scholar experiences with health system partnerships, roles following program completion, and key training challenges. Results To date, the 11 COEs have partnered with health systems to train 110 scholars. Nine (82%) programs partner with a Veterans Affairs health system and 9 (82%) partner with safety net providers. Clinically trained scholars (n = 87; 79%) include 70 physicians and 17 scholars in other clinical disciplines. Non‐clinicians (n = 29; 26%) represent diverse fields, dominated by population health sciences. Stakeholder engagement helps scholars understand health system and patient/family needs and priorities, enabling opportunities to conduct embedded research, improve outcomes, and grow skills in translating research methods and findings into practice. Challenges include supporting scholars through roadblocks that threaten to derail projects during their limited program time, ranging from delays in access to data to COVID‐19‐related impediments and shifts in organizational priorities. Conclusions Four years into this novel training program, there is evidence of scholars' accomplishments, both in traditional academic terms and in terms of moving along career trajectories that hold the potential to lead and accelerate transformational health system change. Future LHS training efforts should focus on sustainability, including organizational support for scholar activities.

In this work, we present a study of electronic health record (EHR) data that aims to identify pediatric obesity clinical subtypes. Specifically, we examine whether certain temporal condition patterns associated with childhood obesity incidence tend to cluster together to characterize subtypes of clinically similar patients. In a previous study, the sequence mining algorithm, SPADE was implemented on EHR data from a large retrospective cohort (n = 49 594 patients) to identify common condition trajectories surrounding pediatric obesity incidence. In this study, we used Latent Class Analysis (LCA) to identify potential subtypes formed by these temporal condition patterns. The demographic characteristics of patients in each subtype are also examined. An LCA model with 8 classes was developed that identified clinically similar patient subtypes. Patients in Class 1 had a high prevalence of respiratory and sleep disorders, patients in Class 2 had high rates of inflammatory skin conditions, patients in Class 3 had a high prevalence of seizure disorders, and patients in Class 4 had a high prevalence of Asthma. Patients in Class 5 lacked a clear characteristic morbidity pattern, and patients in Classes 6, 7, and 8 had a high prevalence of gastrointestinal issues, neurodevelopmental disorders, and physical symptoms respectively. Subjects generally had high membership probability for a single class (>70%), suggesting shared clinical characterization within the individual groups. We identified patient subtypes with temporal condition patterns that are significantly more common among obese pediatric patients using a Latent Class Analysis approach. Our findings may be used to characterize the prevalence of common conditions among newly obese pediatric patients and to identify pediatric obesity subtypes. The identified subtypes align with prior knowledge on comorbidities associated with childhood obesity, including gastro-intestinal, dermatologic, developmental, and sleep disorders, as well as asthma. Author summary Childhood obesity is a significant public health challenge in the United States. Despite its prevalence, it remains uncertain if pediatric obesity represents a single condition or is composed of different subtypes with possibly different underlying causes. Electronic Health Records (EHRs) are an important source of data that may be analyzed to yield clinical and epidemiological insights to aid in the obesity treatment and prevention. In this paper, we present a study of EHR data that aimed to identify clinically similar subtypes among a population of newly obese pediatric patients. Specifically, we examine whether certain temporal condition patterns associated with childhood obesity incidence tend to cluster together to characterize subgroups of clinically similar patients. We identified eight potential subtypes, differentiated by the prevalence of various diagnoses including respiratory and sleep disorders, inflammatory skin conditions, asthma, and seizure disorders. This work may be used as a foundation for future investigations into pediatric obesity subtypes as well as to inform methodological and clinical research to mine EHR data for potential insights that improve patient health outcomes.

IntroductionQualitative research reveals significant caregiver impact resulting from managing children requiring chronic dialysis but offers few quantitative measures of their lived experiences.Materials and MethodsThis cross-sectional study included 25 caregivers of children on chronic peritoneal dialysis (PD) and hemodialysis (HD) enrolled from 2018 to 2019 at a large pediatric dialysis program in the U.S.Patient Reported Outcomes Measures Information System (PROMIS) measures and free text commentary were collected and analyzed to evaluate the self-reported impact and wellbeing of these caregivers.ResultsAmong all dialysis modalities, caregivers' positive affect (43.4 ± 10) and general life satisfaction (45.1 ± 11.5) were significantly lower than the general adult population. Compared with HD caregivers, PD caregivers demonstrated significantly more fatigue and sleep disturbance and less positive affect and life satisfaction. Amongst HD caregivers, sleep disturbance, positive affect, and meaning/purpose differed significantly from the general population. Analyses of text commentary revealed that caregivers also expressed the feelings of loss, importance of knowing the impact of dialysis prior to initiation, need for a support group, and value of home nursing.ConclusionsCaregivers of children on chronic dialysis had significantly poorer self-rated health and wellbeing compared with the general adult population. This may be due in part to their feelings of social isolation. Our findings highlight opportunities to improve caregivers' lived experiences.

Summary: Background: The extent to which healthcare worker (HCWs) experiences during the COVID-19 pandemic vary by race or ethnicity after adjustment for confounding factors is not currently known. Methods: We performed an observational prospective cohort study of 24,769 healthcare workers from 50 U.S. states and the District of Columbia, enrolled between April 10, 2020 and June 30, 2021, and evaluated participant experiences during the COVID-19 pandemic, including testing, diagnosis with COVID-19, emotional experiences, burnout, and interest in vaccines and vaccine clinical trials. Findings: After adjustment for professional role, medical history, and community characteristics, Black and Asian participants were less likely to receive SARS-CoV-2 viral testing (adjusted odds ratio (aOR) 0·82 [0·70, 0·96], p=0·012 and aOR 0·77 [0·67, 0·89], p

Abstract Background PROMIS Pediatric patient-reported outcome measures were developed with children from the general population, and their content validity has not been established in children with chronic disease. This study was done to evaluate the content validity of the PROMIS Pediatric Pain Interference and Fatigue measures in children 8–17 years-old with Crohn’s disease and the PROMIS Pediatric Fatigue, Sleep Disturbance, and Sleep-related Impairment measures for children 8–17 years-old with chronic kidney disease. Methods We conducted semi-structured interviews with individuals affected by Crohn’s disease and chronic kidney disease. The interviews were done to elicit children’s lived experiences of the PROMIS outcomes of interest. We used deductive content analysis to contrast the participants’ reports of their symptoms and impacts on daily life with existing conceptual frameworks for the PROMIS measures, each of which was developed with input from children in the general population. Results On average, we elicited an average of 7 pain interference and 7 fatigue concepts from Crohn’s disease participants (n = 37), while chronic kidney disease participants (n = 26) provided 9 concepts for fatigue, 4 for sleep disturbance, and 7 for sleep-related impairment. Concept saturation was achieved after 16–19 interviews across the four PROMIS measures. Children with these two chronic health conditions reported the same breadth and types of lived experiences as children from the development samples drawn from the general population. Conclusion The study supports the content validity of several PROMIS Pediatric measures for children with Crohn’s disease and chronic kidney disease. These findings provide evidence that PROMIS Pediatric measures, developed as universally relevant patient-reported outcomes, may be more broadly applicable to children with chronic disease.

Abstract Background Pediatric primary care visits are a foundational element in the health maintenance of children. Differential access may be a driver of racial inequities in health. We hypothesized that pediatric primary care accessibility would be lowest in neighborhoods with higher proportion of non-Hispanic Black residents. Methods Annual ratios (2008–2016) of providers to pediatric population were calculated by census tract in Philadelphia, Pennsylvania. Marginal logistic regression was used to estimate the independent association between neighborhood racial composition and access to pediatric primary care controlling for confounders. Results In general, low access to care was associated with greater neighborhood disadvantage (e.g., SES, % poverty, % public insurance). After controlling for neighborhood indicators of disadvantage, risk of being in the lowest quintile of access significantly increased as the percent of non-Hispanic Black residents increased. Conclusion A new measure of pediatric primary care accessibility demonstrates a persistent disparity in primary care access for predominantly non-Hispanic Black neighborhoods.

Abstract Background The vision of learning healthcare systems (LHSs) is attractive as a more effective model for health care services, but achieving the vision is complex. There is limited literature describing the processes needed to construct such multicomponent systems or to assess development. Methods We used the concept of a capability maturity matrix to describe the maturation of necessary infrastructure and processes to create learning networks (LNs), multisite collaborative LHSs that use an actor‐oriented network organizational architecture. We developed a network maturity grid (NMG) assessment tool by incorporating information from literature review, content theory from existing networks, and expert opinion to establish domains and components. We refined the maturity grid in response to feedback from network leadership teams. We followed NMG scores over time for nine LNs and plotted scores for each domain component with respect to SD for one participating network. We sought subjective feedback on the experience of applying the NMG to individual networks. Results LN leaders evaluated the scope, depth, and applicability of the NMG to their networks. Qualitative feedback from network leaders indicated that changes in NMG scores over time aligned with leaders' reports about growth in specific domains; changes in scores were consistent with network efforts to improve in various areas. Scores over time showed differences in maturation in the individual domains of each network. Scoring patterns, and SD for domain component scores, indicated consistency among LN leaders in some but not all aspects of network maturity. A case example from a participating network highlighted the value of the NMG in prompting strategic discussions about network development and demonstrated that the process of using the tool was itself valuable. Conclusions The capability maturity grid proposed here provides a framework to help those interested in creating Learning Health Networks plan and develop them over time.

Pollen from trees, grasses, and weeds can trigger asthma exacerbation in sensitized individuals. However, there are gaps in knowledge about the effects, such as the relative risks from different plant taxa and threshold levels of effect. We aimed to describe the local association between pollen and asthma exacerbation among children in the City of Philadelphia, and to evaluate whether effects are modified by children’s characteristics and clinical factors (e.g., child’s age, race/ethnicity, comorbidities). We conducted a time-stratified case-crossover study of pediatric (age

Abstract Objectives To develop and evaluate the classification accuracy of a computable phenotype for pediatric Crohn's disease using electronic health record data from PEDSnet, a large, multi‐institutional research network and Learning Health System. Study Design Using clinician and informatician input, algorithms were developed using combinations of diagnostic and medication data drawn from the PEDSnet clinical dataset which is comprised of 5.6 million children from eight U.S. academic children's health systems. Six test algorithms (four cases, two non‐cases) that combined use of specific medications for Crohn's disease plus the presence of Crohn's diagnosis were initially tested against the entire PEDSnet dataset. From these, three were selected for performance assessment using manual chart review (primary case algorithm, n = 360, primary non‐case algorithm, n = 360, and alternative case algorithm, n = 80). Non‐cases were patients having gastrointestinal diagnoses other than inflammatory bowel disease. Sensitivity, specificity, and positive predictive value (PPV) were assessed for the primary case and primary non‐case algorithms. Results Of the six algorithms tested, the least restrictive algorithm requiring just ≥1 Crohn's diagnosis code yielded 11 950 cases across PEDSnet (prevalence 21/10 000). The most restrictive algorithm requiring ≥3 Crohn's disease diagnoses plus at least one medication yielded 7868 patients (prevalence 14/10 000). The most restrictive algorithm had the highest PPV (95%) and high sensitivity (91%) and specificity (94%). False positives were due primarily to a diagnosis reversal (from Crohn's disease to ulcerative colitis) or having a diagnosis of “indeterminate colitis.” False negatives were rare. Conclusions Using diagnosis codes and medications available from PEDSnet, we developed a computable phenotype for pediatric Crohn's disease that had high specificity, sensitivity and predictive value. This process will be of use for developing computable phenotypes for other pediatric diseases, to facilitate cohort identification for retrospective and prospective studies, and to optimize clinical care through the PEDSnet Learning Health System.

Abstract Background There is a widely recognized need for more pragmatic trials that evaluate interventions in real-world settings to inform decision-making by patients, providers, and health system leaders. Increasing availability of electronic health records, centralized research ethics review, and novel trial designs, combined with support and resources from governments worldwide for patient-centered research, have created an unprecedented opportunity to advance the conduct of pragmatic trials, which can ultimately improve patient health and health system outcomes. Such trials raise ethical issues that have not yet been fully addressed, with existing literature concentrating on regulations in specific jurisdictions rather than arguments grounded in ethical principles. Proposed solutions (e.g. using different regulations in “learning healthcare systems”) are speculative with no guarantee of improvement over existing oversight procedures. Most importantly, the literature does not reflect a broad vision of protecting the core liberty and welfare interests of research participants. Novel ethical guidance is required. We have assembled a team of ethicists, trialists, methodologists, social scientists, knowledge users, and community members with the goal of developing guidance for the ethical design and conduct of pragmatic trials. Methods Our project will combine empirical and conceptual work and a consensus development process. Empirical work will: (1) identify a comprehensive list of ethical issues through interviews with a small group of key informants (e.g. trialists, ethicists, chairs of research ethics committees); (2) document current practices by reviewing a random sample of pragmatic trials and surveying authors; (3) elicit views of chairs of research ethics committees through surveys in Canada, UK, USA, France, and Australia; and (4) elicit views and experiences of community members and health system leaders through focus groups and surveys. Conceptual work will consist of an ethical analysis of identified issues and the development of new ethical solutions, outlining principles, policy options, and rationales. The consensus development process will involve an independent expert panel to develop a final guidance document. Discussion Planned output includes manuscripts, educational materials, and tailored guidance documents to inform and support researchers, research ethics committees, journal editors, regulators, and funders in the ethical design and conduct of pragmatic trials.