Risk prediction in carotid stenosis with time dependency of high-impact variables and multiple endpoint analysis

<h2>Abstract</h2><h3>Background</h3> Electronic patient records represent a potential data source for the future, as digital free text extraction by artificial intelligence is coming closer to safe options for handling of confidential information. Big data from patient records could, in turn, be applied to improve risk prediction for several disease groups. In this project, a pilot dataset based on information from patient records was collected to explore the suitability of free text information in risk prediction for patients with carotid stenosis. <h3>Methods</h3> A total of 863 patients with the International Classification of Diseases, 10th revision (ICD-10) diagnosis of carotid stenosis were included, and the risk of stroke was analyzed in relation to treatment and variables previously shown to impact disease progression or stroke risk. Variables previously shown to have a strong impact on this risk were included with time dependency, to allow analysis of trends in risk in relation to changes in risk profile over time. Multiple endpoint analysis was performed to explore the excess risk in patients who suffered more than one event. Results from the suggested model were compared with results from traditional Cox regression, and concordance analysis was performed to compare these two models. <h3>Results</h3> In analysis of the pilot dataset, the expanded model with time dependency and multiple endpoint analysis appears to provide a better model fit then a traditional Cox regression analysis performed on the same dataset. <h3>Conclusions</h3> Risk prediction in patients with carotid stenosis seems to be improved by a model with time-dependent variables and multiple event analysis.