Approaches for the identification of chronic kidney disease in CPRD–HES-linked studies
Publication: Journal of Comparative Effectiveness Research
Abstract
Aim: There are different methods to identify chronic kidney disease (CKD) in Clinical Practice Research Datalink (CPRD)-Hospital Episode Statistics (HES). Methods: Using CPRD-HES, nonvalvular atrial fibrillation patients were classified according to CKD category. Results: Using glomerular filtration rate/estimated glomerular filtration rate tests only to identify patients with CKD resulted in 3.5% stage 2, 2.7% stage 3, 0.3% stage 4 and 0.03% stage 5. Using data from diagnostic codes to identify patients with CKD resulted in 1.4% stage 3, 0.4% stage 4 and 0.3% stage 5. Using test records and codes resulted in 3.5% stage 2, 4.0% stage 3, 0.6% stage 4 and 0.4% stage 5. Conclusion: To identify CKD status in CPRD-HES, a combination of test records and codes should be used. Using diagnostic codes only significantly underestimates CKD prevalence.
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References
Papers of special note have been highlighted as: •• of considerable interest
1.
Herbert A, Wijlaars L, Zylbersztejn A, Cromwell D, Hardelid P. Data resource profile: hospital episode statistics admitted patient care (HES APC). Int. J. Epidemiol. 46(4), 1093–1093i (2017).
•• Data resource profile.
2.
Herrett E, Gallagher AM, Bhaskaran K et al. Data resource profile: clinical practice research datalink (CPRD). Int. J. Epidemiol. 44(3), 827–836 (2015).
•• Data resource profile.
3.
Bentley T, Price C, Brown P. Structural and lexical features of successive versions of the Read Codes. Presented at: Proceedings of the Annual Conference of the Primary Health Care Specialist Group. Worcester, UK (1996).
4.
Mc Donald HI, Thomas SL, Millett ER, Nitsch D. CKD and the risk of acute, community-acquired infections among older people with diabetes mellitus: a retrospective cohort study using electronic health records. Am. J. Kidney Dis. 66(1), 60–68 (2015).
5.
Iwagami M, Mansfield KE, Hayes JF et al. Severe mental illness and chronic kidney disease: a cross-sectional study in the United Kingdom. Clin. Epidemiol. 10, 421–429 (2018).
6.
Hamada S, Gulliford MC. Multiple risk factor control, mortality and cardiovascular events in type 2 diabetes and chronic kidney disease: a population-based cohort study. BMJ Open 8(5), e019950 (2018).
7.
Watanabe H, Watanabe T, Sasaki S, Nagai K, Roden DM, Aizawa Y. Close bidirectional relationship between chronic kidney disease and atrial fibrillation: the Niigata preventive medicine study. Am. Heart J. 158(4), 629–636 (2009).
8.
Jain P, Calvert M, Cockwell P, McManus RJ. The need for improved identification and accurate classification of stages 3–5 chronic kidney disease in primary care: retrospective cohort study. PLoS ONE 9(8), e100831 (2014).
•• Compares chronic kidney disease (CKD) prevelence using glomerular filtration rate laboratory test results.
9.
Winkelmayer WC, Schneeweiss S, Mogun H, Patrick AR, Avorn J, Solomon DH. Identification of individuals with CKD from Medicare claims data: a validation study. Am. J. Kidney Dis. 46(2), 225–232 (2005).
10.
Ronksley PE, Tonelli M, Quan H et al. Validating a case definition for chronic kidney disease using administrative data. Nephrol. Dial. Transplant. 27(5), 1826–1831 (2011).
11.
Kiuchi MG. Atrial fibrillation and chronic kidney disease: a bad combination. Kidney Res. Clin. Pract. 37(2), 103 (2018).
12.
Walley T, Mantgani A. The UK general practice research database. Lancet 350(9084), 1097–1099 (1997).
13.
NHS Digital. Hospital Outpatient Activity, 2017–18. (2019). https://digital.nhs.uk/data-and-information/publications/statistical/hospital-outpatient-activity/2017-18
14.
Levey AS, Stevens LA, Schmid CH et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150(9), 604–612 (2009).
15.
Eknoyan G, Lameire N, Eckardt K et al. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. 3(1), 5–14 (2013).
16.
National Institute for Health Care Excellence. Chronic kidney disease in adults: assessment and management (2014). www.nice.org.uk/guidance/cg182
•• CKD identification and classification guidelines.
17.
Levin A, Stevens PE, Bilous RW et al. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. Suppl. 3(1), 1–150 (2013).
•• CKD identification and classification guidelines.
18.
McDonald HI, Shaw C, Thomas SL, Mansfield KE, Tomlinson LA, Nitsch D. Methodological challenges when carrying out research on CKD and AKI using routine electronic health records. Kidney Int. 90(5), 943–949 (2016).
19.
Poh N, Mcgovern AP, De Lusignan S. Improving the measurement of longitudinal change in renal function: automated detection of changes in laboratory creatinine assay. J. Innov. Health Inform. 22(2), 293–301 (2015).
20.
Denburg MR, Haynes K, Shults J, Lewis JD, Leonard MB. Validation of The Health Improvement Network (THIN) database for epidemiologic studies of chronic kidney disease. Pharmacoepidemiol. Drug Saf. 20(11), 1138–1149 (2011).
21.
Mathur R, Dreyer G, Yaqoob MM, Hull SA. Ethnic differences in the progression of chronic kidney disease and risk of death in a UK diabetic population: an observational cohort study. BMJ Open 8(3), e020145 (2018).
22.
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. 14(1), 81 (2013).
23.
So L, Evans D, Quan H. ICD-10 coding algorithms for defining comorbidities of acute myocardial infarction. BMC Health Serv. Res. 6(1), 161 (2006).
24.
Go AS, Fang MC, Udaltsova N et al. Impact of proteinuria and glomerular filtration rate on risk of thromboembolism in atrial fibrillation: the ATRIA study. Circulation 119(10), 1363 (2009).
25.
Kooiman J, Van De Peppel W, Van Der Meer F, Huisman M. Incidence of chronic kidney disease in patients with atrial fibrillation and its relevance for prescribing new oral antithrombotic drugs. J. Thromb. Haemost. 9(8), 1652–1653 (2011).
26.
Nelson SE, Shroff GR, Li S, Herzog CA. Impact of chronic kidney disease on risk of incident atrial fibrillation and subsequent survival in medicare patients. J. Am. Heart Assoc. 1(4), e002097 (2012).
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Pages: 441 - 446
PubMed: 32148084
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© 2020 Sreeram Ramagopalan et al. This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License
History
Received: 11 December 2019
Accepted: 20 February 2020
Published online: 9 March 2020
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Approaches for the identification of chronic kidney disease in CPRD–HES-linked studies. (2020) Journal of Comparative Effectiveness Research. DOI: 10.2217/cer-2019-0190
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