Identifying Patients With Severe Sepsis Using Administrative Claims Patient-Level Validation of the Angus Implementation of the International Consensus Conference Definition of Severe Sepsis

Background:Severe sepsis is a common and costly problem. Although consistently defined clinically by consensus conference since 1991, there have been several different implementations of the severe sepsis definition using ICD-9-CM codes for research. We conducted a single center, patient-level validation of 1 common implementation of the severe sepsis definition, the so-called Angus implementation.Methods:Administrative claims for all hospitalizations for patients initially admitted to general medical services from an academic medical center in 2009-2010 were reviewed. On the basis of ICD-9-CM codes, hospitalizations were sampled for review by 3 internal medicine-trained hospitalists. Chart reviews were conducted with a structured instrument, and the gold standard was the hospitalists' summary clinical judgment on whether the patient had severe sepsis.Results:Three thousand one hundred forty-six (13.5%) hospitalizations met ICD-9-CM criteria for severe sepsis by the Angus implementation (Angus-positive) and 20,142 (86.5%) were Angus-negative. Chart reviews were performed for 92 randomly selected Angus-positive and 19 randomly-selected Angus-negative hospitalizations. Reviewers had a of 0.70. The Angus implementation's positive predictive value was 70.7% [95% confidence interval (CI): 51.2%, 90.5%]. The negative predictive value was 91.5% (95% CI: 79.0%, 100%). The sensitivity was 50.4% (95% CI: 14.8%, 85.7%). Specificity was 96.3% (95% CI: 92.4%, 100%). Two alternative ICD-9-CM implementations had high positive predictive values but sensitivities of <20%.Conclusions:The Angus implementation of the international consensus conference definition of severe sepsis offers a reasonable but imperfect approach to identifying patients with severe sepsis when compared with a gold standard of structured review of the medical chart by trained hospitalists.