Electronic Medical Records Data Analysis Technologies and Services for the Cardiovascular Diseases Risk Prediction

Warfarin is a common anticoagulant agent for cardiovascular diseases, and it is known to interact with several foods and drugs. Several studies report an interaction between warfarin and herbal medicines; however, the influence of herbal medicines on the international normalized ratio (INR) is still controversial. We investigated the influence of herbal formulas on INR of patients taking warfarin. We searched electronic medical records of inpatients for INR results. Then, we compared the changes in INR and any adverse events between the group taking herbal formulas and warfarin (herbal group) and another group taking warfarin only (nonherbal group). Eighty-six patients were included; 45 patients were assigned to the herbal group and 41 patients to the nonherbal group. The herbal group had taken the same dose of warfarin for a longer period. The nonherbal group had a slightly higher mean INR value than the herbal group. The ratio of INR less than 2 and greater than 3, the ratio of INR that increased or decreased by one or more compared to the initial INR, and the ratio of adverse events were not significantly different between the two groups. It is suggested that use of herbal formulas may not influence INR value.

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Abstract 17956: External Validation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records

Circulation ◽

10.1161/circ.130.suppl_2.17956 ◽

2014 ◽

Vol 130 (suppl_2) ◽

Author(s):

Matthew J Kolek ◽

Amy J Graves ◽

Aihua Bian ◽

Pedro L Teixeira ◽

Moore B Shoemaker ◽

...

Keyword(s):

Atrial Fibrillation ◽

Prediction Model ◽

Electronic Medical Records ◽

Risk Prediction ◽

Medical Records ◽

Risk Model ◽

External Validation ◽

Left Ventricular ◽

Cox Proportional Hazards Model ◽

Ambulatory Setting

Background: Atrial fibrillation (AF) contributes to substantial morbidity, mortality, and healthcare costs. Accurate prediction of incident AF might enhance patient care and improve outcomes. We aimed to externally validate the AF risk model developed by the CHARGE-AF investigators utilizing a large repository of electronic medical records (EMR). Methods: Using a database of de-identified EMRs, we conducted a retrospective cohort study of subjects serially followed in internal medicine clinics at our institution (minimum 3 visits in a 24 month window). Subjects were followed for incident AF from 2005 until 2010. We applied the published CHARGE-AF Cox proportional hazards model beta coefficients to our cohort. Predictors included age, race, height, weight, systolic and diastolic blood pressure, treatment for hypertension, smoking status, diabetes, heart failure, history of myocardial infarction, left ventricular hypertrophy, and PR interval. Calibration and discrimination were assessed by generating calibration plots and calculating C-statistics. Results: The study included 33,494 subjects with median age 57 years (25th to 75th percentile: 49 - 67), 57% women, 86% whites, and 14% African Americans. During the mean follow-up period of 4.8 ± 0.85 years, 2455 (7.3%) subjects developed AF. After correcting for baseline hazard, the CHARGE-AF model over-predicted AF at the highest risk deciles but was otherwise well-calibrated (Figure) and showed good discrimination, with a C-statistic of 0.746 (95% confidence interval: 0.738 to 0.754). Conclusion: From clinical factors readily accessible in a large de-identified EMR repository, we externally validated the CHARGE-AF risk prediction model to identify individuals at risk for developing AF in an ambulatory setting. These data not only provide strong validation for the CHARGE-AF risk prediction tool, but also indicate that the tool, and thus primary prevention strategies, can be implemented in an EMR context.

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