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| Developing a patient-centered model of the risk of perioperative complications in spine surgery | |
|---|---|
| Investigator (PI): | Ratliff, John |
| Performing Organization (PO): |
(Current): Stanford University, School of Medicine, Department of Neurosurgery / (650) 725-5562 |
| Supporting Agency (SA): | Agency for Healthcare Research and Quality (AHRQ) |
| Initial Year: | 2015 |
| Final Year: | 2019 |
| Record Source/Award ID: | RePorter/R01HS023800 |
| Funding: | 2015 Award Amount: $104,080 |
| Award Type: | Grant |
| Abstract: | This project will develop an innovative relative risk model predicting incidence of complications in spine surgery, incorporating both Medicare and non-Medicare aged patients. Spine surgery is a rapidly growing area of health care expenditures; complications in spine surgery significantly add to health care spending and may impact patient outcomes. The elderly, as one of the priority populations studied in research funded by AHRQ, are more prone than are many other groups to perioperative complications. Poor understanding of complication incidence makes patient counseling and shared decision making difficult. Better understanding of how patient factors contribute to operative outcomes may significantly improve patient engagement in discussion of treatment options. Development of a clinical tool that may predict occurrence of complications in spine surgery would be an innovative and valuable addition to the practice of spine surgery. This effort will build upon developed measures, incorporating patient comorbidities, choice of surgical approach, and pre-operative diagnosis into a model estimating risk of complication occurrence. The principal goal of our research effort has been to build optimized and validated predictive models of complications after spinal surgery. A retrospective database assessment will be conducted, using the Medicare and Marketscan databases. Statistical analysis of the databases will be used to generate an appropriate adverse event predictive model, where the impact of individual comorbidities and other patient and procedure factors contributing to higher complication incidence is assessed. The study will aggregate patient data into appropriate cohorts, and analyze the data with logistic regression, CART, and other modeling methods. The innovative goal of this investigation will be to predict both risk of any complication and occurrence of specific types of complications in spinal surgery. After a predictive tool has been developed, the measure will be applied prospectively to spinal surgery patients, where complications will be prospectively assessed by an independent auditor and correlated with recording of patient-centered outcome metrics recorded concurrently in our clinic. Appreciation of how patient factors contribute to surgical outcomes will significantly improve patient education and aid informed decision making, may benefit patient care, and will contribute to implementation of comparative effectiveness relative risk adjustment. |
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| Country: | United States |
| State: | California |
| Zip Code: | 94304 |
| UI: | 20161336 |
| Project Status: | Completed |