Stephan Chiu1, John Davis1, JoAnn Giaconi2, Aaron Lee3, Greg Orshansky4, Leonard Kleinman4, Irena Tsui5. 1. Jules Stein Eye Institute, David Geffen School of Medicine at UCLA, 100 Stein Plaza, Los Angeles, CA, United States. 2. Jules Stein Eye Institute, David Geffen School of Medicine at UCLA, 100 Stein Plaza, Los Angeles, CA, United States; VA Greater Los Angeles Health Administration, 11301 Wilshire Blvd, Los Angeles, CA, United States. 3. Department of Ophthalmology, University of Washington, 325 Ninth Avenue, Seattle, WA, United States. 4. VA Greater Los Angeles Health Administration, 11301 Wilshire Blvd, Los Angeles, CA, United States. 5. Jules Stein Eye Institute, David Geffen School of Medicine at UCLA, 100 Stein Plaza, Los Angeles, CA, United States; VA Greater Los Angeles Health Administration, 11301 Wilshire Blvd, Los Angeles, CA, United States. Electronic address: itsui@jsei.ucla.edu.
Abstract
PURPOSE: Electronic health data in the form of International Classification of Disease, Ninth Revision (ICD-9) codes is routinely used for clinical research, yet the accuracy of specific diagnoses is largely unknown. The purpose of this study is to assess the validity of computer extracted problem lists for diabetic retinopathy (DR) and other complications of diabetes mellitus (DM) within the VA Greater Los Angeles Health Administration (VHAGLA). METHODS: The study population consisted of patients at the VHAGLA with an ICD-9 diagnosis of DM between Jan 1st 1999 and March 22nd 2016 with visits to the eye clinic. Fifty patients either with or without an ICD-9 diagnosis of DR were randomly selected. The accuracy of ICD-9 codes for DR, as well as related co-morbidities such as hypertension, hyperlipidemia, coronary artery disease (CAD), and cerebrovascular accident (CVA), were assessed through chart review. RESULTS: A total of 3193 patients met our inclusion criteria. Of the 50 patients with an ICD-9 diagnosis of DR, the positive predictive value (PPV) was 0.7. For 50 patients without a ICD-9 diagnosis of DR, the negative predictive value (NPV) was 0.9. Of the other co-morbid medical conditions, NPV ranged from a low of 63% for obesity to a high of 98% for CVA and CAD. CONCLUSION: Validity of ICD-9 diagnoses of diabetic complications in this VA population varied considerably, with DR demonstrating moderate agreement, obesity being more under-documented, and CVA and CAD being more consistently documented. These discrepancies should be considered when using billing codes for research purposes.
PURPOSE: Electronic health data in the form of International Classification of Disease, Ninth Revision (ICD-9) codes is routinely used for clinical research, yet the accuracy of specific diagnoses is largely unknown. The purpose of this study is to assess the validity of computer extracted problem lists for diabetic retinopathy (DR) and other complications of diabetes mellitus (DM) within the VA Greater Los Angeles Health Administration (VHAGLA). METHODS: The study population consisted of patients at the VHAGLA with an ICD-9 diagnosis of DM between Jan 1st 1999 and March 22nd 2016 with visits to the eye clinic. Fifty patients either with or without an ICD-9 diagnosis of DR were randomly selected. The accuracy of ICD-9 codes for DR, as well as related co-morbidities such as hypertension, hyperlipidemia, coronary artery disease (CAD), and cerebrovascular accident (CVA), were assessed through chart review. RESULTS: A total of 3193 patients met our inclusion criteria. Of the 50 patients with an ICD-9 diagnosis of DR, the positive predictive value (PPV) was 0.7. For 50 patients without a ICD-9 diagnosis of DR, the negative predictive value (NPV) was 0.9. Of the other co-morbid medical conditions, NPV ranged from a low of 63% for obesity to a high of 98% for CVA and CAD. CONCLUSION: Validity of ICD-9 diagnoses of diabetic complications in this VA population varied considerably, with DR demonstrating moderate agreement, obesity being more under-documented, and CVA and CAD being more consistently documented. These discrepancies should be considered when using billing codes for research purposes.
Authors: Liora G Rodill; Lieza G Exalto; Paola Gilsanz; Geert Jan Biessels; Charles P Quesenberry; Rachel A Whitmer Journal: Alzheimer Dis Assoc Disord Date: 2018 Apr-Jun Impact factor: 2.703