Data-modelling and visualisation in chronic kidney disease (CKD): a step towards personalised medicine

Abstract

Background Personalised medicine involves customising management to meet patients' needs. In chronic kidney disease (CKD) at the population level there is steady decline in renal function with increasing age; and progressiveCKDhas been defined as marked variation from this rate of decline. Objective To create visualisations of individual patient's renal function and display smoothed trend lines and confidence intervals for their renal function and other important co-variants. Method Applying advanced pattern recognition techniques developed in biometrics to routinely collected primary care data collected as part of the Quality Improvement in Chronic Kidney Disease (QICKD) trial. We plotted trend lines, using regression, and confidence intervals for individual patients.We also created a visualisation which allowed renal function to be compared with six other covariants: glycated haemoglobin (HbA1c), body mass index (BMI), BP, and therapy. The outputs were reviewed by an expert panel. Results We successfully extracted and displayed data. We demonstrated that estimated glomerular filtration (eGFR) is a noisy variable, and showed that a large number of people would exceed the 'progressive CKD' criteria. We created a data display that could be readily automated. This display was well received by our expert panel but requires extensive development before testing in a clinical setting. Conclusions It is feasible to utilise data visualisation methods developed in biometrics to look at CKD data. The criteria for defining 'progressive CKD' need revisiting, as many patients exceed them. Further development work and testing is needed to explore whether this type of data modelling and visualisation might improve patient care.

Keywords

• automatic data processing
• chronic disease
• diabetes mellitus
• longitudinal studies
• personalised medicine
• renal insufficiency