Using the Electronic Health Record to Enhance Care in Pediatric Rheumatology.

The electronic health record (EHR) ecosystem is undergoing rapid evolution in response to new rules and regulations promulgated by the US HITECH Act (2009) and the 21st Century Cures Act (2016), which together promote and support enhanced information use, access, exchange, as well as vendor-agnostic application development. By leveraging emerging new standards and technology for EHR data interchange, for example, FHIR and SMART, pediatric rheumatology clinical care, research, and quality improvement communities will have the opportunity to streamline documentation workflows, integrate patient-reported outcomes into clinical care, reuse clinical data for research purposes, and embed implementation science approaches within the EHR.

Improved Disease Course Associated With Early Initiation of Biologics in Polyarticular Juvenile Idiopathic Arthritis: Trajectory Analysis of a Childhood Arthritis and Rheumatology Research Alliance Consensus Treatment Plans Study.

OBJECTIVE: To investigate the effects of early introduction of biologic disease-modifying antirheumatic drugs (bDMARDs) on the disease course in untreated polyarticular juvenile idiopathic arthritis (JIA). METHODS: We analyzed data on patients with polyarticular JIA participating in the Start Time Optimization of Biologics in Polyarticular JIA (STOP-JIA) study (n = 400) and a comparator cohort (n = 248) from the Childhood Arthritis and Rheumatology Research Alliance Registry. Latent class trajectory modeling (LCTM) was applied to identify subgroups of patients with distinct disease courses based on disease activity (clinical Juvenile Arthritis Disease Activity Score in 10 joints) over 12 months from baseline. RESULTS: In the STOP-JIA study, 198 subjects (49.5%) received bDMARDs within 3 months of baseline assessment. LCTM analyses generated 3 latent classes representing 3 distinct disease trajectories, characterized by slow, moderate, or rapid disease activity improvement over time. Subjects in the rapid improvement trajectory attained inactive disease within 6 months from baseline. Odds of being in the rapid improvement trajectory versus the slow improvement trajectory were 3.6 times as high (95% confidence interval 1.32-10.0; P = 0.013) for those treated with bDMARDs ≤3 months from baseline compared with subjects who started bDMARDs >3 months after baseline, after adjusting for demographic characteristics, clinical attributes, and baseline disease activity. Shorter disease duration at first rheumatology visit approached statistical significance as a predictor of favorable trajectory without bDMARD treatment. CONCLUSION: Starting bDMARDs within 3 months of baseline assessment is associated with more rapid achievement of inactive disease in subjects with untreated polyarticular JIA. These results demonstrate the utility of trajectory analysis of disease course as a method for determining treatment efficacy.

Optimizing the Start Time of Biologics in Polyarticular Juvenile Idiopathic Arthritis: A Comparative Effectiveness Study of Childhood Arthritis and Rheumatology Research Alliance Consensus Treatment Plans.

OBJECTIVE: The optimal time to start biologics in polyarticular juvenile idiopathic arthritis (JIA) remains uncertain. The Childhood Arthritis and Rheumatology Research Alliance (CARRA) developed 3 consensus treatment plans (CTPs) for untreated polyarticular JIA to compare strategies for starting biologics. METHODS: Start Time Optimization of Biologics in Polyarticular JIA (STOP-JIA) was a prospective, observational, CARRA Registry study comparing the effectiveness of 3 CTPs: 1) the step-up plan (initial nonbiologic disease-modifying antirheumatic drug [DMARD] monotherapy, adding a biologic if needed, 2) the early combination plan (DMARD and biologic started together), and 3) the biologic first plan (biologic monotherapy). The primary outcome measure was clinically inactive disease according to the provisional American College of Rheumatology (ACR) criteria, without glucocorticoids, at 12 months. Secondary outcome measures included Patient-Reported Outcomes Measurement Information System (PROMIS) pain interference and mobility scores, inactive disease as defined by the clinical Juvenile Arthritis Disease Activity Score in 10 joints (JADAS-10), and the ACR Pediatric 70 criteria (Pedi 70). RESULTS: Of 400 patients enrolled, 257 (64%) began the step-up plan, 100 (25%) the early combination plan, and 43 (11%) the biologic first plan. After propensity score weighting and multiple imputation, clinically inactive disease according to the ACR criteria was achieved in 37% of those on the early combination plan, 32% on the step-up plan, and 24% on the biologic first plan (P = 0.17). Inactive disease according to the clinical JADAS-10 (score ≤2.5) was also achieved in more patients on the early combination plan than the step-up plan (59% versus 43%; P = 0.03), as was ACR Pedi 70 (81% versus 62%; P = 0.008), but generalizability was limited by missing data. PROMIS measures improved in all groups, but without significant differences. Twenty serious adverse events were reported (mostly infections). CONCLUSION: Achievement of clinically inactive disease without glucocorticoids did not significantly differ between groups at 12 months. While there was a significantly higher likelihood of early combination therapy achieving inactive disease according to the clinical JADAS-10 and ACR Pedi 70, these results require further exploration.

Claims-Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine-Learning Approaches.

Background Real-world healthcare data are an important resource for epidemiologic research. However, accurate identification of patient cohorts-a crucial first step underpinning the validity of research results-remains a challenge. We developed and evaluated claims-based case ascertainment algorithms for pulmonary hypertension (PH), comparing conventional decision rules with state-of-the-art machine-learning approaches. Methods and Results We analyzed an electronic health record-Medicare linked database from two large academic tertiary care hospitals (years 2007-2013). Electronic health record charts were reviewed to form a gold standard cohort of patients with (n=386) and without PH (n=164). Using health encounter data captured in Medicare claims (including patients' demographics, diagnoses, medications, and procedures), we developed and compared 2 approaches for identifying patients with PH: decision rules and machine-learning algorithms using penalized lasso regression, random forest, and gradient boosting machine. The most optimal rule-based algorithm-having ≥3 PH-related healthcare encounters and having undergone right heart catheterization-attained an area under the receiver operating characteristic curve of 0.64 (sensitivity, 0.75; specificity, 0.48). All 3 machine-learning algorithms outperformed the most optimal rule-based algorithm (P<0.001). A model derived from the random forest algorithm achieved an area under the receiver operating characteristic curve of 0.88 (sensitivity, 0.87; specificity, 0.70), and gradient boosting machine achieved comparable results (area under the receiver operating characteristic curve, 0.85; sensitivity, 0.87; specificity, 0.70). Penalized lasso regression achieved an area under the receiver operating characteristic curve of 0.73 (sensitivity, 0.70; specificity, 0.68). Conclusions Research-grade case identification algorithms for PH can be derived and rigorously validated using machine-learning algorithms. Simple decision rules commonly applied in published literature performed poorly; more complex rule-based algorithms may potentially address the limitation of this approach. PH research using claims data would be considerably strengthened through the use of validated algorithms for cohort ascertainment.

Racial and Ethnic Differences in Pediatric Pulmonary Hypertension: An Analysis of the Pediatric Pulmonary Hypertension Network Registry.

OBJECTIVE: To investigate racial and ethnic differences in pulmonary hypertension subtypes and survival differences in a pediatric population. STUDY DESIGN: This was a retrospective analysis of a cohort of patients with pulmonary hypertension (aged ≤18 years) enrolled in the Pediatric Pulmonary Hypertension Network registry between 2014 and 2018, comprising patients at eight Pediatric Centers throughout North America (n = 1417). RESULTS: Among children diagnosed after the neonatal period, pulmonary arterial hypertension was more prevalent among Asians (OR, 1.83; 95% CI, 1.21-2.79; P = .0045), lung disease-associated pulmonary hypertension among blacks (OR, 2.09; 95% CI, 1.48-2.95; P < .0001), idiopathic pulmonary arterial hypertension among whites (OR, 1.58; 95% CI, 1.06-2.41; P = .0289), and pulmonary veno-occlusive disease among Hispanics (OR, 6.11; 95% CI, 1.34-31.3; P = .0184). Among neonates, persistent pulmonary hypertension of the newborn (OR, 4.07; 95% CI, 1.54-10.0; P = .0029) and bronchopulmonary dysplasia (OR, 8.11; 95% CI, 3.28-19.8; P < .0001) were more prevalent among blacks, and congenital diaphragmatic hernia was more prevalent among whites (OR, 2.29; 95% CI, 1.25-4.18; P = .0070). An increased mortality risk was observed among blacks (HR, 1.99; 95% CI, 1.03-3.84; P = .0396), driven primarily by the heightened mortality risk among those with lung disease-associated pulmonary hypertension (HR, 2.84; 95% CI, 1.15-7.04; P = .0241). CONCLUSIONS: We found significant racial variability in the prevalence of pulmonary hypertension subtypes and survival outcomes among children with pulmonary hypertension. Given the substantial burden of this disease, further studies to validate phenotypic differences and to understand the underlying causes of survival disparities between racial and ethnic groups are warranted.

The Association of Black Cardiologists (ABC) Cardiovascular Implementation Study (CVIS): A Research Registry Integrating Social Determinants to Support Care for Underserved Patients.

African Americans, other minorities and underserved populations are consistently under- represented in clinical trials. Such underrepresentation results in a gap in the evidence base, and health disparities. The ABC Cardiovascular Implementation Study (CVIS) is a comprehensive prospective cohort registry that integrates social determinants of health. ABC CVIS uses real world clinical practice data to address critical gaps in care by facilitating robust participation of African Americans and other minorities in clinical trials. ABC CVIS will include diverse patients from collaborating ABC member private practices, as well as patients from academic health centers and Federally Qualified Health Centers (FQHCs). This paper describes the rationale and design of the ABC CVIS Registry. The registry will: (1) prospectively collect socio-demographic, clinical and biospecimen data from enrolled adults, adolescents and children with prioritized cardiovascular diseases; (2) Evaluate the safety and clinical outcomes of new therapeutic agents, including post marketing surveillance and pharmacovigilance; (3) Support National Institutes of Health (NIH) and industry sponsored research; (4) Support Quality Measures standards from the Center for Medicare and Medicaid Services (CMS) and Commercial Health Plans. The registry will utilize novel data and technology tools to facilitate mobile health technology application programming interface (API) to health system or practice electronic health records (EHR). Long term, CVIS will become the most comprehensive patient registry for underserved diverse patients with cardiovascular disease (CVD) and co morbid conditions, providing real world data to address health disparities. At least 10,000 patients will be enrolled from 50 sites across the United States.

A federated EHR network data completeness tracking system.

OBJECTIVE: The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network. MATERIALS AND METHODS: The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source tools (DQe-c and Vue) to integrate technology and human actors in a system geared for increasing capacity and taking action. A pilot implementation of the system involved 6 ARCH partner sites between January 2017 and May 2018. RESULTS: The ARCH CTX has enabled the network to monitor and, if needed, adjust its data management processes to maintain complete datasets for secondary use. The system allows the network and its partner sites to profile data completeness both at the network and partner site levels. Interactive visualizations presenting the current state of completeness in the context of the entire network as well as changes in completeness across time were valued among the CTX user base. DISCUSSION: Distributed clinical data networks are complex systems. Top-down approaches that solely rely on technology to report data completeness may be necessary but not sufficient for improving completeness (and quality) of data in large-scale clinical data networks. Improving and maintaining complete (high-quality) data in such complex environments entails sociotechnical systems that exploit technology and empower human actors to engage in the process of high-quality data curating. CONCLUSIONS: The CTX has increased the network's capacity to rapidly identify data completeness issues and empowered ARCH partner sites to get involved in improving the completeness of respective data in their repositories.

Learning a Comorbidity-Driven Taxonomy of Pediatric Pulmonary Hypertension.

RATIONALE: Pediatric pulmonary hypertension (PH) is a heterogeneous condition with varying natural history and therapeutic response. Precise classification of PH subtypes is, therefore, crucial for individualizing care. However, gaps remain in our understanding of the spectrum of PH in children. OBJECTIVE: We seek to study the manifestations of PH in children and to assess the feasibility of applying a network-based approach to discern disease subtypes from comorbidity data recorded in longitudinal data sets. METHODS AND RESULTS: A retrospective cohort study comprising 6 943 263 children (<18 years of age) enrolled in a commercial health insurance plan in the United States, between January 2010 and May 2013. A total of 1583 (0.02%) children met the criteria for PH. We identified comorbidities significantly associated with PH compared with the general population of children without PH. A Bayesian comorbidity network was constructed to model the interdependencies of these comorbidities, and network-clustering analysis was applied to derive disease subtypes comprising subgraphs of highly connected comorbid conditions. A total of 186 comorbidities were found to be significantly associated with PH. Network analysis of comorbidity patterns captured most of the major PH subtypes with known pathological basis defined by the World Health Organization and Panama classifications. The analysis further identified many subtypes documented in only a few case studies, including rare subtypes associated with several well-described genetic syndromes. CONCLUSIONS: Application of network science to model comorbidity patterns recorded in longitudinal data sets can facilitate the discovery of disease subtypes. Our analysis relearned established subtypes, thus validating the approach, and identified rare subtypes that are difficult to discern through clinical observations, providing impetus for deeper investigation of the disease subtypes that will enrich current disease classifications.

A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry.

OBJECTIVES: To compare registry and electronic health record (EHR) data mining approaches for cohort ascertainment in patients with pediatric pulmonary hypertension (PH) in an effort to overcome some of the limitations of registry enrollment alone in identifying patients with particular disease phenotypes. STUDY DESIGN: This study was a single-center retrospective analysis of EHR and registry data at Boston Children's Hospital. The local Informatics for Integrating Biology and the Bedside (i2b2) data warehouse was queried for billing codes, prescriptions, and narrative data related to pediatric PH. Computable phenotype algorithms were developed by fitting penalized logistic regression models to a physician-annotated training set. Algorithms were applied to a candidate patient cohort, and performance was evaluated using a separate set of 136 records and 179 registry patients. We compared clinical and demographic characteristics of patients identified by computable phenotype and the registry. RESULTS: The computable phenotype had an area under the receiver operating characteristics curve of 90% (95% CI, 85%-95%), a positive predictive value of 85% (95% CI, 77%-93%), and identified 413 patients (an additional 231%) with pediatric PH who were not enrolled in the registry. Patients identified by the computable phenotype were clinically distinct from registry patients, with a greater prevalence of diagnoses related to perinatal distress and left heart disease. CONCLUSIONS: Mining of EHRs using computable phenotypes identified a large cohort of patients not recruited using a classic registry. Fusion of EHR and registry data can improve cohort ascertainment for the study of rare diseases. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02249923.

The new Childhood Arthritis and Rheumatology Research Alliance (CARRA) registry: design, rationale, and characteristics of patients enrolled in the first 12 months.

BACKGROUND: Herein we describe the history, design, and rationale of the new Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry and present the characteristics of patients with juvenile idiopathic arthritis (JIA) enrolled in the first 12 months of operation. METHODS: The CARRA Registry began prospectively collecting data in the United States and Canada in July 2015 to evaluate the safety of therapeutic agents in persons with childhood-onset rheumatic disease, initially restricted to JIA. Secondary objectives include the evaluation of disease outcomes and their associations with medication use and other factors. Data are collected every 6 months and include clinical assessments, detailed medication use, patient-reported outcomes, and safety events. Follow-up is planned for at least 10 years for each participant and is facilitated by a telephone call center. RESULTS: As of July 2016, 1192 patients with JIA were enrolled in the CARRA Registry at 49 clinical sites. At enrollment, their median age was 12.4 years old and median disease duration was 2.6 years. Owing to preferential enrollment, patients with systemic JIA (13%) and with a polyarticular course (75%) were over-represented compared to patients in typical clinical practice. Approximately 49% were currently using biologic agents and ever use of oral glucocorticoids was common (47%). The CARRA Registry provides safety surveillance data to pharmaceutical companies to satisfy their regulatory requirements, and several independently-funded sub-studies that use the Registry infrastructure are underway. CONCLUSION: The new CARRA Registry successfully enrolled nearly 1200 participants with JIA in the first 12 months of its operation. Sustainable funding has been secured from multiple sources. The CARRA Registry may serve as a model for the study of other uncommon diseases.

C3-PRO: Connecting ResearchKit to the Health System Using i2b2 and FHIR.

A renewed interest by consumer information technology giants in the healthcare domain is focused on transforming smartphones into personal health data storage devices. With the introduction of the open source ResearchKit, Apple provides a framework for researchers to inform and consent research subjects, and to readily collect personal health data and patient reported outcomes (PRO) from distributed populations. However, being research backend agnostic, ResearchKit does not provide data transmission facilities, leaving research apps disconnected from the health system. Personal health data and PROs are of the most value when presented in context along with health system data. Our aim was to build a toolchain that allows easy and secure integration of personal health and PRO data into an open source platform widely adopted across 140 academic medical centers. We present C3-PRO: the Consent, Contact, and Community framework for Patient Reported Outcomes. This open source toolchain connects, in a standards-compliant fashion, any ResearchKit app to the widely-used clinical research infrastructure Informatics for Integrating Biology and the Bedside (i2b2). C3-PRO leverages the emerging health data standard Fast Healthcare Interoperability Resources (FHIR).

Supporting Multi-sourced Medication Information in i2b2.

Postmarketing drug surveillance is critical to assessing adverse events associated with medications, because prelaunch clinical trials frequently miss negative drug effects. The Informatics for Integrating Biology and the Bedside platform (i2b2) has been used effectively for this. However, previous work suffers from incomplete medical data present in electronic health record (EHR) systems. Here, we develop a system to integrate non-traditional data sources with EHR data: pharmacy dispensing information and patient-reported data. We implement and validate a toolset to gather medication data from a Pharmacy Benefit Manager network, import it into an i2b2 EHR repository using a standard data format, merge it with the EHR data, and present it to for annotation with results returned to i2b2. This toolkit is enabling studies on medication list data quality, adherence, and adverse event detection.

Next-generation registries: fusion of data for care, and research.

Disease-based registries are a critical tool for electronic data capture of high-quality, gold standard data for clinical research as well as for population management in clinical care. Yet, a legacy of significant operational costs, resource requirements, and poor data liquidity have limited their use. Research registries have engendered more than $3 Billion in HHS investment over the past 17 years. Health delivery systems and Accountable Care Organizations are investing heavily in registries to track care quality and follow-up of patient panels. Despite the investment, regulatory and financial models have often enforced a "single purpose" limitation on each registry, restricting the use of data to a pre-defined set of protocols. The need for cost effective, multi-sourced, and widely shareable registry data sets has never been greater, and requires next-generation platforms to robustly support multi-center studies, comparative effectiveness research, post-marketing surveillance and disease management. This panel explores diverse registry efforts, both academic and commercial, that have been implemented in leading-edge clinical, research, and hybrid use cases. Panelists present their experience in these areas as well as lessons learned, challenges addressed, and near innovations and advances.

An i2b2-based, generalizable, open source, self-scaling chronic disease registry.

OBJECTIVE: Registries are a well-established mechanism for obtaining high quality, disease-specific data, but are often highly project-specific in their design, implementation, and policies for data use. In contrast to the conventional model of centralized data contribution, warehousing, and control, we design a self-scaling registry technology for collaborative data sharing, based upon the widely adopted Integrating Biology & the Bedside (i2b2) data warehousing framework and the Shared Health Research Information Network (SHRINE) peer-to-peer networking software. MATERIALS AND METHODS: Focusing our design around creation of a scalable solution for collaboration within multi-site disease registries, we leverage the i2b2 and SHRINE open source software to create a modular, ontology-based, federated infrastructure that provides research investigators full ownership and access to their contributed data while supporting permissioned yet robust data sharing. We accomplish these objectives via web services supporting peer-group overlays, group-aware data aggregation, and administrative functions. RESULTS: The 56-site Childhood Arthritis & Rheumatology Research Alliance (CARRA) Registry and 3-site Harvard Inflammatory Bowel Diseases Longitudinal Data Repository now utilize i2b2 self-scaling registry technology (i2b2-SSR). This platform, extensible to federation of multiple projects within and between research networks, encompasses >6000 subjects at sites throughout the USA. DISCUSSION: We utilize the i2b2-SSR platform to minimize technical barriers to collaboration while enabling fine-grained control over data sharing. CONCLUSIONS: The implementation of i2b2-SSR for the multi-site, multi-stakeholder CARRA Registry has established a digital infrastructure for community-driven research data sharing in pediatric rheumatology in the USA. We envision i2b2-SSR as a scalable, reusable solution facilitating interdisciplinary research across diseases.