Is routine hospital episode data sufficient for identifying individuals with chronic kidney disease? A comparison study with laboratory data

Health Informatics Journal

Volumn 22, Issue 2, 2016, Pages 383-396

  Robertson, Lynn M ^a   Denadai, Lucas ^a   Black, Corri ^a,b   Fluck, Nicholas ^b   Prescott, Gordon ^a   Simpson, William ^b   Wilde, Katie ^a   Marks, Angharad ^a,b  

^a UNIVERSITY OF ABERDEEN   (United Kingdom)

^b NHS GRAMPIAN   (United Kingdom)

Author keywords

databases and data mining; ehealth; electronic health records; record linkage; secondary care

Indexed keywords

ADOLESCENT; ADULT; AGED; CHRONIC KIDNEY FAILURE; COMPARATIVE STUDY; DATA MINING; ELECTRONIC HEALTH RECORD; FEMALE; GLOMERULUS FILTRATION RATE; HOSPITAL; HUMAN; LABORATORY INFORMATION SYSTEM; MALE; MEDICAL RECORD; MIDDLE AGED; PROCEDURES; STATISTICS AND NUMERICAL DATA; UTILIZATION; VERY ELDERLY;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; CLINICAL LABORATORY INFORMATION SYSTEMS; DATA MINING; ELECTRONIC HEALTH RECORDS; FEMALE; GLOMERULAR FILTRATION RATE; HOSPITALS; HUMANS; MALE; MEDICAL RECORD LINKAGE; MIDDLE AGED; RENAL INSUFFICIENCY, CHRONIC;

EID: 84971324433     PISSN: 14604582     EISSN: 17412811     Source Type: Journal    
DOI: 10.1177/1460458214562286     Document Type: Article

References (33)

1. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002; 39 (Suppl. 1): S1-S266.
2. Kerr M, Bray B, Medcalf J, et al. Estimating the financial cost of chronic kidney disease to the NHS in England. Nephrol Dial Transplant 2012; 27 (Suppl. 3): iii73-iii80.
3. Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA 2007; 298: 2038-2047.
4. McCullough K, Sharma P, Ali T, et al. Measuring the population burden of chronic kidney disease: a systematic literature review of the estimated prevalence of impaired kidney function. Nephrol Dial Transplant 2012; 27: 1812-1821.
5. Walker N, Bankart J, Brunskill N, et al. Which factors are associated with higher rates of chronic kidney disease recording in primary care? A cross-sectional survey of GP practices. Br J Gen Pract 2011; 61: 203-205.
6. Leal JR, Laupland KB,. Validity of ascertainment of co-morbid illness using administrative databases: a systematic review. Clin Microbiol Infect 2010; 16: 715-721.
7. Hudson M, Avina-Zubieta A, Lacaille D, et al. The validity of administrative data to identify hip fractures is high-a systematic review. J Clin Epidemiol 2013; 66: 278-285.
8. Department of Health, NHS Improvement & Efficiency Directorate, Innovation and Service Improvement. Innovation health and wealth: accelerating adoption and diffusion in the NHS., http://webarchive.nationalarchives.gov.uk/20130107105354/http://www.dh.gov.uk/prod-consum-dh/groups/dh-digitalassets/documents/digitalasset/dh-134597.pdf (2011,. accessed September 2014).
9. Medical Research Council. Funding opportunities-E-Health Informatics Research Centres (EHIRCs) call., http://www.mrc.ac.uk/Fundingopportunities/Calls/E-healthCentresCall/index.htm (2011,. accessed May 2013).
10. Medical Research Council. Strategic framework for health informatics in support of research., http://webarchive.nationalarchives.gov.uk/20130502104716/http://mrc.ac.uk/Utilities/Documentrecord/index.htm?d=MRC006669 (. accessed December 2014).
11. Medical Research Council. UK e-health records research capacity and capability., http://webarchive.nationalarchives.gov.uk/20130502104716/http://mrc.ac.uk/Utilities/Documentrecord/index.htm?d=MRC007896 (. accessed December 2014).
12. The White House, Office of Science and Technology Policy. Big data is a big deal., http://www.whitehouse.gov/blog/2012/03/29/big-data-big-deal (2012,. accessed September 2014).
13. Black C, Sharma P, Scotland G, et al. Early referral strategies for management of people with markers of renal disease: a systematic review of the evidence of clinical effectiveness, cost-effectiveness and economic analysis. Health Technol Assess 2010; 14 (21): 1-184.
14. Grams ME, Plantinga LC, Hedgeman E, et al. Validation of CKD and related conditions in existing data sets: a systematic review. Am J Kidney Dis 2011; 57: 44-54.
15. Vlasschaert ME, Bejaimal SA, Hackam DG, et al. Validity of administrative database coding for kidney disease: a systematic review. Am J Kidney Dis 2011; 57: 29-43.
16. Chase HS, Radhakrishnan J, Shirazian S, et al. Under-documentation of chronic kidney disease in the electronic health record in outpatients. J Am Med Inform Assoc 2010; 17: 588-594.
17. Denburg MR, Haynes K, Shults J, et al. Validation of The Health Improvement Network (THIN) database for epidemiologic studies of chronic kidney disease. Pharmacoepidemiol Drug Saf 2011; 20: 1138-1149.
18. Ronksley PE, Tonelli M, Quan H, et al. Validating a case definition for chronic kidney disease using administrative data. Nephrol Dial Transplant 2012; 27: 1826-1831.
19. Fleet JL, Dixon SN, Shariff SZ, et al. Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes. BMC Nephrol 2013; 14: 81.
20. Kern EFO, Maney M, Miller DR, et al. Failure of ICD-9-CM codes to identify patients with comorbid chronic kidney disease in diabetes. Health Serv Res 2006; 41: 564-580.
21. Ferris M, Shoham DA, Pierre-Louis M, et al. High prevalence of unlabeled chronic kidney disease among inpatients at a tertiary-care hospital. Am J Med Sci 2009; 337: 93-97.
22. Information and Statistics Division. NHS Scotland data quality assurance report on acute inpatient/day case data 2000-2002. Edinburgh: NHS Scotland, http://www.isdscotlandarchive.scot.nhs.uk/isd/files//SMR01%20National%20Report.pdf (. accessed December 2014).
23. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005; 43: 1130-1139.
24. University of Aberdeen. Grampian Data Safe Haven., http://www.abdn.ac.uk/iahs/facilities/grampian-data-safe-haven.php (2013,. accessed September 2014).
25. Charlson M, Pompei P, Ales K, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40: 373-383.
26. Landis JR, Koch GG,. The measurement of observer agreement for categorical data. Biometrics 1977; 33: 159-174.
27. Petrie A, Sabin C,. Medical Statistics at a Glance. Oxford, England: Blackwell Science Ltd., 2000.
28. StataCorp LP,. Stata statistical software: release 13. College Station, TX: StataCorp LP, 2013.
29. National Records of Scotland. Revised mid-2003 population estimates, council and health board areas., http://www.gro-scotland.gov.uk/statistics/theme/population/estimates/mid-year/archive/2003/index.html (2011,. accessed October 2013).
30. James MT, Quan H, Tonelli M, et al. CKD and risk of hospitalization and death with pneumonia. Am J Kidney Dis 2009; 54: 24-32.
31. Soo M, Robertson LM, Ali T, et al. Approaches to ascertaining comorbidity information: validation of routine hospital episode data with clinician-based case note review. BMC Res Notes 2014; 7: 253.
32. White SL, Polkinghorne KR, Atkins RC, et al. Comparison of the prevalence and mortality risk of CKD in Australia using the CKD Epidemiology Collaboration (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) Study GFR estimating equations: the AusDiab (Australian Diabetes, Obesity and Lifestyle) Study. Am J Kidney Dis 2010; 55: 660-670.
33. Marks A, Black C, Fluck N, et al. Translating chronic kidney disease epidemiology into patient care-the individual/public health risk paradox. Nephrol Dial Transplant 2012; 27 (Suppl. 3): iii65-iii72.