Visualising disease trajectories from population-wide data

The rise of precision medicine as both a research discipline and clinical practice looks to improve clinical experience by using the wealth of health-associated data to tailor treatments towards the individual patient. Such data includes electronic health records alongside omics data that are increasingly collected in healthcare settings including genetic data, proteomics, metabolomics and more. Digitalisation of electronic healthcare has led to a wealth of data describing individuals’ health and disease status. Taken together, such data that describes individuals molecular biology and pathology is being used to expand our understanding and utility of personalised medicine. Using electronic healthcare records and disease histories for either explorative or predictive purposes can help identify risk factors and stratify patients by disease risk which can ultimately inform screening protocols. In the recent decade, studies have been utilising the concept of disease trajectories in classical statistical approaches to explore risk factors and complications, deep learning algorithms for disease onset prediction or patient stratification, amongst others (Jensen et al., 2017; Shickel et al., 2018; Hu et al., 2019; Lademann et al., 2019; Nielsen et al., 2019; Thorsen-Meyer et al., 2020; Placido et al., 2022). Many studies have previously analysed diseases in an either monoor bidirectional manner. Today, trajectory analyses and visualisations can utilise temporal information in expanding large health data sets allowing for consideration of comorbidities for different patients. Comorbidity and multimorbidity refer to the presence of more than one disease in a single patient and has been increasingly recognised as a crucial consideration when diagnosing and treating patients (Hu et al., 2016). Visualisation can be a powerful tool for understanding all steps in an analysis using large data sets across a temporal axis. Denmark is one of the leading countries in collecting decades of longitudinal population-wide health data. Denmark has a wealth of health registries for which patients can be linked on an individual level through the unique Central Person Register (CPR) identifier. One of the largest and most comprehensive national registries is the Danish National Patient Registry (NPR), which covers around 8.2 million Danish patients over nearly 45 years. The visualisation of this type of large health dataset can be a highly complex matter. Here, we will use pancreatic cancer as an example to visualise temporal disease patterns in the NPR, giving examples of different types of plots and tools useful for the overviewing and analysing large longitudinal health data. Pancreatic cancer is one of the most lethal cancer types with a 5-year survival rate at only 8% (American Cancer Society, 2020) and has been estimated to become the second leading cause of cancer in 203010. Due to the lack of clear symptoms, this cancer type is often diagnosed at a later stage resulting in poor outcomes. Hence, the need for detecting early symptoms and risk factors is crucial. We will highlight some of the key forms of data visualisation methods utilised OPEN ACCESS