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Similar articles for PubMed (Select 23165299)

1.

Assessing risk in chronic kidney disease: a methodological review.

Grams ME, Coresh J.

Nat Rev Nephrol. 2013 Jan;9(1):18-25. doi: 10.1038/nrneph.2012.248. Epub 2012 Nov 20. Review.

PMID:
23165299
2.

Risk models to predict chronic kidney disease and its progression: a systematic review.

Echouffo-Tcheugui JB, Kengne AP.

PLoS Med. 2012;9(11):e1001344. doi: 10.1371/journal.pmed.1001344. Epub 2012 Nov 20. Review.

3.

Risk prediction models for patients with chronic kidney disease: a systematic review.

Tangri N, Kitsios GD, Inker LA, Griffith J, Naimark DM, Walker S, Rigatto C, Uhlig K, Kent DM, Levey AS.

Ann Intern Med. 2013 Apr 16;158(8):596-603. doi: 10.7326/0003-4819-158-8-201304160-00004. Review.

PMID:
23588748
4.

Urinary neutrophil gelatinase-associated lipocalin may aid prediction of renal decline in patients with non-proteinuric Stages 3 and 4 chronic kidney disease (CKD).

Smith ER, Lee D, Cai MM, Tomlinson LA, Ford ML, McMahon LP, Holt SG.

Nephrol Dial Transplant. 2013 Jun;28(6):1569-79. doi: 10.1093/ndt/gfs586. Epub 2013 Jan 16.

5.

Comparison of predictability of future cardiovascular events between chronic kidney disease (CKD) stage based on CKD epidemiology collaboration equation and that based on modification of diet in renal disease equation in the Japanese general population--Iwate KENCO Study.

Ohsawa M, Tanno K, Itai K, Turin TC, Okamura T, Ogawa A, Ogasawara K, Fujioka T, Onoda T, Yoshida Y, Omama S, Ishibashi Y, Nakamura M, Makita S, Tanaka F, Kuribayashi T, Koyama T, Sakata K, Okayama A.

Circ J. 2013;77(5):1315-25. Epub 2013 Feb 21.

6.

Improved cardiovascular risk prediction using nonparametric regression and electronic health record data.

Kennedy EH, Wiitala WL, Hayward RA, Sussman JB.

Med Care. 2013 Mar;51(3):251-8. doi: 10.1097/MLR.0b013e31827da594.

7.

Assessing the performance of prediction models: a framework for traditional and novel measures.

Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, Pencina MJ, Kattan MW.

Epidemiology. 2010 Jan;21(1):128-38. doi: 10.1097/EDE.0b013e3181c30fb2.

8.

A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods.

Collins GS, Omar O, Shanyinde M, Yu LM.

J Clin Epidemiol. 2013 Mar;66(3):268-77. doi: 10.1016/j.jclinepi.2012.06.020. Epub 2012 Oct 30. Review.

PMID:
23116690
9.

The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation incorporating both cystatin C and creatinine best predicts individual risk: a cohort study in 444 patients with chronic kidney disease.

Rogacev KS, Pickering JW, Seiler S, Zawada AM, Emrich I, Fliser D, Heine GH.

Nephrol Dial Transplant. 2014 Feb;29(2):348-55. doi: 10.1093/ndt/gft422. Epub 2013 Oct 28.

PMID:
24166454
10.

Risk prediction models.

Tripepi G, Heinze G, Jager KJ, Stel VS, Dekker FW, Zoccali C.

Nephrol Dial Transplant. 2013 Aug;28(8):1975-80. doi: 10.1093/ndt/gft095. Epub 2013 May 7.

PMID:
23658248
11.

Biomarkers of inflammation, fibrosis, cardiac stretch and injury predict death but not renal replacement therapy at 1 year in a Canadian chronic kidney disease cohort.

Levin A, Rigatto C, Barrett B, Madore F, Muirhead N, Holmes D, Clase CM, Tang M, Djurdjev O; CanPREDDICT Investigators.

Nephrol Dial Transplant. 2014 May;29(5):1037-47. doi: 10.1093/ndt/gft479. Epub 2013 Dec 26.

PMID:
24371297
12.

Validation of the kidney failure risk equation in European CKD patients.

Peeters MJ, van Zuilen AD, van den Brand JA, Bots ML, Blankestijn PJ, Wetzels JF; MASTERPLAN Study Group.

Nephrol Dial Transplant. 2013 Jul;28(7):1773-9. doi: 10.1093/ndt/gft063. Epub 2013 May 3.

13.

Clinical trial--derived risk model may not generalize to real-world patients with acute coronary syndrome.

Yan AT, Jong P, Yan RT, Tan M, Fitchett D, Chow CM, Roe MT, Pieper KS, Langer A, Goodman SG; Canadian Acute Coronary Syndromes registry investigators.

Am Heart J. 2004 Dec;148(6):1020-7.

PMID:
15632888
14.

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery - part II: an illustrative example.

Cevenini G, Barbini E, Scolletta S, Biagioli B, Giomarelli P, Barbini P.

BMC Med Inform Decis Mak. 2007 Nov 22;7:36.

15.

A predictive model for progression of chronic kidney disease to kidney failure.

Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D, Levin A, Levey AS.

JAMA. 2011 Apr 20;305(15):1553-9. doi: 10.1001/jama.2011.451. Epub 2011 Apr 11.

PMID:
21482743
16.

Pre-operative and intraoperative determinants for prolonged ventilation following adult cardiac surgery.

Widyastuti Y, Stenseth R, Pleym H, Wahba A, Videm V.

Acta Anaesthesiol Scand. 2012 Feb;56(2):190-9. doi: 10.1111/j.1399-6576.2011.02538.x. Epub 2011 Oct 14.

PMID:
22091558
17.

Predicting 5-year risk of kidney transplant failure: a prediction instrument using data available at 1 year posttransplantation.

Shabir S, Halimi JM, Cherukuri A, Ball S, Ferro C, Lipkin G, Benavente D, Gatault P, Baker R, Kiberd B, Borrows R.

Am J Kidney Dis. 2014 Apr;63(4):643-51. doi: 10.1053/j.ajkd.2013.10.059. Epub 2013 Dec 31.

PMID:
24387794
18.

Prediction of poor outcome in patients with acute liver failure-systematic review of prediction models.

Wlodzimirow KA, Eslami S, Chamuleau RA, Nieuwoudt M, Abu-Hanna A.

PLoS One. 2012;7(12):e50952. doi: 10.1371/journal.pone.0050952. Epub 2012 Dec 14. Review.

19.

Development and validation of a clinical prediction rule for major adverse outcomes in coronary bypass grafting.

Fortescue EB, Kahn K, Bates DW.

Am J Cardiol. 2001 Dec 1;88(11):1251-8.

PMID:
11728352
20.

ACHTUNG-Rule: a new and improved model for prognostic assessment in myocardial infarction.

Barra S, ProvidĂȘncia R, Paiva L, Caetano F, Almeida I, Gomes P, Marques AL.

Eur Heart J Acute Cardiovasc Care. 2012 Dec;1(4):320-36. doi: 10.1177/2048872612466536.

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