HSR&D Home » Research » CDA 09-014 – HSR&D Study
Predicting Surgical Outcomes with NSQIP and Clinical Monitoring Data
Joshua S Richman, MD PhD MS
Birmingham VA Medical Center, Birmingham, AL
Funding Period: January 2011 - December 2015
Approximately 375,000 surgical procedures per year are performed in the VHA, most being monitored by VA Surgical Quality Improvement Program (VASQIP, formerly NSQIP). A study using VASQIP data from FY-04 found 3.1% 30-day mortality, 10.7% at one year, and 43% at five years. While the VASQIP was initially successful in reducing peri-operative morbidity and mortality, further improvement has slowed. To continue improvement, new research must be pursued in two directions, exploiting new sources data and applying improved analytic methods to identify subgroups at risk for adverse outcomes.
The objectives of this study are to a) identify novel predictors of adverse outcomes with a focus on physiologic dynamics, b) Implement and evaluate Atul Gawande's "Surgical Apgar" risk score using VASQIP data, c) compare the predictive capabilities of models incorporating new measures and the Surgical Apgar to the currently used VASQIP models, d) explore additional modeling and classification methods, e) assess whether these additional methods and measures significantly improve predictive performance.
The VASQIP data has been collecting comprehensive pre-operative and outcomes data on a substantial sample of surgeries in VA nationwide since 1994. An ongoing study (VA HSR&D IIR 05-229, PI Dr. Terri Monk) is collecting and standardizing AIMS data from multiple VA facilities and merging it with VASQIP data for approximately 30,000 surgeries. Novel summary measures of the AIMS data will be identified, focusing on physiologic dynamics and will be incorporated into standard predictive models. Additional prediction methods will be applied including classification and regression trees (CART), hybrid CART/logistic regression, random forests, support vector machines, and boosting. Last, Gawande's surgical Apgar will be implemented using the VASQIP data and the relative merits of all methods and predictors will be assessed using cross-validation.
Not yet available.
Improved risk modeling and prediction can ultimately result in further reducing the rates of post-operative morbidity and mortality within the VA, thereby positively impacting Veterans' health.
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DRA: Health Systems
DRE: Treatment - Comparative Effectiveness, Technology Development and Assessment
MeSH Terms: none