CM&R 2011 : 3/4 (November) 154 HMORN -Selected Abstracts adjusted for sex, age, BMI percentile, race/ethnicity and clinic department. Results: Overall, blood pressure was measured at >80% of children's visits to pediatric and family medicine clinics. The predicted probability of a blood pressure recorded at a primary care visit increased steadily with age (from .81 in 3-5 year olds to .89 in 15-17 year olds, p for trend <.0001). Of the 60% of visits where children had a recorded BMI, children with a BMI =95th percentile were slightly less likely to have a blood pressure recorded compared to BMI 85th <95th, and BMI<85th (91%, 93%, 94%, respectively, p<.0001). There were no differences by sex or race/ethnicity in the adjusted prevalence of documented blood pressure measurement. Conclusions: Blood pressure measurements are documented at a large proportion of children's visits to pediatric and family medicine clinics. Younger children were less likely to have blood pressure measurements recorded than adolescents. The relative lower likelihood of blood pressure measurement in obese children warrants further investigation given the higher prevalence of hypertension in these children. Background: Despite consistent recommendations in numerous guidelines sponsored by professional societies, screening youth for familial hypercholesterolemia (FH) and/or those with a family history of premature cardiovascular disease (CVD) identifies only a small percentage of high-risk youth. By linking parent and child data in the virtual data warehouse, we can assess current screening practices, factors that impact screening practices and ultimately strategies aimed at improving childhood screening. Aims: the aim of this study is to compare cholesterol testing and screening rates in youth who have at least one parent covered under the same health plan with a history of CVD defined as a history of myocardial infarction, percutaneous coronary artery intervention (PCI) or coronary artery bypass surgery (CABG) and/or a parent with FH established by ICD-9 diagnoses or an abnormal cholesterol level. Methods: A cohort of youth who were 2-26 years of age anytime between service dates inclusive of 01/01/2001 and 12/31/2009 and who were covered as insured members under their parents' health plans within the Scott & White HMO were identified including a subset with any ICD-9 diagnosis code associated with hyperlipidemia. Parent-child linkagages were created using a subscriber_id relationship code and relationship description. The parental cohort with CVD was identified from the CVRN Surveillance study as well as parents who have FH defined as an LDL-C of > 215 mg/dl or a total cholesterol > 300 mg/dl. Results: We found that adherence to guidelines for cholesterol screening of youth at a higher risk for premature CHD is no greater than screening rates for children with a lower risk who do not have an affected parent. Moreover, a family history of CHD and hypercholesterolemia is rarely documented in a dependent's medical record. Conclusions: Cost-ef...

needed to have similar and merge-able data for multi-site projects. Data standards set by NAACCR are optimal for construction of this resource as they are designed to collect tumor data centrally from multiple data sources. The standards establish processes for data exchange and record layout in addition to coordinating input from sponsoring organizations, such as AJCC and NCI. NAACCR is also responsible for incorporating new items of interest as the data used to characterize cancers evolve. We describe how these changes were incorporated into the VDW-TR. Results: AJCC Collaborative Stage I (CS-1), applicable to cases diagnosed beginning with January 2004, brought many changes to data and data formats required for staging. These changes were not incorporated by the VDW tumor file in 2004 due to the lack of ownership and oversight. Discrepancies eventually developed between VDW data dictionary and NAACCR causing data value decay. ICDO-2 histology lists were expanded and recoded in ICDO-3, providing additional challenges, along with other rules-based changes in tumor classification. CS-1 also mandated addition of anatomic site specific factors. Many additional changes occurred in 2010 with CS-2. The specifications incorporated in our current VDW-TR address all of these data changes. Conclusion: The VDW has to adopt NAACCR changes as they are adapted to remain current with all standards. We are now sensitized to monitor and adjust for significant future changes in NAACCR.

concordance between these scores when both measures could be computed. Results: We identified 122,270 eligible patients. Of these, 59.7% (n=73,023) had sufficient data to calculate the lab-based risk score and 88.1% (102,795) clinic-based risk score. Neither score could be calculated for 14.5% (n=17,732). The most common reason for not being able to calculate was missing data on cholesterol. Using the laboratory-based score only, we found 12.9% of the population were at high risk (risk >20%), 24.5% moderate risk (10-20%), and 62.6% low risk (<10%). For those with both risk scores (n=71,280), the lab-based risk score was lower than the clinic-based score for 84.3% of patients (60,060/71,280). The lab-based score was 3.1% lower on average, but the two risk scores were within ±5% for 77.0% of patients (54,874/71,280). The risk scores differed by more than 10% for only 8.7% of patients (n=6236), and in most cases (6098 of 6236), the clinic-based score was higher. Conclusion: Electronic data can be used to classify CVD risk for most adults age 30-74. Risk scores based on BMI tend to estimate risk as higher than scores based on laboratory data. However, the risk scores do not differ by more than 5% for most patients.

needed to have similar and merge-able data for multi-site projects. Data standards set by NAACCR are optimal for construction of this resource as they are designed to collect tumor data centrally from multiple data sources. The standards establish processes for data exchange and record layout in addition to coordinating input from sponsoring organizations, such as AJCC and NCI. NAACCR is also responsible for incorporating new items of interest as the data used to characterize cancers evolve. We describe how these changes were incorporated into the VDW-TR. Results: AJCC Collaborative Stage I (CS-1), applicable to cases diagnosed beginning with January 2004, brought many changes to data and data formats required for staging. These changes were not incorporated by the VDW tumor file in 2004 due to the lack of ownership and oversight. Discrepancies eventually developed between VDW data dictionary and NAACCR causing data value decay. ICDO-2 histology lists were expanded and recoded in ICDO-3, providing additional challenges, along with other rules-based changes in tumor classification. CS-1 also mandated addition of anatomic site specific factors. Many additional changes occurred in 2010 with CS-2. The specifications incorporated in our current VDW-TR address all of these data changes. Conclusion: The VDW has to adopt NAACCR changes as they are adapted to remain current with all standards. We are now sensitized to monitor and adjust for significant future changes in NAACCR.

Abstracts have an enormous impact on the people, processes, and technology throughout KP and other Health Care Organizations. ICD-9 is running out f codes. Hundreds of new diagnosis codes are submitted annually. ICD-10 will allow not only for more codes, but also for greater specificity and thus better epidemiological tracking. How will this change impact data? Where do analysts find the new codes and what process should they follow to get ready for this conversion. What Clarity tables and columns will carry the new codes and how should the mapping be done? This presentation will provide tools for the programmers and guide them to make this conversion less painful. Keywords: High-Level Overview of ICD-10; Difference between ICD-9-CM and ICD-10-CM; Health Methods: A prototype patient SDM tool developed for point of care use is presented to the patient as a companion piece that is congruent with a physician clinical decision support tool called CV Wizard. The patient tool was designed to convey clear, succinct and personalized information about blood pressure, lipids, blood sugar, weight, smoking, and aspirin use. Reversible CV risk associated with each of these risk factors is conveyed using a combination of symbols and text accommodating a range of patient educational and literacy levels. The patient tool was presented to the HealthPartners Patient Council (HPC), the patient education specialist and a number of physician and leadership groups for feedback on content and design. Results: The HPC found the initial version confusing. They wanted more specific information on the values of their current CV risk factors and preferred the more complex tool like the CV Wizard physician tool because of its quantitative detail on reversible CV risk and pharmacologic recommendations. However, they did acknowledge that not every patient would understand that level of detail. They noted that dialogue between the patient and the physician in conjunction with the tool was more important than the tool itself. Others thought the tool was a good start with minor modifications suggested. Conclusion: The HPC preferred more specific CV risk factor values and recommendations than were included on the low literacy, or simple tool we presented. Tools that are tailored or able to accommodate a wide range of educational and literacy levels may be desirable to facilitate provider-patient shared decision making discussions. The version of the patient tool discussed here will be implemented in summer of 2012.