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Grinspan ZM, Patel AD, Shellhaas RA, Berg AT, Axeen ET, Bolton J, Clarke DF, Coryell J, Gaillard WD, Goodkin HP, Koh S, Kukla A, Mbwana JS, Morgan LA, Singhal NS, Storey MM, Yozawitz EG, Abend NS, Fitzgerald MP, Fridinger SE, Helbig I, Massey SL, Prelack MS, Buchhalter J. Design and implementation of electronic health record common data elements for pediatric epilepsy: Foundations for a learning health care system. Epilepsia 2021; 62:198-216. [PMID: 33368200 PMCID: PMC10508354 DOI: 10.1111/epi.16733] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 09/28/2020] [Accepted: 09/28/2020] [Indexed: 01/19/2023]
Abstract
OBJECTIVE Common data elements (CDEs) are standardized questions and answer choices that allow aggregation, analysis, and comparison of observations from multiple sources. Clinical CDEs are foundational for learning health care systems, a data-driven approach to health care focused on continuous improvement of outcomes. We aimed to create clinical CDEs for pediatric epilepsy. METHODS A multiple stakeholder group (clinicians, researchers, parents, caregivers, advocates, and electronic health record [EHR] vendors) developed clinical CDEs for routine care of children with epilepsy. Initial drafts drew from clinical epilepsy note templates, CDEs created for clinical research, items in existing registries, consensus documents and guidelines, quality metrics, and outcomes needed for demonstration projects. The CDEs were refined through discussion and field testing. We describe the development process, rationale for CDE selection, findings from piloting, and the CDEs themselves. We also describe early implementation, including experience with EHR systems and compatibility with the International League Against Epilepsy classification of seizure types. RESULTS Common data elements were drafted in August 2017 and finalized in January 2020. Prioritized outcomes included seizure control, seizure freedom, American Academy of Neurology quality measures, presence of common comorbidities, and quality of life. The CDEs were piloted at 224 visits at 10 centers. The final CDEs included 36 questions in nine sections (number of questions): diagnosis (1), seizure frequency (9), quality of life (2), epilepsy history (6), etiology (8), comorbidities (2), treatment (2), process measures (5), and longitudinal history notes (1). Seizures are categorized as generalized tonic-clonic (regardless of onset), motor, nonmotor, and epileptic spasms. Focality is collected as epilepsy type rather than seizure type. Seizure frequency is measured in nine levels (all used during piloting). The CDEs were implemented in three vendor systems. Early clinical adoption included 1294 encounters at one center. SIGNIFICANCE We created, piloted, refined, finalized, and implemented a novel set of clinical CDEs for pediatric epilepsy.
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Affiliation(s)
- Zachary M Grinspan
- Departments of Population Health Sciences and Pediatrics, Weill Cornell Medicine, New York, NY
| | - Anup D Patel
- Division of Neurology, Nationwide Children’s Hospital, Columbus, OH, USA
| | - Renée A Shellhaas
- Department of Pediatrics (Pediatric Neurology), Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Anne T Berg
- Division of Neurology, Epilepsy Center, Ann & Robert H. Lurie Children’s Hospital of Chicago and Department of Pediatrics, Northwestern Feinberg School of Medicine, United States of America
| | - Erika T Axeen
- Department of Neurology, University of Virginia, Charlottesville, Virginia
| | - Jeffrey Bolton
- Harvard Medical School, Boston, MA
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts, U.S.A
| | - David F Clarke
- Division of Pediatric Neurology, Department of Neurology, Dell Medical School University of Texas at Austin, Austin, Texas
| | - Jason Coryell
- Departments of Pediatrics and Neurology, Oregon Health and Sciences University, Portland, Oregon
| | - William D Gaillard
- Department of Neurology, Children’s National Health System and School of Medicine, The George Washington University, Washington, District of Columbia
| | - Howard P Goodkin
- Department of Neurology, University of Virginia, Charlottesville, Virginia
| | - Sookyong Koh
- Department of Pediatrics, Emory University School of Medicine, Emory Children’s Center, 2015 Uppergate Drive NE, Atlanta, GA
| | | | - Juma S Mbwana
- Department of Neurology, Children’s National Health System and School of Medicine, The George Washington University, Washington, District of Columbia
| | | | - Nilika S Singhal
- Departments of Pediatrics and Neurology, Seattle Children’s Hospital, University of Washington, and Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA
| | - Margaret M Storey
- Department of History, College of Liberal Arts & Social Sciences, DePaul University, Chicago, IL
| | - Elissa G Yozawitz
- Saul Korey Department of Neurology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY
| | - Nicholas S Abend
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
| | - Mark P Fitzgerald
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
| | - Sara E Fridinger
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
| | - Ingo Helbig
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia
- Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Shavonne L Massey
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
| | - Marisa S Prelack
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA
| | - Jeffrey Buchhalter
- Department of Neurology, St Joseph’s Hospital and Medical Center, Phoenix, Arizona
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Kukla A, Radosevich DM, Finger EB, Kandaswamy R. High urine amylase level and the risk of enteric conversion in solitary pancreas transplant recipients. Transplant Proc 2015; 46:1938-41. [PMID: 25131076 DOI: 10.1016/j.transproceed.2014.05.081] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Compared with enteric drainage, bladder-drained solitary pancreas transplants can be monitored for rejection by measuring urine amylase levels. However, bladder drainage is associated with a higher risk of infection and metabolic complications, necessitating enteric conversion in about one third of patients. We hypothesized that hypersecreting pancreata with high urine amylase levels have a higher propensity for enteric conversion from an antecedent elevated enzymatic effect on the urinary tract and increased fluid losses. PATIENTS AND METHODS We analyzed the risk for enteric conversion in 312 bladder-drained solitary pancreas transplant recipients. Urine amylase levels at 30 days were used to identify those at risk for enteric conversion. Time-to-event analysis was used to evaluate the risk of enteric conversion at 10 years, adjusting for urine amylase level and other confounding factors. Confounding risk factors statistically related to enteric conversion were incorporated into the multivariable analysis by using Cox proportional hazards regression at 3 years' posttransplant. RESULTS During the median follow-up of 184.6 months, 31% of recipients underwent duct conversion. A majority of recipients (84.5%) who required duct conversion were primary transplants. The 30-day median urine amylase level was 1749 IU/h (quartile 1, <777 IU/h; quartile 3, ≥3272 IU/h). Using receiver operating characteristic analysis, it was determined that urine amylase levels >3272 IU/h had the greatest specificity for predicting risk of enteric conversion. In the multivariate analysis, high urine amylase levels increased the risk of enteric conversion only in repeated pancreas transplants. CONCLUSIONS Primary transplants are more likely to undergo enteric conversion than retransplants. High urine amylase levels increase the risk of enteric conversion in retransplants only, and therefore this enzyme alone cannot serve as the sole predictor for conversion in primary transplants. Other factors, such as fluid and bicarbonate losses, increased bladder pressure, and a pre-existing lower urinary tract pathologic condition may be also responsible for the development of complications; these factors warrant additional study.
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Affiliation(s)
- A Kukla
- Division of Renal Diseases and Hypertension, University of Minnesota, Minneapolis, Minnesota
| | - D M Radosevich
- Department of Surgery, Department of Medicine, University of Minnesota, Minneapolis, Minnesota
| | - E B Finger
- Department of Surgery, Department of Medicine, University of Minnesota, Minneapolis, Minnesota
| | - R Kandaswamy
- Department of Surgery, Department of Medicine, University of Minnesota, Minneapolis, Minnesota.
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Abstract
Kidney donors, similar to the general population, are at risk for development of type 2 diabetes mellitus (T2DM). The course of donors who develop T2DM has not been studied. We surveyed 3777 kidney donors regarding the development of T2DM. Of the 2954 who responded, 154 developed T2DM 17.7 +/- 9.0 years after donation. The multivariable risk of development of T2DM was associated with type 1 DM in the recipient, male gender and body mass index >30 kg/m(2) at time of donation. Compared to age, gender, duration after donation and body mass index (BMI)-matched non-diabetic donor controls; diabetic donors were more likely to have hypertension (70.8% vs. 36.2%, p = 0.005), proteinuria (18.8% vs. 3.9%, p < 0.0001) but had a similar serum creatinine. eGFR change after T2DM development was -0.80 +/- 0.94 mL/min/year, -0.70 +/- 0.86 in nondiabetic donors with similar duration after donation and -0.61 +/- 0.76 mL/min/year in age, gender, BMI and duration after donation matched nondiabetic donor controls. These preliminary and short-term data demonstrate that factors associated with T2DM in kidney donors are similar to those in the general population and donors screened carefully at the time of donation do not appear to have an acceleration of diabetic kidney disease.
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Affiliation(s)
- H. N. Ibrahim
- Department of Medicine, University of Minnesota, Minneapolis, MN
| | - A. Kukla
- Department of Medicine, University of Minnesota, Minneapolis, MN
| | - G. Cordner
- Department of Surgery, University of Minnesota, Minneapolis, MN
| | - R. Bailey
- Department of Surgery, University of Minnesota, Minneapolis, MN
| | - K. Gillingham
- Department of Surgery, University of Minnesota, Minneapolis, MN
| | - A. J. Matas
- Department of Surgery, University of Minnesota, Minneapolis, MN
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