Aaron W Miller1, Catherine A McCarty2, Ulrich Broeckel3, Vangelis Hytopoulos4, Deanna S Cross5. 1. Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, 1000 North Oak Avenue, Marshfield, WI 54449, USA. Electronic address: miller.aaron@mcrf.mfldclin.edu. 2. Essentia Institute of Rural Health, 502 East Second Street, Duluth, MN 55805, USA. 3. Department of Pediatrics, Medical College of Wisconsin, TBCR-CRI, Room C2440, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA. 4. Aviir Corp, Department of Computational Sciences, 9805 Research Drive, Irvine, CA 92618, USA. 5. Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Boulevard, Fort Worth, TX 76107, USA.
Abstract
OBJECTIVE: We aim to quantify HMG-CoA reductase inhibitor (statin) prescriber-intended exposure-time using a generalizable algorithm that interrogates data stored in the electronic health record (EHR). MATERIALS AND METHODS: This study was conducted using the Marshfield Clinic (MC) Personalized Medicine Research Project (PMRP) a central Wisconsin-based population and biobank with, on average, 30 years of electronic health data available in the independently-developed MC Cattails MD EHR. Individuals with evidence of statin exposure were identified from the electronic records, and manual chart abstraction of all mentions of prescribed statins was completed. We then performed electronic chart abstraction of prescriber-intended exposure time for statins, using previously identified logic to capture pill-splitting events, normalizing dosages to atorvastatin-equivalent dose. Four models using iterative training sets were tested to capture statin end-dates. Calculated cumulative provider-intended exposures were compared to manually abstracted gold-standard measures of ordered statin prescriptions, and aggregate model results (totals) for training and validation populations were compared. The most successful model was the one with the smallest discordance between modeled and manually abstracted Atorvastatin 10mg/year Equivalents (AEs). RESULTS: Of the approximately 20,000 patients enrolled in the PMRP, 6243 were identified with statin exposure during the study period (1997-2011), 59.8% of whom had been prescribed multiple statins over an average of approximately 11 years. When the best-fit algorithm was implemented and validated by manual chart review for the statin-ordered population, it was found to capture 95.9% of the correlation between calculated and expected statin provider-intended exposure time for a random validation set, and the best-fit model was able to predict intended statin exposure to within a standard deviation of 2.6 AEs, with a standard error of +0.23 AEs. CONCLUSION: We demonstrate that normalized provider-intended statin exposure time can be estimated using a combination of structured clinical data sources, including a medications ordering system and a clinical appointment coordination system, supplemented with text data from clinical notes.
OBJECTIVE: We aim to quantify HMG-CoA reductase inhibitor (statin) prescriber-intended exposure-time using a generalizable algorithm that interrogates data stored in the electronic health record (EHR). MATERIALS AND METHODS: This study was conducted using the Marshfield Clinic (MC) Personalized Medicine Research Project (PMRP) a central Wisconsin-based population and biobank with, on average, 30 years of electronic health data available in the independently-developed MCCattails MD EHR. Individuals with evidence of statin exposure were identified from the electronic records, and manual chart abstraction of all mentions of prescribed statins was completed. We then performed electronic chart abstraction of prescriber-intended exposure time for statins, using previously identified logic to capture pill-splitting events, normalizing dosages to atorvastatin-equivalent dose. Four models using iterative training sets were tested to capture statin end-dates. Calculated cumulative provider-intended exposures were compared to manually abstracted gold-standard measures of ordered statin prescriptions, and aggregate model results (totals) for training and validation populations were compared. The most successful model was the one with the smallest discordance between modeled and manually abstracted Atorvastatin 10mg/year Equivalents (AEs). RESULTS: Of the approximately 20,000 patients enrolled in the PMRP, 6243 were identified with statin exposure during the study period (1997-2011), 59.8% of whom had been prescribed multiple statins over an average of approximately 11 years. When the best-fit algorithm was implemented and validated by manual chart review for the statin-ordered population, it was found to capture 95.9% of the correlation between calculated and expected statin provider-intended exposure time for a random validation set, and the best-fit model was able to predict intended statin exposure to within a standard deviation of 2.6 AEs, with a standard error of +0.23 AEs. CONCLUSION: We demonstrate that normalized provider-intended statin exposure time can be estimated using a combination of structured clinical data sources, including a medications ordering system and a clinical appointment coordination system, supplemented with text data from clinical notes.
Authors: Zheng Zhou; Elham Rahme; Michal Abrahamowicz; Jack V Tu; Mark J Eisenberg; Karin Humphries; Peter C Austin; Louise Pilote Journal: CMAJ Date: 2005-04-26 Impact factor: 8.262
Authors: Gregory A Nichols; Jay Desai; Jennifer Elston Lafata; Jean M Lawrence; Patrick J O'Connor; Ram D Pathak; Marsha A Raebel; Robert J Reid; Joseph V Selby; Barbara G Silverman; John F Steiner; W F Stewart; Suma Vupputuri; Beth Waitzfelder Journal: Prev Chronic Dis Date: 2012-06-07 Impact factor: 2.830
Authors: Huong Q McLean; Brian D W Chow; Jeffrey J VanWormer; Jennifer P King; Edward A Belongia Journal: J Infect Dis Date: 2016-07-28 Impact factor: 5.226