S Elizabeth Williams1, Ryan Carnahan2, Melissa L McPheeters3. 1. Vanderbilt Vaccine Research Program, Vanderbilt University Medical Center North, 1161 21st Avenue, CCC 5326 Nashville, TN, 37232-0012, USA. Electronic address: elizabeth.williams@vanderbilt.edu. 2. Department of Epidemiology, University of Iowa College of Public Health, S437 CPHB University of Iowa, 105 River Street, Iowa City, IA 52242, USA. Electronic address: ryan-carnahan@uiowa.edu. 3. Vanderbilt Evidence-based Practice Center, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Suite 600, 2525 West End Avenue, Nashville, TN 37203-1738, USA. Electronic address: melissa.mcpheeters@vanderbilt.edu.
Abstract
PURPOSE: To review algorithms used to identify uveitis in administrative and claims databases. METHODS: We searched the MEDLINE database via PubMed from 1991 to September 2012 using vocabulary and key terms related to uveitis. We also searched the reference lists of included studies. Two investigators independently assessed studies against pre-determined inclusion criteria. The same two investigators independently extracted data regarding participant and algorithm characteristics and assessed a study's methodological rigor using a pre-defined approach. RESULTS: Seven studies met inclusion criteria. Variability exists among algorithms employed in these studies for finding cases of uveitis and related conditions as well as in use and implementation of validation methods. Of the seven included studies, three involved case validation. One used a narrow algorithm in addition to text mining of electronic medical records to identify incident cases and found a positive predictive value of 52.1%. The other two, which used broader uveitis definitions and included both incident and prevalent cases, found positive predictive values of 24.8% and 52.6%. CONCLUSIONS: Further research, with case as well as individual code validation, is needed to determine appropriate uveitis algorithms for purposes of active surveillance in administrative data. Decisions about which algorithm to use will depend on the desired balance of sensitivity and specificity.
PURPOSE: To review algorithms used to identify uveitis in administrative and claims databases. METHODS: We searched the MEDLINE database via PubMed from 1991 to September 2012 using vocabulary and key terms related to uveitis. We also searched the reference lists of included studies. Two investigators independently assessed studies against pre-determined inclusion criteria. The same two investigators independently extracted data regarding participant and algorithm characteristics and assessed a study's methodological rigor using a pre-defined approach. RESULTS: Seven studies met inclusion criteria. Variability exists among algorithms employed in these studies for finding cases of uveitis and related conditions as well as in use and implementation of validation methods. Of the seven included studies, three involved case validation. One used a narrow algorithm in addition to text mining of electronic medical records to identify incident cases and found a positive predictive value of 52.1%. The other two, which used broader uveitis definitions and included both incident and prevalent cases, found positive predictive values of 24.8% and 52.6%. CONCLUSIONS: Further research, with case as well as individual code validation, is needed to determine appropriate uveitis algorithms for purposes of active surveillance in administrative data. Decisions about which algorithm to use will depend on the desired balance of sensitivity and specificity.
Authors: Alessandro Montedori; Iosief Abraha; Carlos Chiatti; Francesco Cozzolino; Massimiliano Orso; Maria Laura Luchetta; Joseph M Rimland; Giuseppe Ambrosio Journal: BMJ Open Date: 2016-09-15 Impact factor: 2.692
Authors: Joseph M Simonett; Mahsa A Sohrab; Jennifer Pacheco; Loren L Armstrong; Margarita Rzhetskaya; Maureen Smith; M Geoffrey Hayes; Amani A Fawzi Journal: Sci Rep Date: 2015-08-10 Impact factor: 4.379