National Veterans Health Administration inpatient risk stratification models for hospital-acquired acute kidney injury

Journal of the American Medical Informatics Association

Volumn 22, Issue 5, 2015, Pages 1054-1071

  Cronin, Robert M ^a,b,c   VanHouten, Jacob P ^b,c   Siew, Edward D ^b   Eden, Svetlana K ^c   Fihn, Stephan D ^a,d   Nielson, Christopher D ^a,e   Peterson, Josh F ^b   Baker, Clifton R ^a   Alp Ikizler, T ^b   Speroff, Theodore ^a,c   Matheny, Michael E ^a,b,c  

^a VETERANS AFFAIRS MEDICAL CENTER   (United States)

^b VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

^c VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE   (United States)

^e UNIVERSITY OF NEVADA   (United States)

Author keywords

Acute kidney injury; Logistic regression; Random forest; Risk models

Indexed keywords

ANTIBIOTIC AGENT; BENZODIAZEPINE DERIVATIVE; BICARBONATE; BILIRUBIN; CALCIUM; CHLORIDE; CREATININE; INFUSION FLUID; NONSTEROID ANTIINFLAMMATORY AGENT; RINGER LACTATE SOLUTION; SODIUM; SODIUM CHLORIDE; VANCOMYCIN;

ACUTE KIDNEY FAILURE; ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; BICARBONATE BLOOD LEVEL; BILIRUBIN BLOOD LEVEL; CALCIUM BLOOD LEVEL; CHLORIDE BLOOD LEVEL; COHORT ANALYSIS; COMPARATIVE STUDY; CREATININE BLOOD LEVEL; ELECTRONIC MEDICAL RECORD; FEMALE; GLOMERULUS FILTRATION RATE; HEMODIALYSIS; HIGH RISK PATIENT; HOSPITAL ACQUIRED ACUTE KIDNEY INJURY; HOSPITAL ADMISSION; HOSPITAL PATIENT; HOSPITALIZATION; HUMAN; LENGTH OF STAY; MAJOR CLINICAL STUDY; MALE; MULTICENTER STUDY; OUTCOME ASSESSMENT; PATIENT IDENTIFICATION; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; RISK ASSESSMENT; RISK FACTOR; SODIUM BLOOD LEVEL; UREA NITROGEN BLOOD LEVEL; VETERANS HEALTH; GOVERNMENT; IATROGENIC DISEASE; MIDDLE AGED; PROGNOSIS; PUBLIC HOSPITAL; RISK; STATISTICAL MODEL; UNITED STATES;

ACUTE KIDNEY INJURY; AGED; FEMALE; HOSPITALIZATION; HOSPITALS, VETERANS; HUMANS; IATROGENIC DISEASE; LOGISTIC MODELS; MALE; MIDDLE AGED; MODELS, STATISTICAL; PROGNOSIS; RETROSPECTIVE STUDIES; RISK; ROC CURVE; UNITED STATES; UNITED STATES DEPARTMENT OF VETERANS AFFAIRS;

EID: 84947063569     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1093/jamia/ocv051     Document Type: Article

References (76)

1. Bellomo R, Ronco C, Kellum JA, et al. Acute renal failure-definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit Care Lond Engl. 2004;8:R204-R212.
2. Uchino S, Kellum JA, Bellomo R, et al. Acute renal failure in critically ill patients: a multinational, multicenter study. JAMA J Am Med Assoc. 2005; 294:813-818.
3. Hou SH, Bushinsky DA, Wish JB, et al. Hospital-acquired renal insufficiency: a prospective study. Am J Med. 1983;74:243-248.
4. Liaño F, Junco E, Pascual J, et al. The spectrum of acute renal failure in the intensive care unit compared with that seen in other settings. The Madrid Acute Renal Failure Study Group. Kidney Int Suppl. 1998;66:S16-S24.
5. Wang Y, Cui Z, Fan M. Hospital-acquired and community-acquired acute renal failure in hospitalized Chinese: a ten-year review. Ren Fail. 2007;29: 163-168.
6. Samaan KH, Dahlke M, Stover J. Addressing safety concerns about U-500 insulin in a hospital setting. Am J Health-Syst Pharm AJHP OffJ Am Soc Health-Syst Pharm. 2011;68:63-68.
7. Brivet FG, Kleinknecht DJ, Loirat P, et al. Acute renal failure in intensive care units-causes, outcome, and prognostic factors of hospital mortality; a prospective, multicenter study. French Study Group on Acute Renal Failure. Crit Care Med. 1996;24:192-198.
8. Coca SG, Yusuf B, Shlipak MG, et al. Long-term risk of mortality and other adverse outcomes after acute kidney injury: a systematic review and metaanalysis. Am J Kidney Dis OffJ Natl Kidney Found. 2009;53:961-973.
9. Mehta RL, Pascual MT, Soroko S, et al. Spectrum of acute renal failure in the intensive care unit: the PICARD experience. Kidney Int. 2004;66: 1613-1621.
10. Bernieh B, Al Hakim M, Boobes Y, et al. Outcome and predictive factors of acute renal failure in the intensive care unit. Transplant Proc. 2004;36: 1784-1787.
11. Prins JM, Büller HR, Kuijper EJ, et al. Once versus thrice daily gentamicin in patients with serious infections. Lancet. 1993;341:335-339.
12. Hatala R, Dinh TT, Cook DJ. Single daily dosing of aminoglycosides in immunocompromised adults: a systematic review. Clin Infect Dis OffPubl Infect Dis Soc Am. 1997;24:810-815.
13. Hock R, Anderson RJ. Prevention of drug-induced nephrotoxicity in the intensive care unit. J Crit Care. 1995;10:33-43.
14. Tran DD, Oe PL, de Fijter CW, et al. Acute renal failure in patients with acute pancreatitis: prevalence, risk factors, and outcome. Nephrol Dial Transplant OffPubl Eur Dial Transpl Assoc-Eur Ren Assoc. 1993;8:1079-1084.
15. Taber SS, Mueller BA. Drug-associated renal dysfunction. Crit Care Clin. 2006;22:357-374, viii.
16. Walsh TJ, Hiemenz JW, Seibel NL, et al. Amphotericin B lipid complex for invasive fungal infections: analysis of safety and efficacy in 556 cases. Clin Infect Dis OffPubl Infect Dis Soc Am. 1998;26:1383-1396.
17. Bamgboye EL, Mabayoje MO, Odutola TA, et al. Acute renal failure at the Lagos University Teaching Hospital: a 10-year review. Ren Fail. 1993;15: 77-80.
18. Nolan CR, Anderson RJ. Hospital-acquired acute renal failure. J Am Soc Nephrol JASN. 1998;9:710-718.
19. Thadhani R, Pascual M, Bonventre JV. Acute renal failure. N Engl J Med. 1996;334:1448-1460.
20. Kleinknecht D. Epidemiology in acute renal failure in France today. In: Biari D, Neild G, eds. Acute Renal Failure in Intensive Therapy Unit. Berlin: Springer-Verlag; 1990:13-21.
21. Cantarovich F, Bodin L. Functional acute renal failure. In: Cantarovich F, Rangoonwala B, Verho M, eds. Progress in Acute Renal Failure. Paris: Hoechst Marion Roussel; 1998:55-65.
22. Hunt JP, Meyer AA. Predicting survival in the intensive care unit. Curr Probl Surg. 1997;34:527-599.
23. Randolph AG, Guyatt GH, Carlet J. Understanding articles comparing outcomes among intensive care units to rate quality of care. Evidence based medicine in critical care group. Crit Care Med. 1998;26:773-781.
24. Matheny ME, Ohno-Machado L, Resnic FS. Risk-adjusted sequential probability ratio test control chart methods for monitoring operator and institutional mortality rates in interventional cardiology. Am Heart J. 2008;155: 114-120.
25. Topol EJ, Block PC, Holmes DR, et al. Readiness for the scorecard era in cardiovascular medicine. Am J Cardiol. 1995;75:1170-1173.
26. Rodríguez E, Soler MJ, Rap O, et al. Risk factors for acute kidney injury in severe rhabdomyolysis. PloS One. 2013;8:e82992.
27. McMahon GM, Zeng X, Waikar SS. A risk prediction score for kidney failure or mortality in rhabdomyolysis. JAMA Intern Med. 2013;173:1821-1828.
28. Kim WH, Lee SM, Choi JW, et al. Simplified clinical risk score to predict acute kidney injury after aortic surgery. J Cardiothorac Vasc Anesth. 2013; 27:1158-1166.
29. Slankamenac K, Beck-Schimmer B, Breitenstein S, et al. Novel prediction score including pre-and intraoperative parameters best predicts acute kidney injury after liver surgery. World J Surg. 2013;37:2618-2628.
30. Gurm HS, Seth M, Kooiman J, et al. A novel tool for reliable and accurate prediction of renal complications in patients undergoing percutaneous coronary intervention. J Am Coll Cardiol. 2013;61:2242-2248.
31. Ng SY, Sanagou M, Wolfe R, et al. Prediction of acute kidney injury within 30 days of cardiac surgery. J Thorac Cardiovasc Surg. Published Online First: 28 August 2013. doi:10.1016/j.jtcvs.2013.06.049.
32. Schneider DF, Dobrowolsky A, Shakir IA, et al. Predicting acute kidney injury among burn patients in the 21st century: a classification and regression tree analysis. J Burn Care Res OffPubl Am Burn Assoc. 2012;33:242-251.
33. Breidthardt T, Christ-Crain M, Stolz D, et al. A combined cardiorenal assessment for the prediction of acute kidney injury in lower respiratory tract infections. Am J Med. 2012;125:168-175.
34. Casanova R, Saldana S, Chew EY, et al. Application of random forests methods to diabetic retinopathy classification analyses. PloS One. 2014;9:e98587. 35.
35. Liu Y, Traskin M, Lorch SA, et al. Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance. Health Care Manag Sci. Published Online First: April 29, 2014. doi:10.1007/s10729-014-9272-4.
36. Sowa J-P, Heider D, Bechmann LP, et al. Novel algorithm for non-invasive assessment of fibrosis in NAFLD. PloS One. 2013;8:e62439.
37. Matheny ME, Miller RA, Ikizler TA, et al. Development of inpatient risk stratification models of acute kidney injury for use in electronic health records. Med Decis Mak Int J Soc Med Decis Mak. 2010;30:639-650.
38. Kolodner R. Computerizing Large Integrated Health Networks. The VA Success. New York: Springer-Verlag; 1997.
39. Payne TH, Savarino J. Development of a clinical event monitor for use with the Veterans Affairs Computerized Patient Record System and other data sources. Proc AMIA Annu Symp AMIA Symp. 1998;145-149.
40. Perlin JB, Kolodner RM, Roswell RH. The Veterans Health Administration: quality, value, accountability, and information as transforming strategies for patient-centered care. Am J Manag Care. 2004;10:828-836.
41. Brown SH, Lincoln MJ, Groen PJ, et al. VistA-U.S. Department of Veterans Affairs national-scale HIS. Int J Med Inf. 2003;69:135-156.
42. Siew ED, Ikizler TA, Matheny ME, et al. Estimating baseline kidney function in hospitalized patients with impaired kidney function. Clin J Am Soc Nephrol. 2012;7:712-719.
43. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373-383.
44. Greevy RA, Huizinga MM, Roumie CL, et al. Comparisons of persistence and durability among three oral antidiabetic therapies using electronic prescription-fill data: the impact of adherence requirements and stockpiling. Clin Pharmacol Ther. 2011;90:813-819.
45. Bishop CM. Pattern Recognition and Machine Learning. New York: Springer; 2006.
46. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Methodol. 1996;58:267-288.
47. Breiman L. Random forests. Mach Learn. 2001;45:5-32.
48. Team RCD. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna Austria; 2005.
49. Friedman J, Hastie T, Tibshirani R. glmnet: Lasso and elastic-net regularized generalized linear models. R Package Version. 2009;1:1-4.
50. De Ville B, Neville P. Decision Trees for Analytics Using SAS Enterprise Miner. SAS Institute; 2013. Cary, North Carolina, USA.
51. Harrell FE Jr, Lee KL, CaliffRM, et al. Regression modelling strategies for improved prognostic prediction. Stat Med. 1984;3:143-152.
52. Efron B. The Jackknife, the Bootstrap, and Other Resampling Plans. Philadelphia, PA: Society for Industrial and Applied Mathematics; 1982.
53. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29-36.
54. Pencina MJ, D'Agostino RB, D'Agostino RB, et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:157-172; discussion 207-212.
55. Brier G. Verification of forecasts expressed in terms of probabilities. Mon Weather Rev. 1950;78:1-3.
56. Lockhart R, Taylor J, Tibshirani RJ, et al. A significance test for the lasso. Ann Stat. 2014;42:413-468.
57. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiol Camb Mass. 2010;21:128-138.
58. Winham SJ, Colby CL, Freimuth RR, et al. SNP interaction detection with Random Forests in high-dimensional genetic data. BMC Bioinformatics. 2012;13:164.
59. Chen C, Liaw A, Breiman L. Using random forest to learn imbalanced data. Univ Calif Berkeley. Technical Report 666 2004.
60. Maudes J, Rodríguez JJ, García-Osorio C, et al. Random feature weights for decision tree ensemble construction. Inf Fusion. 2012;13: 20-30.
61. Amaratunga D, Cabrera J, Lee Y-S. Enriched random forests. Bioinforma Oxf Engl. 2008;24:2010-2014.
62. Winham SJ, Freimuth RR, Biernacka JM. A weighted random forests approach to improve predictive performance. Stat Anal Data Min. 2013;6: 496-505.
63. Better OS, Abassi ZA. Early fluid resuscitation in patients with rhabdomyolysis. Nat Rev Nephrol. 2011;7:416-422.
64. Trivedi HS, Moore H, Nasr S, et al. A randomized prospective trial to assess the role of saline hydration on the development of contrast nephrotoxicity. Nephron Clin Pract. 2003;93:C29-C34.
65. Wu LN, Genge BR, Wuthier RE. Evidence for specific interaction between matrix vesicle proteins and the connective tissue matrix. Bone Miner. 1992; 17:247-252.
66. Kellum JA, Unruh ML, Murugan R. Acute kidney injury. Clin Evid. 2011; 2011:2001.
67. Mueller C, Buerkle G, Buettner HJ, et al. Prevention of contrast mediaassociated nephropathy: randomized comparison of 2 hydration regimens in 1620 patients undergoing coronary angioplasty. Arch Intern Med. 2002;162:329-336.
68. Marathias KP, Vassili M, Robola A, et al. Preoperative intravenous hydration confers renoprotection in patients with chronic kidney disease undergoing cardiac surgery. Artif Organs. 2006;30:615-621.
69. Grams ME, Astor BC, Bash LD, et al. Albuminuria and estimated glomerular filtration rate independently associate with acute kidney injury. J Am Soc Nephrol. 2010;21:1757-1764.
70. Hsu C-Y, McCulloch CE, Fan D, et al. Community-based incidence of acute renal failure. Kidney Int. 2007;72:208-212.
71. Bellomo R, Kellum JA, Ronco C. Acute kidney injury. Lancet. 2012;380: 756-766.
72. Minne L, Eslami S, de Keizer N, et al. Effect of changes over time in the performance of a customized SAPS-II model on the quality of care assessment. Intensive Care Med. 2012;38:40-46.
73. Amarasingham R, Patzer RE, Huesch M, et al. Implementing electronic health care predictive analytics: considerations and challenges. Health AffProj Hope. 2014;33:1148-1154.
74. Hickey GL, Grant SW, Murphy GJ, et al. Dynamic trends in cardiac surgery: why the logistic EuroSCORE is no longer suitable for contemporary cardiac surgery and implications for future risk models. Eur J Cardio-Thorac Surg OffJ Eur Assoc Cardio-Thorac Surg. 2013;43:1146-1152.
75. Siew ED, Peterson JF, Eden SK, et al. Outpatient nephrology referral rates after acute kidney injury. J Am Soc Nephrol. 2012;23:305-312.
76. Gerds TA, Cai T, Schumacher M. The Performance of Risk Prediction Models. Biom J. 2008;50:457-479.