Development and validation of an electronic medical record (EMR)-based computed phenotype of HIV-1 infection.

Background: Electronic medical record (EMR) computed algorithms allow investigators to screen thousands of patient records to identify specific disease cases. No computed algorithms have been developed to detect all cases of human immunodeficiency virus (HIV) infection using administrative, laboratory, and clinical documentation data outside of the Veterans Health Administration. We developed novel EMR-based algorithms for HIV detection and validated them in a cohort of subjects in the Duke University Health System (DUHS). Methods: We created 2 novel algorithms to identify HIV-infected subjects. Algorithm 1 used laboratory studies and medications to identify HIV-infected subjects, whereas Algorithm 2 used International Classification of Diseases, Ninth Revision (ICD-9) codes, medications, and laboratory testing. We applied the algorithms to a well-characterized cohort of patients and validated both against the gold standard of physician chart review. We determined sensitivity, specificity, and prevalence of HIV between 2007 and 2011 in patients seen at DUHS. Results: A total of 172 271 patients were detected with complete data; 1063 patients met algorithm criteria for HIV infection. In all, 970 individuals were identified by both algorithms, 78 by Algorithm 1 alone, and 15 by Algorithm 2 alone. The sensitivity and specificity of each algorithm were 78% and 99%, respectively, for Algorithm 1 and 77% and 100% for Algorithm 2. The estimated prevalence of HIV infection at DUHS between 2007 and 2011 was 0.6%. Conclusions: EMR-based phenotypes of HIV infection are capable of detecting cases of HIV-infected adults with good sensitivity and specificity. These algorithms have the potential to be adapted to other EMR systems, allowing for the creation of cohorts of patients across EMR systems.

One-year mortality and predictors of death among hospital survivors of acute respiratory distress syndrome.

PURPOSE: Advances in supportive care and ventilator management for acute respiratory distress syndrome (ARDS) have resulted in declines in short-term mortality, but risks of death after survival to hospital discharge have not been well described. Our objective was to quantify the difference between short-term and long-term mortality in ARDS and to identify risk factors for death and causes of death at 1 year among hospital survivors. METHODS: This multi-intensive care unit, prospective cohort included patients with ARDS enrolled between January 2006 and February 2010. We determined the clinical characteristics associated with in-hospital and 1-year mortality among hospital survivors and utilized death certificate data to identify causes of death. RESULTS: Of 646 patients hospitalized with ARDS, mortality at 1 year was substantially higher (41 %, 95% CI 37-45%) than in-hospital mortality (24%, 95% CI 21-27%), P < 0.0001. Among 493 patients who survived to hospital discharge, the 110 (22%) who died in the subsequent year were older (P < 0.001) and more likely to have been discharged to a nursing home, other hospital, or hospice compared to patients alive at 1 year (P < 0.001). Important predictors of death among hospital survivors were comorbidities present at the time of ARDS, and not living at home prior to admission. ARDS-related measures of severity of illness did not emerge as independent predictors of mortality in hospital survivors. CONCLUSIONS: Despite improvements in short-term ARDS outcomes, 1-year mortality is high, mostly because of the large burden of comorbidities, which are prevalent in patients with ARDS.