S Elizabeth Williams1, Ryan Carnahan2, Shanthi Krishnaswami3, Melissa L McPheeters4. 1. Vanderbilt Vaccine Research Program, Vanderbilt University Medical Center, 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, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Suite 600, 2525 West End Avenue, Nashville, TN 37203-1738, USA. Electronic address: shanthi.krishnaswami@vanderbilt.edu. 4. Vanderbilt Evidence-based Practice Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Suite 600, 2525 West End Avenue, Nashville, TN 37203-1738, USA; 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 identify and assess billing, procedural, or diagnostic code algorithms used to identify transverse myelitis in administrative databases. METHODS: We searched the MEDLINE database from 1991 to September 2012 using controlled vocabulary and key terms related to transverse myelitis. We also searched the reference lists of included studies. Two investigators independently assessed the full text of studies against pre-determined inclusion criteria. Two reviewers independently extracted data regarding participant and algorithm characteristics. RESULTS: Three studies met criteria for inclusion in this review. The only algorithm based solely on administrative claims data with a reported positive predictive value included five ICD-9 codes (codes 341.20, 341.21, 341.22, 323.8, 323.9). The positive predictive value for physician-diagnosed acute transverse myelitis was 62%. CONCLUSIONS: More research is needed to establish an accurate algorithm to identify transverse myelitis in large administrative databases using diagnosis and/or procedure codes. Use of standardized consensus definitions, clear description for algorithm selection, and reporting of validation procedure and results would be most beneficial.
PURPOSE: To identify and assess billing, procedural, or diagnostic code algorithms used to identify transverse myelitis in administrative databases. METHODS: We searched the MEDLINE database from 1991 to September 2012 using controlled vocabulary and key terms related to transverse myelitis. We also searched the reference lists of included studies. Two investigators independently assessed the full text of studies against pre-determined inclusion criteria. Two reviewers independently extracted data regarding participant and algorithm characteristics. RESULTS: Three studies met criteria for inclusion in this review. The only algorithm based solely on administrative claims data with a reported positive predictive value included five ICD-9 codes (codes 341.20, 341.21, 341.22, 323.8, 323.9). The positive predictive value for physician-diagnosed acute transverse myelitis was 62%. CONCLUSIONS: More research is needed to establish an accurate algorithm to identify transverse myelitis in large administrative databases using diagnosis and/or procedure codes. Use of standardized consensus definitions, clear description for algorithm selection, and reporting of validation procedure and results would be most beneficial.
Authors: Chester Ho; Sara J T Guilcher; Nicole McKenzie; Magda Mouneimne; Anita Williams; Jennifer Voth; Yan Chen; Shawna Cronin; Vanessa K Noonan; Susan B Jaglal Journal: Top Spinal Cord Inj Rehabil Date: 2017
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