title: 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 progressive CKD has 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.
  
  
  METHODS
  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.
authors.affiliation: Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Guildford, Surrey, UK.
authors.forename: Norman
authors.initials: N
authors.lastname: Poh
authors.forename: Simon
authors.initials: S
authors.lastname: de Lusignan
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology; D015415:Biomarkers / Q000032:analysis; D001699:Biometry / Q000379:methods*; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007676:Kidney Failure, Chronic / Q000503:physiopathology / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D010865:Pilot Projects; D057285:Precision Medicine; D011320:Primary Health Care; D058996:Quality Improvement
chemical_list: D015415:Biomarkers