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RCS 05-196
Research Career Scientist Award
Xiao-Hua Andrew Zhou, PhD MSc VA Puget Sound Health Care System Seattle Division, Seattle, WA Seattle, WA Funding Period: October 2007 - September 2018 |
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BACKGROUND/RATIONALE:
1. Methods in Personalized Medicine: Background: An important area of medical research is personalized medicine, whose goal is to identify the best treatment to achieve the optimal clinical outcome based on a patient's individual profile, including both genetical and clinical information. 2. Modeling of Health Care Costs of Veterans with Chronic Diseases: Background: New cost models are needed to allow for the accurate identification of disease-attributable costs and investigation of modifiable patient and system factors associated with variation within important care areas. 3. A Joint Evaluation of Surgery-Related Outcomes and Costs across VAMCs: Background: VHA has been strongly committed to reviewing and improving the quality of care provided to veterans. One of the best examples of this activity has been the National Surgical Quality Improvement Program (NSQIP). However, 30-day mortality focused in the NSQIP only provides a limited picture of surgical quality. 4. Preventing Vein Graft Stenosis in Peripheral Vascular Surgery: Background: Vein graft stenosis and failure is a serious clinical problem in peripheral arterial disease (PAD) that requires additional research into the key contributing biological pathways. 5. Re-identification Risk of VA De-Identified Patient Health Data: Background: Privacy, security, and confidentiality of patient level health care data are cornerstones of VA health care. Re-identification is a technique for removing identifying information from a data set before it is released for research or public use. The potential to re-identify individual Veterans in de-identified datasets will become an increasing concern as the size and complexity of electronic health data across the nation. 6. INtegrated Care After Exacerbation of COPD (InCaseE): Background: Chronic obstructive pulmonary disease (COPD) exacerbations are common among Veterans admitted to hospital, lead to decrements in health-related quality of life, and are important drivers of health care expenditures. An intervention to improve COPD care is needed, not only to treat patients for COPD and their accompanying comorbidities, but also to redesign the care delivery system, such as specialties treating patients within Patient Aligned Care Teams (PACT). Determining how to deploy existing specialties using a PACT-Veteran-centric approach is important to improve access, timeliness, and quality of care. 7. Semi-parametric Statistical Methods for Predicting High-cost VA Patients Using High-Dimensional Covariates: Background: The rising demands and health care costs make it urgent to develop new statistical methods to accurately predict high-costs VA patients and important risk factors associated with high costs. Health care cost data are characterized by a high level of skewness and heteroscedastic variances. The large number of variables collected in the VA database provides rich information, but at the same time, imposes great challenges for statistical analysis and computation. The administrative and electronic medical record data from VA databases often contain missing data. The new statistical procedure we propose aims to take advantage of the rich databases in VA for analyzing costs data. It employs and develops state-of-art high-dimensional semiparametric statistical procedures to handle the complexity of VA data sets. OBJECTIVE(S): 1. Methods in Personalized Medicine: Objectives: Develop new statistical methods, called covariate specific treatment effect (CSTE) curve to select the best treatment for individual patients based on patients' marker values. 2. Modeling of Health Care Costs of Veterans with Chronic Diseases: Objectives: Develop robust non-parametric cost models that allow for detailed evaluation of costs within the VA. 3. A Joint Evaluation of Surgery-Related Outcomes and Costs across VAMCs: Objectives: Develop new statistical methods for evaluating short- and long-term costs and develop new statistical methods for assessing surgical care efficiency via jointly modeling surgical morbidity, mortality, and costs. 4. Preventing Vein Graft Stenosis in Peripheral Vascular Surgery: Objectives: Identify the key biological pathways that contribute to a pathological thrombo-inflammatory response and "at risk" patients by phenotype. 5. Re-identification Risk of VA De-Identified Patient Health Data: Objectives: (1) Obtain a comprehensive understanding of issues related to the re-identification risk of VA patient health data that meets HIPAA de-identification criteria; (2) develop standardized statistical procedures to evaluate the re-identification risk of specific HIPAA deidentified VA patient health databases. 6. INtegrated Care After Exacerbation of COPD (InCaseE): Objectives: Test a novel intervention that is aligned with VA operational goals and seeks to improve the quality of care among patients with COPD, improve their quality of life, and reduce their hospital re-admissions and mortality. 7. Semi-parametric Statistical Methods for Predicting High-cost VA Patients Using High-Dimensional Covariates: Objectives: Develop a High Costs Prediction (HCP) system, which employs novel high-dimensional semiparametric statistical methods and algorithms to analyze large VA database with missing values and occurrence of censoring. Combine the HCP system with the existing Care Assessment Needs Scoring (CAN) system, in order to make important progress toward the goal of building a data-driven decision support system. METHODS: 1. Methods in Personalized Medicine: Methods: We developed a new statistical method, called conditional average and quantile treatment effect (CSTE) curves, to select the optimal treatment based the patient's marker value when the clinical outcome of the patient is binary and continuous. We also used B-spine data to estimate these curves. 2. Modeling of Health Care Costs of Veterans with Chronic Diseases: Methods: We utilized semi-parametric and non-parametric models to evaluate costs within the VA. 3. A Joint Evaluation of Surgery-Related Outcomes and Costs across VAMCs: Methods: We received data from the National Surgical Quality Improvement Program (VASQIP) and used it to analyze facility variability in complication rates and costs for the seven common surgical procedures in the VHA. 4. Preventing Vein Graft Stenosis in Peripheral Vascular Surgery: Methods: We conducted a prospective, longitudinal study of PAD patients who underwent leg bypass surgery with autogenous vein. 5. Re-identification Risk of VA De-Identified Patient Health Data: Methods: We performed a literature review on existing statistical concepts and methods for re-identification risk. We also applied the existing methods to a de-identified VA Rheumatoid Arthritis Registry database to assess the de-identification risk of releasing it and made recommendations to VACO about the release of future VA datasets. 6. INtegrated Care After Exacerbation of COPD (InCaseE): Methods: We are using a randomized stepped-wedge design to evaluate a multifaceted intervention that seeks to improve quality-of-life and decrease rate of hospital readmission and mortality among patients with COPD. 7. Semi-parametric Statistical Methods for Predicting High-cost VA Patients Using High-Dimensional Covariates: Methods: We are developing a novel semiparametric procedure for predicting high costs patients. The approach we propose incorporates high-dimensional covariates and nonlinear covariate effects and addresses the challenge of censoring by death, which improves accuracy and increases the flexibility of modeling. We will link data from the Managerial Cost Accounting System (MCA, formerly Decision Support System or DSS) with three VA databases including: the VA Patient Treatment File (PTF); the VA Outpatient Clinic File (OCF); and the VA Beneficiary Identification and Records Locator Subsystem death file. We will compare the newly proposed methods with existing methods using both the VA data and simulated data. FINDINGS/RESULTS: Not yet available. IMPACT: 1. Methods in Personalized Medicine: Impact: The results contribute novel and critical clinical information for the welfare of veterans and address treatment of veterans with colorectal cancer. Our statistical methods contribute to statistical literature in personalize medicine, which is one of most active and important research areas in medicine. 2. Modeling of Health Care Costs of Veterans with Chronic Diseases: Impact: The knowledge gained provides researchers with tools for examining the VA medical care costs with increased precision, to provide more efficient care. 3. A Joint Evaluation of Surgery-Related Outcomes and Costs across VAMCs: Impact: The results from this project provide objective comparisons on VA surgical care across VA facilities and add an additional economic dimension to current assessment. They provide the knowledge to better reward efficient care that improves health. They also improve our understanding of how high quality care can be provided at low cost. 4. Preventing Vein Graft Stenosis in Peripheral Vascular Surgery: Impact: By identifying biological pathways and phenotypes for a pathological thrombo, we can target those patients in greatest need of treatment, while sparing low risk patients from the adverse effects of treatment. 5. Re-identification Risk of VA De-Identified Patient Health Data: Impact: The project adds to the methodology and science of data protection and informs VA policy on release of VA data. This benefits veterans by reducing the risk that veteran data will be compromised, as well as allowing datasets to be more safely and effectively shared, facilitating important future research and policies serving veterans. 6. INtegrated Care After Exacerbation of COPD (InCaseE): Impact: The intervention aims to help veterans with COPD to achieve better clinical outcomes by providing evidence about how VA may expand the responsibilities of specialists to better support patients during high risk periods. 7. Semi-parametric Statistical Methods for Predicting High-cost VA Patients Using High-Dimensional Covariates: Impact: The proposed High Costs Prediction (HCP) system will improve care allocation by identifying patients who are at high-risk of incurring high costs within a subsequent one-year period. Targeting care to these patients can reduce avoidable use of health care services and have a positive impact on reducing costs. The HCP system also allows us to identify disease areas that contribute significantly to high health care costs which policymakers can target by future intervention. External Links for this ProjectNIH ReporterGrant Number: IK6RX002991-01Link: https://reporter.nih.gov/project-details/9612868 Dimensions for VA Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects. Learn more about Dimensions for VA. VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address. Search Dimensions for this project PUBLICATIONS:Journal Articles
DRA:
Health Systems
DRE: none Keywords: none MeSH Terms: none |
