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Revised pooled cohort equations for estimating atherosclerotic cardiovascular disease
We revised the 2013 pooled cohort equations (PCEs) using newer data and statistical methods, which improved the clinical accuracy of CVD risk prediction. Approximately 11.8 million U.S. adults previously labeled high-risk (10-year risk 7.5%) by the 2013 PCEs would be relabeled lower-risk by the updated equations.
Risk equations for complications of type 2 diabetes
Updated risk equations for complications of type 2 diabetes were developed using data from the Action to Control Cardiovascular Risk in Diabetes study (ACCORD). The equations were validated for microvascular events using data from the Diabetes Prevention Program Outcomes Study (DPPOS) and for cardiovascular events using data from the Action for Health in Diabetes (Look AHEAD).
Risk prediction model for patients with inflammatory bowel disease
Inflammatory bowel disease (IBD) is a chronic disease characterized by unpredictable episodes of flares and periods of remission. We developed a longitudinal machine learning model, which substantially improved our ability to predict inflammatory bowel disease-related hospitalization and outpatient steroid use. We are applying the same longitudinal methods to develop an advanced prediction model to optimize treatment and access for Veterans with Hepatitis C, as part of a recently funded HSR&D IIR. Our partners in this work are the VA National Program for Gastroenterology, VA Pharmacy Benefits Management, and VA National Hepatitis C Program.
Statistical code for identifying acute hospitalizations using data from the VA's Clinical Data Warehouse (CDW)
Hospital readmission is a key metric of hospital quality and requires the identification of individual hospitalizations. However, in the VA CDW, data are organized by "bedded stays", which is any stay in a health-care facility where a patient is provided a bed. Thus, CDW data pose several challenges to identifying hospitalizations: (1) bedded stays include both non-acute stays (i.e., nursing home, mental health) and acute inpatient hospital care; (2) transfers between VA facilities appear as separate bedded stays; and (3) VA care may be fragmented by non-VA care. We sought to develop a rigorous method to identify acute hospitalizations using the VA CDW, including: (1) dropping non-acute portions of a stay; (2) merging VA to VA transfers when consecutive discharge and admission dates were within one calendar day; and (3) merging hospitalizations that occurred in a non-VA facility.