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VA » Health Care » VA Center for Clinical Management Research » Impact of CCMR Work - Methods
VA Center for Clinical Management Research
Impact of CCMR Work - Methods
A model to predict central line-associated bloodstream infection among patients with PICCs
- We developed a model to estimate an individual's risk of central-line-associated bloodstream infection (CLABSI) from peripherally inserted catheters (PICCs).
- Herc E, Patel P, Conlon A, Washer L, Flanders SA, Chopra V. A model to predict central line- associated bloodstream infection among patients with PICCs: The Michigan PICC CLABSI Risk Score. Infect Control Hosp Epid 2017; 38(10):1155-1166.
Multi-morbidity index weighted to physical functioning
- The Multimorbidity-Weighted Index (MWI) was strongly associated with subjective and objective physical and cognitive performance thus serves as a valid patient-centered measure of multimorbidity.
- Wei M, Kabeto M, Langa KM, Mukamal K. Multimorbidity and physical and cognitive function: performance of a new multimorbidity-weighted index. J Gerontol A Biol Sci Med Sci. 2018; 73(2): 225-232.
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.
- Yadlowsky S, Hayward RA, Sussman JB , McClelland RL, Min Y-I, Basu S. Clinical implications of revised pooled cohort equations for estimating atherosclerotic cardiovascular disease risk. Annals of Internal Medicine 2018; 169(1):20-29.
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).
- Basu S, Sussman JB , Berkowitz SA, Hayward RA, Yudkin JS. Development and validation of risk equations for complications of type 2 diabetes (RECODe) using individual participant data from randomized trials. Lancet Diabetes Endocrinol 2017; 5(10): 788-789.
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.
- Waljee AK, Lipson R, Wiitala WL, Zhang Y, Liu B, Zhu J, Wallace B, Govani SM, Stidham RW, Hayward R, Higgins PDR. Predicting hospitalization and outpatient corticosteroid use in inflammatory bowel disease patients using machine learning. Inflamm Bowel Dis 2017; 24(1):45-53.
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.