Predicting Surgical Risk: How Much Data is Enough?

As medicine becomes increasingly data driven, caregivers are required to collect and analyze an increasingly copious volume of patient data. Although methods for studying these data have recently evolved, the collection of clinically validated data remains cumbersome. We explored how to reduce the amount of data needed to risk stratify patients. We focused our investigation on patient data from the National Surgical Quality Improvement Program (NSQIP) to study how the accuracy of predictive models may be affected by changing the number of variables, the categories of variables, and the times at which these variables were collected. By examining the implications of creating predictive models based on the entire variable set in NSQIP and smaller selected variable groups, our results show that using far fewer variables than traditionally done can lead to similar predictive accuracy.