A novel tool for visualizing chronic kidney disease associated polymorbidity: a 13-year cohort study in Taiwan

OBJECTIVE The aim of this study is to analyze and visualize the polymorbidity associated with chronic kidney disease (CKD). The study shows diseases associated with CKD before and after CKD diagnosis in a time-evolutionary type visualization. MATERIALS AND METHODS Our sample data came from a population of one million individuals randomly selected from the Taiwan National Health Insurance Database, 1998 to 2011. From this group, those patients diagnosed with CKD were included in the analysis. We selected 11 of the most common diseases associated with CKD before its diagnosis and followed them until their death or up to 2011. We used a Sankey-style diagram, which quantifies and visualizes the transition between pre- and post-CKD states with various lines and widths. The line represents groups and the width of a line represents the number of patients transferred from one state to another. RESULTS The patients were grouped according to their states: that is, diagnoses, hemodialysis/transplantation procedures, and events such as death. A Sankey diagram with basic zooming and planning functions was developed that temporally and qualitatively depicts they had amid change of comorbidities occurred in pre- and post-CKD states. DISCUSSION This represents a novel visualization approach for temporal patterns of polymorbidities associated with any complex disease and its outcomes. The Sankey diagram is a promising method for visualizing complex diseases and exploring the effect of comorbidities on outcomes in a time-evolution style. CONCLUSIONS This type of visualization may help clinicians foresee possible outcomes of complex diseases by considering comorbidities that the patients have developed.

[1]  Chun-Yuh Yang,et al.  Epidemiological features of CKD in Taiwan. , 2007, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[2]  Athanasios V. Vasilakos,et al.  Big data: From beginning to future , 2016, Int. J. Inf. Manag..

[3]  Maximilian Röglinger,et al.  Big Data , 2013, Wirtschaftsinf..

[4]  David Gotz,et al.  Exploring Flow, Factors, and Outcomes of Temporal Event Sequences with the Outflow Visualization , 2012, IEEE Transactions on Visualization and Computer Graphics.

[5]  C. Schmid,et al.  A new equation to estimate glomerular filtration rate. , 2009, Annals of internal medicine.

[6]  M. Williams,et al.  Automated estimation of disease recurrence in head and neck cancer using routine healthcare data , 2014, Comput. Methods Programs Biomed..

[7]  G. Remuzzi,et al.  The contribution of chronic kidney disease to the global burden of major noncommunicable diseases. , 2011, Kidney international.

[8]  Cockcroft Dw,et al.  Prediction of Creatinine Clearance from Serum Creatinine , 1976 .

[9]  G. Eknoyan,et al.  Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). , 2005, Kidney international.

[10]  Kai-Che Liu,et al.  Stereoscopic visualization of laparoscope image using depth information from 3D model , 2014, Comput. Methods Programs Biomed..

[11]  Han-Cheng Wang,et al.  Developing a data mining approach to investigate association between physician prescription and patient outcome - A study on re-hospitalization in Stevens-Johnson Syndrome , 2013, Comput. Methods Programs Biomed..

[12]  Alex A. T. Bui,et al.  Leveraging Domain Knowledge to Facilitate Visual Exploration of Large Population Datasets , 2013, AMIA.

[13]  Hui Ting Chan,et al.  All-cause mortality attributable to chronic kidney disease: a prospective cohort study based on 462 293 adults in Taiwan , 2008, The Lancet.

[14]  Jane Murray Cramm,et al.  High-quality chronic care delivery improves experiences of chronically ill patients receiving care , 2013, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[15]  Helwig Hauser,et al.  Parallel Sets: interactive exploration and visual analysis of categorical data , 2006, IEEE Transactions on Visualization and Computer Graphics.

[16]  Jer-Chia Tsai,et al.  Epidemiology, impact and preventive care of chronic kidney disease in Taiwan , 2010, Nephrology.

[17]  E. Krishnan,et al.  Big Data and Clinicians: A Review on the State of the Science , 2014, JMIR medical informatics.

[18]  Krist Wongsuphasawat,et al.  Outflow : Visualizing Patient Flow by Symptoms and Outcome , 2011 .

[19]  Heidrun Schumann,et al.  Model-Driven Design for the Visual Analysis of Heterogeneous Data , 2012, IEEE Transactions on Visualization and Computer Graphics.

[20]  S. Brunak,et al.  Mining electronic health records: towards better research applications and clinical care , 2012, Nature Reviews Genetics.

[21]  Jeremiah Scholl,et al.  Empowering village doctors and enhancing rural healthcare using cloud computing in a rural area of mainland China , 2014, Comput. Methods Programs Biomed..

[22]  Raymond Vanholder,et al.  The burden of kidney disease: improving global outcomes. , 2004, Kidney international.

[23]  Shabbir Syed-Abdul,et al.  Disease universe: Visualisation of population-wide disease-wide associations , 2013, 1308.2557.

[24]  Phung Anh Nguyen,et al.  Influenza vaccination and reduction in risk of ischemic heart disease among chronic obstructive pulmonary elderly , 2013, Comput. Methods Programs Biomed..

[25]  Thomas Wetter,et al.  A web-based system for clinical decision support and knowledge maintenance for deterioration monitoring of hemato-oncological patients , 2013, Comput. Methods Programs Biomed..

[26]  Y. Hassan,et al.  Drug use and dosing in chronic kidney disease. , 2009, Annals of the Academy of Medicine, Singapore.

[27]  Mario Schmidt,et al.  The Sankey Diagram in Energy and Material Flow Management , 2008 .

[28]  George Hripcsak,et al.  Next-generation phenotyping of electronic health records , 2012, J. Am. Medical Informatics Assoc..

[29]  Harold I Feldman,et al.  Estimating glomerular filtration rate from serum creatinine and cystatin C. , 2012, The New England journal of medicine.

[30]  W. Kannel,et al.  The natural history of congestive heart failure: the Framingham study. , 1971, The New England journal of medicine.

[31]  C Wagner,et al.  Can preventable adverse events be predicted among hospitalized older patients? The development and validation of a predictive model. , 2014, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[32]  Yulia R. Gel,et al.  A new surveillance and spatio-temporal visualization tool SIMID: SIMulation of Infectious Diseases using random networks and GIS , 2013, Comput. Methods Programs Biomed..

[33]  E. Roughead,et al.  Prevalence of comorbidity of chronic diseases in Australia , 2008, BMC public health.

[34]  V. Jha,et al.  The impact of CKD identification in large countries: the burden of illness. , 2012, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[35]  Heejung Bang,et al.  SCreening for Occult REnal Disease (SCORED): a simple prediction model for chronic kidney disease. , 2007, Archives of internal medicine.

[36]  Phung Anh Nguyen,et al.  The relationship between usage intention and adoption of electronic health records at primary care clinics , 2013, Comput. Methods Programs Biomed..

[37]  P. Riehmann,et al.  Interactive Sankey diagrams , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[38]  Helmut Krcmar,et al.  Big Data , 2014, Wirtschaftsinf..

[39]  L. Hood,et al.  Predictive, personalized, preventive, participatory (P4) cancer medicine , 2011, Nature Reviews Clinical Oncology.

[40]  Keith C. Norris,et al.  CKD and cardiovascular disease in screened high-risk volunteer and general populations: the Kidney Early Evaluation Program (KEEP) and National Health and Nutrition Examination Survey (NHANES) 1999-2004. , 2008, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[41]  D. Rothenbacher,et al.  Prevalence of chronic kidney disease in population-based studies: Systematic review , 2008, BMC public health.