Biomedical subjects

Kristine Ruppert

Publications and source records attributed to Kristine Ruppert.

3 recordsLinked to original sources

PubMed2006

Developing and validating a diabetes database in a large health system.

BACKGROUND: One component of clinical information systems is a registry of patients. Registries allow providers to identify gaps in care at the population level. Registries also allow for rapid cycle continuous quality improvement, targeted practice change and improved outcomes. Most registries are built based on membership with an insurer or other selection criteria. Little, if any data exist on registries representing demographically heterogeneous populations. METHODS: Administrative and clinical data for the period 1/1/2000-12/30/03 were examined. In total, 46,082,941 lab reports, 233,292,544 medical records, and 9,351,415 medical record abstracts, representing approximately 2 million unique patients were searched. The diabetes source population was identified by presence of any one of the following criteria: ICD-9 code 250 (diabetes) for inpatient, emergency room or outpatient visits; any hemoglobin A1c result; blood glucose >200mg/dl; or diabetes medication. A diagnosis of diabetes was verified by trained chart reviewers on a sample of patients. Single indicators and combinations were examined to determine optimal identification of these cases. RESULTS: In two separate validation studies, using two or more indicators or outpatient diagnosis maximized positive predictive value (PPV) (96 and 97%) and sensitivity (99 and 100%) and identified 55,807 individuals. When all patients with a single indicator of outpatient diagnosis (which had the highest single PPV of 94 and 95%) were included together with those having >or=2 indicators, the final sample size was 65,725. CONCLUSION: Two or more indicators or an out-patient-diagnosis identifies a sizeable and unselective diabetes database which can be used to track processes and outcomes.

Databases, Factual↗

PubMed2006

Deficiencies of cardiovascular risk prediction models for type 1 diabetes.

OBJECTIVE: Cardiovascular risk prediction models are available for the general population (Framingham) and for type 2 diabetes (U.K. Prospective Diabetes Study [UKPDS] Risk Engine) but may not be appropriate in type 1 diabetes, as risk factors including younger age at diabetes onset and presence of diabetes complications are not considered. Therefore, our objective was to examine the accuracy of Framingham and UKPDS models for predicting coronary heart disease (CHD) in a type 1 diabetic cohort. RESEARCH DESIGN AND METHODS: Ten-year follow-up data from the Pittsburgh Epidemiology of Diabetes Complications (EDC) study, a prospective cohort study of 658 subjects with childhood-onset type 1 diabetes diagnosed between 1950 and 1980 first seen in 1986-1988, were analyzed. EDC study data were used to calculate the 10-year probability of CHD (fatal CHD, nonfatal myocardial infarction, or Q-waves) applying to the Framingham and UKPDS equations. RESULTS: Mean age at CHD onset was 39 years. When fatal/nonfatal myocardial infarction and CHD death were modeled, both the UKPDS and Framingham models showed significant lack of calibration (P < 0.0001) but moderate discrimination (0.76 UKPDS, 0.77 Framingham men, and 0.88 Framingham women). Both the UKPDS and Framingham models underestimated probability of events in highest risk deciles. CONCLUSIONS: Currently available CHD models poorly predict events in type 1 diabetes. Future research should focus on determining the risk factors accounting for the lack of fit and developing prediction models specific to this high-risk group.

Adult↗

PubMedDate not supplied

Deploying the chronic care model to implement and sustain diabetes self-management training programs.

PURPOSE: The purpose of this project was to evaluate the utility of using the 6 elements of the chronic care model (CCM; health system, community, decision support, self-management support, clinical information systems, and delivery system design) to implement and financially sustain an effective diabetes self-management training (DSMT) program. METHODS: The University of Pittsburgh Medical Center (UPMC) uses all elements of the CCM. Partnerships were formed between UPMC and western Pennsylvanian community hospitals and practices; the American Diabetes Association DSMT recognition program provided decision support. A clinical data repository and reorganization of primary care practices aided in supporting DSMT. The following process and patient outcomes were measured: number of recognized programs, reimbursement, patient hemoglobin A1C levels, and the proportion of patients who received DSMT in primary care practices versus hospital-based programs. RESULTS: Using elements of the CCM, the researchers were able to gain administrative support; expand the number of recognized programs from 3 to 21; cover costs through increased reimbursement; reduce hemoglobin A1C levels (P < .0001), and increase the proportion of patients receiving DSMT through delivery in primary care (26.4% suburban; 19.8% urban) versus hospital-based practices (8.3%; P < .0001). CONCLUSIONS: The CCM serves as an effective model for implementing and sustaining DSMT programs.

Chronic Disease↗