James H Flory1, Jason Roy2, Joshua J Gagne3, Kevin Haynes4, Lisa Herrinton5, Christine Lu6, Elisabetta Patorno7, Azadeh Shoaibi8, Marsha A Raebel5. 1. Weill Cornell Medicine, NY, USA. 2. University of Pennsylvania, Philadelphia, PA, 9103, USA. 3. Brigham & Women's Hospital, Boston, MA, 02115, USA. 4. HealthCore Inc, Wilmingon, DE, 19801, USA. 5. Kaiser Permanente, Oakland, CA, 94612, USA. 6. Harvard Pilgrim, Boston, MA, 02215, USA. 7. Partner's Healthcare System, Boston, MA, 02215, USA. 8. US FDA, Silver Spring, MD, 20993, USA.
Abstract
AIM: Laboratory test (lab) results may be useful to detect incident diabetes in electronic health record and claims-based studies. RESEARCH DESIGN & METHODS: Using the Mini-Sentinel distributed database, we assessed the value of lab results added to diagnosis codes and dispensing claims to identify incident diabetes. RESULTS: Inclusion of lab results increased the number of diabetes outcomes identified by 21%. In settings where capture of lab results was relatively complete, the absence of lab results was associated with implausibly low rates of the outcome. CONCLUSION: Lab results can increase sensitivity of algorithms for detecting diabetes, and missing lab results are associated with much lower rates of diabetes ascertainment regardless of algorithm. Patterns of missing lab results may identify ascertainment bias.
AIM: Laboratory test (lab) results may be useful to detect incident diabetes in electronic health record and claims-based studies. RESEARCH DESIGN & METHODS: Using the Mini-Sentinel distributed database, we assessed the value of lab results added to diagnosis codes and dispensing claims to identify incident diabetes. RESULTS: Inclusion of lab results increased the number of diabetes outcomes identified by 21%. In settings where capture of lab results was relatively complete, the absence of lab results was associated with implausibly low rates of the outcome. CONCLUSION: Lab results can increase sensitivity of algorithms for detecting diabetes, and missing lab results are associated with much lower rates of diabetes ascertainment regardless of algorithm. Patterns of missing lab results may identify ascertainment bias.
Authors: Marsha A Raebel; Susan M Shetterly; Bharati Bhardwaja; Andrew T Sterrett; Emily B Schroeder; Joseph Chorny; Tyson P Hagen; David J Silverman; Rex Astles; Ira M Lubin Journal: Popul Health Manag Date: 2019-05-20 Impact factor: 2.459
Authors: Sruthi Adimadhyam; Erin F Barreto; Noelle M Cocoros; Sengwee Toh; Jeffrey S Brown; Judith C Maro; Jacqueline Corrigan-Curay; Gerald J Dal Pan; Robert Ball; David Martin; Michael Nguyen; Richard Platt; Xiaojuan Li Journal: J Am Soc Nephrol Date: 2020-10-19 Impact factor: 10.121
Authors: Marsha A Raebel; LeeAnn M Quintana; Emily B Schroeder; Susan M Shetterly; Lisa E Pieper; Paul L Epner; Laura K Bechtel; David H Smith; Andrew T Sterrett; Joseph A Chorny; Ira M Lubin Journal: Arch Pathol Lab Med Date: 2018-12-10 Impact factor: 5.534