Can decision support systems work for acute kidney injury?

Nephrology Dialysis Transplantation

Volumn 30, Issue 11, 2015, Pages 1786-1789

  Kellum, John A ^a   Kane Gill, Sandra L ^a,b   Handler, Steven M ^a  

^a UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CREATININE;

ACUTE KIDNEY FAILURE; ARTICLE; CLINICAL DECISION MAKING; CREATININE BLOOD LEVEL; DECISION SUPPORT SYSTEM; HUMAN; PATIENT MONITORING; PRIORITY JOURNAL; RISK ASSESSMENT; URINE VOLUME;

EID: 84948438735     PISSN: 09310509     EISSN: 14602385     Source Type: Journal    
DOI: 10.1093/ndt/gfv285     Document Type: Article

References (30)

1. Kellum JA, Bellomo R, Ronco C. Kidney attack. JAMA 2012; 307: 2265-2266
2. Lameire NH, Bagga A, Cruz D et al. Acute kidney injury: an increasing global concern. Lancet 2013; 382: 170-179
3. USRDS. United States Renal Data Systems 2013 Annual Data Report. United States Renal Data Systems. http://www.usrds.org/2013/pdf/v1-ch6-13.pdf (7 January 2015, date last accessed)
4. Kane-Gill SL, Sileanu FE, Murugan R et al. Risk factors for acute kidney injury in older adults with critical illness: a retrospective cohort study. Am J Kidney Dis 2015; 65: 860-869
5. Kellum JA, Sileanu FE, Murugan R et al. Classifying AKI by urine output versus serum creatinine level. J Am Soc Nephrol 2015; 26: 2231-2238
6. Uchino S, Kellum JA, Bellomo R et al. Acute renal failure in critically ill patients: a multinational, multicenter study. JAMA 2005; 294: 813-818
7. Mehta RL, PascualMT, Soroko S et al. Spectrum of acute renal failure in the intensive care unit: the PICARD experience. Kidney Int 2004; 66: 1613-1621
8. Menon S, Kirkendall ES, Nguyen H et al. Acute kidney injury associated with high nephrotoxic medication exposure leads to chronic kidney disease after 6 months. J Pediatr 2014; 165: 522-527.e2
9. Murugan R, Karajala-Subramanyam V, Lee M et al. Acute kidney injury in non-severe pneumonia is associated with an increased immune response and lower survival. Kidney Int 2010; 77: 527-535
10. Sileanu FE, Murugan R, Lucko N et al. AKI in low-risk versus high-risk patients in intensive care. Clin J Am Soc Nephrol 2015; 10: 187-196
11. Hoste EAJ, Clermont G, Kersten A et al. RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis. Crit Care 2006; 10: R73
12. Palevsky PM, Liu KD, Brophy PD et al. KDOQI US commentary on the 2012 KDIGO clinical practice guideline for acute kidney injury. Am J Kidney Dis 2013; 61: 649-672
13. Fliser D, Laville M, Covic A et al. A European Renal Best Practice (ERBP) position statement on the Kidney Disease Improving Global Outcomes (KDIGO) Clinical Practice Guidelines on Acute Kidney Injury: Part 1: definitions, conservative management and contrast-induced nephropathy. Nephrol Dial Transplant 2012; 27: 4263-4272
14. KDIGO AKIWG. Kidney Disease: Improving Global Outcomes (KDIGO) clinical practice guideline for acute kidney injury. Kidney Inter Suppl 2012; 2: 1-138. 1-141
15. Porter CJ, Juurlink I, Bisset LH et al. A real-time electronic alert to improve detection of acute kidney injury in a large teaching hospital. Nephrol Dial Transplant 2014; 29: 1888-1893
16. Selby NM, Crowley L, Fluck RJ et al. Use of electronic results reporting to diagnose and monitor AKI in hospitalized patients. Clin J Am Soc Nephrol 2012; 7: 533-540
17. Sawhney S, Fluck N, Marks A et al. Acute kidney injury - how does automated detection perform? Nephrol Dial Transplant 2015; 30: 1853-1861
18. Colpaert K, Hoste EA, Steurbaut K et al. Impact of real-time electronic alerting of acute kidney injury on therapeutic intervention and progression of RIFLE class. Crit Care Med 2012; 40: 1164-1170
19. Wilson FP, Shashaty M, Testani J et al. Automated, electronic alerts for acute kidney injury: a single-blind, parallel-group, randomised controlled trial. Lancet 2015; 385: 1966-1974
20. McCoy AB, Cox ZL, Neal EB et al. Real-time pharmacy surveillance and clinical decision support to reduce adverse drug events in acute kidney injury: a randomized, controlled trial. Appl Clin Inform 2012; 3: 221-238
21. Kellum JA, Devarajan P. What can we expect from biomarkers for acute kidney injury? Biomark Med 2014; 8: 1239-1245
22. Roshanov PS, Fernandes N,Wilczynski JM et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ 2013; 346: f657
23. ProCESS Investigators Yealy DM, Kellum JA et al. A randomized trial of protocol-based care for early septic shock. N Engl J Med 2014; 370: 1683-1693
24. Solomon R, Werner C, Mann D et al. Effects of saline, mannitol, and furosemide to prevent acute decreases in renal function induced by radiocontrast agents. N Engl J Med 1994; 331: 1416-1420
25. Kellum JA, Decker JM. Use of dopamine in acute renal failure: a meta-analysis. Crit Care Med 2001; 29: 1526-1531
26. Bove T, Zangrillo A, Guarracino F et al. Effect of fenoldopam on use of renal replacement therapy among patients with acute kidney injury after cardiac surgery: a randomized clinical trial. JAMA 2014; 312: 2244-2253
27. Winkelmayer WC, Finkel KW. Prevention of acute kidney injury using vasoactive or antiplatelet treatment: three strikes and out? JAMA 2014; 312: 2221-2222
28. Rind DM, Safran C, Phillips RS et al. Effect of computer-based alerts on the treatment and outcomes of hospitalized patients. Arch Intern Med 1994; 154: 1511-1517
29. Handler SM, Altman RL, Perera S et al. A systematic review of the performance characteristics of clinical event monitor signals used to detect adverse drug events in the hospital setting. J Am Med Inform Assoc 2007; 14: 451-458
30. Závada J, Hoste E, Cartin-Ceba R et al. A comparison of three methods to estimate baseline creatinine for RIFLE classification. Nephrol Dial Transplant 2010; 25: 3911-3918