OBJECTIVES: Identification of patients with left ventricular systolic dysfunction is the first step in identifying which patients may benefit from clinical practice guidelines. The purpose of this study was to develop and validate a computerized tool using clinical information that is commonly available to identify patients with left ventricular systolic dysfunction (LVSD). METHODS: We performed a cross-sectional study of patients seen in a Department of Veterans Affairs General Internal Medicine Clinic who had echocardiography or radionuclide ventriculography performed as part of their clinical care. RESULTS: We identified 2246 subjects who had at least one cardiac imaging study. A total of 778 (34.6%) subjects met study criteria for LVSD. Subjects with LVSD were slightly older than subjects without LVSD (70 years vs 68 years, P =.00002) but were similar with regard to sex and race. Subjects with LVSD were more likely to have prescriptions for angiotensin-converting enzyme (ACE) inhibitors, carvedilol, digoxin, loop diuretics, hydralazine, nitrates, and angiotensin II receptor antagonists. Of the variables included in the final predictive model, ACE inhibitors, loop diuretics, and digoxin exerted the greatest predictive power. Discriminant analysis demonstrated that models containing pharmacy information were consistently more accurate (75% accurate [65% sensitivity, 81% specificity]) than those models that contained only International Classification of Diseases, 9th revision (ICD-9), codes, including ICD-9 codes for congestive heart failure (72% accurate [80% sensitivity, 68% specificity]). CONCLUSIONS: We demonstrated that an automated, computer-driven algorithm identifying LVSD permits simple, rapid, and timely identification of patients with congestive heart failure by use of only routinely collected data. Future research is needed to develop accurate electronic identification of heart failure and other common chronic conditions.
OBJECTIVES: Identification of patients with left ventricular systolic dysfunction is the first step in identifying which patients may benefit from clinical practice guidelines. The purpose of this study was to develop and validate a computerized tool using clinical information that is commonly available to identify patients with left ventricular systolic dysfunction (LVSD). METHODS: We performed a cross-sectional study of patients seen in a Department of Veterans Affairs General Internal Medicine Clinic who had echocardiography or radionuclide ventriculography performed as part of their clinical care. RESULTS: We identified 2246 subjects who had at least one cardiac imaging study. A total of 778 (34.6%) subjects met study criteria for LVSD. Subjects with LVSD were slightly older than subjects without LVSD (70 years vs 68 years, P =.00002) but were similar with regard to sex and race. Subjects with LVSD were more likely to have prescriptions for angiotensin-converting enzyme (ACE) inhibitors, carvedilol, digoxin, loop diuretics, hydralazine, nitrates, and angiotensin II receptor antagonists. Of the variables included in the final predictive model, ACE inhibitors, loop diuretics, and digoxin exerted the greatest predictive power. Discriminant analysis demonstrated that models containing pharmacy information were consistently more accurate (75% accurate [65% sensitivity, 81% specificity]) than those models that contained only International Classification of Diseases, 9th revision (ICD-9), codes, including ICD-9 codes for congestive heart failure (72% accurate [80% sensitivity, 68% specificity]). CONCLUSIONS: We demonstrated that an automated, computer-driven algorithm identifying LVSD permits simple, rapid, and timely identification of patients with congestive heart failure by use of only routinely collected data. Future research is needed to develop accurate electronic identification of heart failure and other common chronic conditions.
Authors: Joy R Goebel; Lynn V Doering; Lorraine S Evangelista; Adeline M Nyamathi; Sally L Maliski; Steven M Asch; Cathy D Sherbourne; Lisa R Shugarman; Andy B Lanto; Angela Cohen; Karl A Lorenz Journal: J Card Fail Date: 2008-11-13 Impact factor: 5.712
Authors: Job F M van Boven; Joost J van Raaij; Ruben van der Galiën; Maarten J Postma; Thys van der Molen; P N Richard Dekhuijzen; Stefan Vegter Journal: NPJ Prim Care Respir Med Date: 2014-10-02 Impact factor: 2.871