Statistical primer: using prognostic models to predict the future: what cardiothoracic surgery can learn from Strictly Come Dancing

Abstract

OBJECTIVES: Prognostic models are widely used across medicine and within cardiothoracic surgery, where predictive tools such as EuroSCORE are commonplace. Such models are a useful component of clinical assessment but may be misapplied. In this article, we demonstrate some of the major issues with risk scores by using the popular BBC television programme Strictly Come Dancing (known as Dancing with the Stars in many other countries) as an example. METHODS: We generated a multivariable prognostic model using data from the then-completed 19 series of Strictly Come Dancing to predict prospectively the results of the 20th series. RESULTS: The initial model based solely on demographic data was limited in its predictive value (0.25, 0.22; R2 and Spearman's rank correlation, respectively) but was substantially improved following the introduction of early judges' scores deemed representative of whether contestants could actually dance (0.40, 0.30). We then utilize our model to discuss the difficulties and pitfalls in using and interpreting prognostic models in cardiothoracic surgery and beyond, particularly where these do not adequately capture potentially important prognostic information. CONCLUSION: Researchers and clinicians alike should use prognostic models cautiously and not extrapolate conclusions from demographic data alone.