Andrea L Oliverio1, Dorota Marchel2, Jonathan P Troost3, Isabelle Ayoub4, Salem Almaani4, Jessica Greco4, Cheryl L Tran5, Michelle R Denburg6, Michael Matheny7,8, Chad Dorn8, Susan F Massengill9, Hailey Desmond2, Debbie S Gipson2, Laura H Mariani1. 1. Department of Internal Medicine, Nephrology, University of Michigan, Ann Arbor, Michigan. 2. Department of Pediatrics, CS Mott Children's Hospital, Ann Arbor, Michigan. 3. Michigan Institute for Clinical and Health Research, University of Michigan, Ann Arbor, Michigan. 4. Department of Internal Medicine, Division of Nephrology, The Ohio State University Wexner Medical Center, Columbus, Ohio. 5. Division of Pediatric Nephrology, Mayo Clinic, Rochester, Minnesota. 6. Division of Nephrology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania. 7. Department of Veterans Affairs, Vanderbilt University, Nashville, Tennessee. 8. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee. 9. Pediatric Nephrology, Levine Children's Hospital at Atrium Health, Charlotte, North Carolina.
Abstract
Background: Primary nephrotic syndromes are rare diseases which can impede adequate sample size for observational patient-oriented research and clinical trial enrollment. A computable phenotype may be powerful in identifying patients with these diseases for research across multiple institutions. Methods: A comprehensive algorithm of inclusion and exclusion ICD-9 and ICD-10 codes to identify patients with primary nephrotic syndrome was developed. The algorithm was executed against the PCORnet CDM at three institutions from January 1, 2009 to January 1, 2018, where a random selection of 50 cases and 50 noncases (individuals not meeting case criteria seen within the same calendar year and within 5 years of age of a case) were reviewed by a nephrologist, for a total of 150 cases and 150 noncases reviewed. The classification accuracy (sensitivity, specificity, positive and negative predictive value, F1 score) of the computable phenotype was determined. Results: The algorithm identified a total of 2708 patients with nephrotic syndrome from 4,305,092 distinct patients in the CDM at all sites from 2009 to 2018. For all sites, the sensitivity, specificity, and area under the curve of the algorithm were 99% (95% CI, 97% to 99%), 79% (95% CI, 74% to 85%), and 0.9 (0.84 to 0.97), respectively. The most common causes of false positive classification were secondary FSGS (nine out of 39) and lupus nephritis (nine out of 39). Conclusion: This computable phenotype had good classification in identifying both children and adults with primary nephrotic syndrome utilizing only ICD-9 and ICD-10 codes, which are available across institutions in the United States. This may facilitate future screening and enrollment for research studies and enable comparative effectiveness research. Further refinements to the algorithm including use of laboratory data or addition of natural language processing may help better distinguish primary and secondary causes of nephrotic syndrome.
Background: Primary nephrotic syndromes are rare diseases which can impede adequate sample size for observational patient-oriented research and clinical trial enrollment. A computable phenotype may be powerful in identifying patients with these diseases for research across multiple institutions. Methods: A comprehensive algorithm of inclusion and exclusion ICD-9 and ICD-10 codes to identify patients with primary nephrotic syndrome was developed. The algorithm was executed against the PCORnet CDM at three institutions from January 1, 2009 to January 1, 2018, where a random selection of 50 cases and 50 noncases (individuals not meeting case criteria seen within the same calendar year and within 5 years of age of a case) were reviewed by a nephrologist, for a total of 150 cases and 150 noncases reviewed. The classification accuracy (sensitivity, specificity, positive and negative predictive value, F1 score) of the computable phenotype was determined. Results: The algorithm identified a total of 2708 patients with nephrotic syndrome from 4,305,092 distinct patients in the CDM at all sites from 2009 to 2018. For all sites, the sensitivity, specificity, and area under the curve of the algorithm were 99% (95% CI, 97% to 99%), 79% (95% CI, 74% to 85%), and 0.9 (0.84 to 0.97), respectively. The most common causes of false positive classification were secondary FSGS (nine out of 39) and lupus nephritis (nine out of 39). Conclusion: This computable phenotype had good classification in identifying both children and adults with primary nephrotic syndrome utilizing only ICD-9 and ICD-10 codes, which are available across institutions in the United States. This may facilitate future screening and enrollment for research studies and enable comparative effectiveness research. Further refinements to the algorithm including use of laboratory data or addition of natural language processing may help better distinguish primary and secondary causes of nephrotic syndrome.
Authors: Geoffrey H Tison; Alanna M Chamberlain; Mark J Pletcher; Shannon M Dunlay; Susan A Weston; Jill M Killian; Jeffrey E Olgin; Véronique L Roger Journal: Int J Med Inform Date: 2018-09-19 Impact factor: 4.046
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Authors: Katherine N Bachmann; Christianne L Roumie; Andrew D Wiese; Carlos G Grijalva; John B Buse; Robert Bradford; Emily O Zalimeni; Patricia Knoepp; Sofia Dard; Heather L Morris; William T Donahoo; Nada Fanous; Vivian Fonseca; Bonnie Katalenich; Sujung Choi; Darcy Louzao; Emily O'Brien; Megan M Cook; Russell L Rothman; Rosette J Chakkalakal Journal: Pharmacol Res Perspect Date: 2020-10
Authors: Christopher B Forrest; Kathleen M McTigue; Adrian F Hernandez; Lauren W Cohen; Henry Cruz; Kevin Haynes; Rainu Kaushal; Abel N Kho; Keith A Marsolo; Vinit P Nair; Richard Platt; Jon E Puro; Russell L Rothman; Elizabeth A Shenkman; Lemuel Russell Waitman; Neely A Williams; Thomas W Carton Journal: J Clin Epidemiol Date: 2020-09-28 Impact factor: 6.437
Authors: Jonathan P Troost; Howard Trachtman; Cathie Spino; Frederick J Kaskel; Aaron Friedman; Marva M Moxey-Mims; Richard N Fine; Jennifer J Gassman; Jeffrey B Kopp; Liron Walsh; Rong Wang; Debbie S Gipson Journal: Am J Kidney Dis Date: 2020-08-10 Impact factor: 8.860