Anthony J Lisi1, A Lucile Burgo-Black, Todd Kawecki, Cynthia A Brandt, Joseph L Goulet. 1. *VA Connecticut Health Care System, West Haven, CT †University of Bridgeport College of Chiropractic, Bridgeport, CT; and ‡Department of Internal Medicine §Department of Emergency Medicine; and ¶Department of Psychiatry, Yale University School of Medicine, New Haven, CT.
Abstract
STUDY DESIGN: This work compared administrative data obtained from the Department of Veterans Affairs (VA) databases with structured chart review. OBJECTIVE: We set out to determine whether a decision tool using administrative data could discriminate acute from nonacute cases among the many patients seen for a low back pain (LBP)-related diagnosis. SUMMARY OF BACKGROUND DATA: Large health care systems' databases present an opportunity for conducting research and planning operations related to the management of highly burdensome conditions. An efficient method of identifying cases of acute LBP in these databases may be useful. METHODS: This was a retrospective review of all consecutive Iraq and/or Afghanistan Veterans seen in a VA primary care service during a 6-month period. Administrative data were extracted from VA databases. Patients with at least 1 encounter that was coded with at least 1 LBP-related ICD-9 code were included. Structured chart review of electronic medical record free text was the "gold standard" to determine acute LBP cases. Logistic regression models were used to assess the association of administrative data variables with chart review findings. RESULTS: We obtained complete data on 354 patient encounters, of which 83 (23.4%) were designated acute upon chart review. No diagnostic code was more likely to be used in acute cases than nonacute. We identified an administrative data model of 18 variables that were significant and positively associated with an acute case (C-statistic = 0.819). A reduced model of 5 variables including a lumbar magnetic resonance imaging order, tramadol prescription, skeletal muscle relaxant prescription, physical therapy order, and addition of a new LBP-related ICD-9 code to the electronic medical record remained reasonable (C-statistic = 0.784). CONCLUSION: Our results suggest that a decision model can identify acute from nonacute LBP cases in Veterans using readily available VA administrative data. LEVEL OF EVIDENCE: N/A.
STUDY DESIGN: This work compared administrative data obtained from the Department of Veterans Affairs (VA) databases with structured chart review. OBJECTIVE: We set out to determine whether a decision tool using administrative data could discriminate acute from nonacute cases among the many patients seen for a low back pain (LBP)-related diagnosis. SUMMARY OF BACKGROUND DATA: Large health care systems' databases present an opportunity for conducting research and planning operations related to the management of highly burdensome conditions. An efficient method of identifying cases of acute LBP in these databases may be useful. METHODS: This was a retrospective review of all consecutive Iraq and/or Afghanistan Veterans seen in a VA primary care service during a 6-month period. Administrative data were extracted from VA databases. Patients with at least 1 encounter that was coded with at least 1 LBP-related ICD-9 code were included. Structured chart review of electronic medical record free text was the "gold standard" to determine acute LBP cases. Logistic regression models were used to assess the association of administrative data variables with chart review findings. RESULTS: We obtained complete data on 354 patient encounters, of which 83 (23.4%) were designated acute upon chart review. No diagnostic code was more likely to be used in acute cases than nonacute. We identified an administrative data model of 18 variables that were significant and positively associated with an acute case (C-statistic = 0.819). A reduced model of 5 variables including a lumbar magnetic resonance imaging order, tramadol prescription, skeletal muscle relaxant prescription, physical therapy order, and addition of a new LBP-related ICD-9 code to the electronic medical record remained reasonable (C-statistic = 0.784). CONCLUSION: Our results suggest that a decision model can identify acute from nonacute LBP cases in Veterans using readily available VA administrative data. LEVEL OF EVIDENCE: N/A.