Predicting the risk of hyperkalemia in patients with chronic kidney disease starting lisinopril

Pharmacoepidemiology and Drug Safety

Volumn 19, Issue 3, 2010, Pages 266-272

  Johnson, Eric S ^a   Weinstein, Jessica R ^b   Thorp, Micah L ^c   Platt, Robert W ^d   Petrik, Amanda F ^a   Yang, Xiuhai ^a   Anderson, Sharon ^b,e   Smith, David H ^a  

^a CENTER FOR HEALTH RESEARCH   (United States)

^b OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^c McGill University ^*  (United States)

^d MCGILL UNIVERSITY   (Canada)

^e PORTLAND VA MEDICAL CENTER   (United States)

Author keywords

ACE inhibitors; Adverse effects; Chronic kidney disease; Cohort study; Hyperkalemia; Risk score

Indexed keywords

AMILORIDE; BUMETANIDE; CHLORTALIDONE; EPLERENONE; FUROSEMIDE; HYDROCHLOROTHIAZIDE; LISINOPRIL; METOLAZONE; POTASSIUM; SPIRONOLACTONE; TRIAMTERENE;

ADULT; AGED; ARTICLE; CHRONIC KIDNEY DISEASE; CONTROLLED STUDY; CREATININE BLOOD LEVEL; DIABETES MELLITUS; DRUG MEGADOSE; ELECTRONIC MEDICAL RECORD; FEMALE; FOLLOW UP; GLOMERULUS FILTRATION RATE; HEART FAILURE; HIGH RISK PATIENT; HUMAN; HYPERKALEMIA; MAJOR CLINICAL STUDY; MALE; OUTCOME ASSESSMENT; PATIENT SELECTION; POTASSIUM BLOOD LEVEL; PREDICTOR VARIABLE; PRIORITY JOURNAL; PROGNOSIS; PROPORTIONAL HAZARDS MODEL; RETROSPECTIVE STUDY; RISK ASSESSMENT;

ADULT; AGED; AGED, 80 AND OVER; ANGIOTENSIN-CONVERTING ENZYME INHIBITORS; CHRONIC DISEASE; COHORT STUDIES; FEMALE; FOLLOW-UP STUDIES; GLOMERULAR FILTRATION RATE; HUMANS; HYPERKALEMIA; KIDNEY DISEASES; LISINOPRIL; MALE; MIDDLE AGED; PROPORTIONAL HAZARDS MODELS; RETROSPECTIVE STUDIES; RISK; RISK FACTORS;

EID: 77649217394     PISSN: 10538569     EISSN: 10991557     Source Type: Journal    
DOI: 10.1002/pds.1923     Document Type: Article

References (21)

1. Crowe E, Halpin D, Stevens P. Early identification and management of chronic kidney disease: summary of the NICE guidance. Br Med J 2008; 337: a1530. doi:10.1136/bmj.a1530.
2. Kent DM, Jafar TH, Hayward RA, et al. Progression risk, urinary protein excretion, and treatment effects of angiotensin-converting enzyme inhibitors in nondiabetic kidney disease. J Am Soc Nephrol 2007; 18: 1959-1965.
3. Palmer BF. Managing hyperkalemia caused by inhibitors of the reninangiotensin-aldosterone system. N Engl J Med 2004; 351: 585-592.
4. Hebert LA. Optimizing ACE-inhibitor therapy for chronic kidney disease. N Engl J Med 2006; 354: 189-191.
5. Parfrey PS. Inhibitors of the renin-angiotensin system: proven benefits, unproven safety. Ann Intern Med 2008; 148: 76-77.
6. Weinberg JM, Appel LJ, Bakris G, et al. Risk of hyperkalemia in nondiabetic patients with chronic kidney disease receiving antihypertensive therapy. Arch Intern Med 2009; 169: 1587-1594.
7. Moons KGM, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how? Br Med J 2009; 338: b375.
8. Morimoto T, Gandhi TK, Fiskio JM, et al. Development and validation of a clinical prediction rule for angiotensin-converting enzyme inhibitor-induced cough. J Gen Intern Med 2004; 19: 684-691.
9. Thorpe KE, Zwarenstein M, Oxman AD, et al. A pragmatic - explanatory continuum indicator summary (PRECIS): a tool to help trial designers. J Clin Epidemiol 2009; 62: 464-475.
10. Levey AS, Coresh J, Balk E, et al. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med 2003; 139: 137-147.
11. Smith LR, Harrell FE, Muhlbaier LH. Problems and potentials in modeling survival. In Medical Effectiveness Research Data Methods (Summary Report). Grady ML, Schwartz HA (eds). AHCPR Pub No 92-0056. US Department of Health and Human Services, Agency for Health Care Policy and Research: Rockville, MD, 1992; 151-159.
12. Harrell FE. General aspects of fitting regression models. In Regression Modeling Strategies. Springer: New York, 2001; 11-37.
13. Sullivan LM, Massaro JM, D'Agostino RB Sr. Presentation of multivariable data for clinical use: the Framingham Study risk score functions. Statist Med 2004; 23: 1631-1660.
14. Royston P, Moons KGM, Altman DG, Vergouwe Y. Prognosis and prognostic research: developing a prognostic model. Br Med J 2009; 338: b604.
15. Pepe MS, Feng Z, Huang Y, Longton G, et al. Integrating the predictiveness of a marker with its performance as a classifier. Am J Epidemiol 2008; 167: 362-368.
16. Maddirala S, Khan A, Vincent A, Lau K. Effect of angiotensin converting enzyme inhibitors and angiotensin receptor blockers on serum potassium levels and renal function in ambulatory outpatients: risk factor analysis. Am J Med Sci 2008; 336: 330-335.
17. Moons KG, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. Br Med J 2009; 338: b606.
18. Kunz R, Friedrich C, Wolbers M, Mann JFE. Meta-analysis: effect of monotherapy and combination therapy with inhibitors of the reninangiotensin system on proteinuria in renal disease. Ann Intern Med 2008; 148: 30-48.
19. Einhorn LM, Zhan M, Hsu VD, et al. The frequency of hyperkalemia and its significance in chronic kidney disease. Arch Intern Med 2009; 169: 1156-1162.
20. Altman DG, Royston P. What do we mean by validating a prognostic model? Statist Med 2000; 19: 453-473.
21. Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. Br Med J 2009; 338: b605.