Assessing Pediatric Rheumatology Fellow Competence in the Milestone Era: Past, Present, and Future.

Starting in 2015, pediatric rheumatology fellowship training programs were required by the Accreditation Council for Graduate Medical Education to assess fellows' academic performance within 21 subcompetencies falling under six competency domains. Each subcompetency had four or five milestone levels describing developmental progression of knowledge and skill acquisition. Milestones were standardized across all pediatric subspecialties. As part of the Milestones 2.0 revision project, the Accreditation Council for Graduate Medical Education convened a workgroup in 2022 to write pediatric rheumatology-specific milestones. Using adult rheumatology's Milestones 2.0 as a starting point, the workgroup revised the patient care and medical knowledge subcompetencies and milestones to reflect requirements and nuances of pediatric rheumatology care. Milestones within four remaining competency domains (professionalism, interpersonal and communication skills, practice-based learning and improvement, and systems-based practice) were standardized across all pediatric subspecialties, and therefore not revised. The workgroup created a supplemental guide with explanations of the intent of each subcompetency, 25 in total, and examples for each milestone level. The new milestones are an important step forward for competency-based medical education in pediatric rheumatology. However, challenges remain. Milestone level assignment is meant to be informed by results of multiple assessment methods. The lack of pediatric rheumatology-specific assessment tools typically result in clinical competency committees determining trainee milestone levels without such collated results as the foundation of their assessments. Although further advances in pediatric rheumatology fellowship competency-based medical education are needed, Milestones 2.0 importantly establishes the first pediatric-specific rheumatology Milestones to assess fellow performance during training and help measure readiness for independent practice.

Using a Multi-Institutional Pediatric Learning Health System to Identify Systemic Lupus Erythematosus and Lupus Nephritis: Development and Validation of Computable Phenotypes.

BACKGROUND AND OBJECTIVES: Performing adequately powered clinical trials in pediatric diseases, such as SLE, is challenging. Improved recruitment strategies are needed for identifying patients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Electronic health record algorithms were developed and tested to identify children with SLE both with and without lupus nephritis. We used single-center electronic health record data to develop computable phenotypes composed of diagnosis, medication, procedure, and utilization codes. These were evaluated iteratively against a manually assembled database of patients with SLE. The highest-performing phenotypes were then evaluated across institutions in PEDSnet, a national health care systems network of >6.7 million children. Reviewers blinded to case status used standardized forms to review random samples of cases (n=350) and noncases (n=350). RESULTS: Final algorithms consisted of both utilization and diagnostic criteria. For both, utilization criteria included two or more in-person visits with nephrology or rheumatology and ≥60 days follow-up. SLE diagnostic criteria included absence of neonatal lupus, one or more hydroxychloroquine exposures, and either three or more qualifying diagnosis codes separated by ≥30 days or one or more diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 100% (95% confidence interval [95% CI], 99 to 100), specificity was 92% (95% CI, 88 to 94), positive predictive value was 91% (95% CI, 87 to 94), and negative predictive value was 100% (95% CI, 99 to 100). Lupus nephritis diagnostic criteria included either three or more qualifying lupus nephritis diagnosis codes (or SLE codes on the same day as glomerular/kidney codes) separated by ≥30 days or one or more SLE diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 90% (95% CI, 85 to 94), specificity was 93% (95% CI, 89 to 97), positive predictive value was 94% (95% CI, 89 to 97), and negative predictive value was 90% (95% CI, 84 to 94). Algorithms identified 1508 children with SLE at PEDSnet institutions (537 with lupus nephritis), 809 of whom were seen in the past 12 months. CONCLUSIONS: Electronic health record-based algorithms for SLE and lupus nephritis demonstrated excellent classification accuracy across PEDSnet institutions.

Elevated numbers of CD163+ macrophages in hearts of simian immunodeficiency virus-infected monkeys correlate with cardiac pathology and fibrosis.

The role of macrophage activation, traffic, and accumulation on cardiac pathology was examined in 23 animals. Seventeen animals were simian immunodeficiency virus (SIV) infected, 12 were CD8 lymphocyte depleted, and the remaining six were uninfected controls (two CD8 lymphocyte depleted, four nondepleted). None of the uninfected controls had cardiac pathology. One of five (20%) SIV-infected, non-CD8 lymphocyte-depleted animals had minor cardiac pathology with increased numbers of macrophages in ventricular tissue compared to controls. Seven of the 12 (58%) SIV-infected, CD8 lymphocyte-depleted animals had cardiac pathology in ventricular tissues, including macrophage infiltration and myocardial degeneration. The extent of fibrosis (measured as the percentage of collagen per tissue area) was increased 41% in SIV-infected, CD8 lymphocyte-depleted animals with cardiac pathology compared to animals without pathological abnormalities. The number of CD163+ macrophages increased significantly in SIV-infected, CD8 lymphocyte-depleted animals with cardiac pathology compared to ones without pathology (1.66-fold) and controls (5.42-fold). The percent of collagen (percentage of collagen per total tissue area) positively correlated with macrophage numbers in ventricular tissue in SIV-infected animals. There was an increase of BrdU+ monocytes in the heart during late SIV infection, regardless of pathology. These data implicate monocyte/macrophage activation and accumulation in the development of cardiac pathology with SIV infection.