Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration

Journal of Biomedical Informatics

Volumn 53, Issue , 2015, Pages 220-228

  Singh, Anima ^a   Nadkarni, Girish ^b   Gottesman, Omri ^b   Ellis, Stephen B ^b   Bottinger, Erwin P ^b,c   Guttag, John V ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^c One Gustave L Levy Pl   (United States)

Author keywords

Electronic health records; Progression of kidney function loss; Risk stratification; Temporal analysis

Indexed keywords

DETERIORATION; HEALTH RISKS; INDUSTRIAL POISONS; LEARNING SYSTEMS; MULTI-TASK LEARNING; RECORDS MANAGEMENT; RISK ASSESSMENT;

ELECTRONIC HEALTH RECORD; ELECTRONIC HEALTH RECORD (EHRS); GLOMERULAR FILTRATION RATE; KIDNEY FUNCTION; RISK STRATIFICATION; TECHNICAL CHALLENGES; TEMPORAL ANALYSIS; TEMPORAL INFORMATION;

PREDICTIVE ANALYTICS;

AGED; ARTICLE; DETERIORATION; ELECTRONIC MEDICAL RECORD; FEMALE; GLOMERULUS FILTRATION RATE; HUMAN; ICD-9; KIDNEY FUNCTION; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MEDICAL HISTORY; PATIENT INFORMATION; PREDICTION; PREDICTIVE VALUE; PRIORITY JOURNAL; PROBABILITY; RISK ASSESSMENT; RISK FACTOR; TEMPORAL ANALYSIS; TIME; ALGORITHM; AREA UNDER THE CURVE; COMPUTER PROGRAM; DISEASE COURSE; HOSPITAL; KIDNEY; KIDNEY DISEASES; MEDICAL INFORMATICS; PATHOPHYSIOLOGY; PROCEDURES; RISK; STATISTICAL MODEL; UNITED STATES;

ALGORITHMS; AREA UNDER CURVE; DISEASE PROGRESSION; ELECTRONIC HEALTH RECORDS; GLOMERULAR FILTRATION RATE; HOSPITALS; HUMANS; KIDNEY; KIDNEY DISEASES; MACHINE LEARNING; MEDICAL INFORMATICS; MODELS, STATISTICAL; NEW YORK CITY; RISK; SOFTWARE; TIME FACTORS;

EID: 84924295546     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2014.11.005     Document Type: Article

References (20)

1. Levey A.S., Stevens L.A., Coresh J. Conceptual model of CKD: applications and implications. Am J Kidney Dis 2009, 53(Suppl. 3):S4-S16. 10.1053/j.ajkd.2008.07.048.
2. US Renal Data System, USRDS 2013 Annual Data Report in Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States. 2013, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases: Bethesda, MD.
3. Sud M., et al. CKD stage at nephrology referral and factors influencing the risks of ESRD and death. Am J Kidney Dis 2014, pii: S0272-6386(13)01637-5. 10.1053/j.ajkd.2013.12.008.
4. Chou C.-C., et al. Adults with late stage 3 chronic kidney disease are at high risk for prevalent silent brain infarction: a population-based study. Stroke; J Cerebral Circulat 2011, 42(8):2120-2125. 10.1161/STROKEAHA.110.597930.
5. Tangri N., et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 2011, 305(15):1553-1559. 10.1001/jama.2011.451.
6. Desai A.S., et al. Association between cardiac biomarkers and the development of ESRD in patients with type 2 diabetes mellitus, Anemia, and CKD. Am J Kidney Dis 2011, 58(5):717-728. 10.1053/j.ajkd.2011.05.020.
7. Landray M.J., et al. Prediction of ESRD and death among people with CKD: the Chronic Renal Impairment in Birmingham (CRIB) prospective cohort study. Am J Kidney Dis 2010, 56(6):1082-1094. 10.1053/j.ajkd.2010.07.016.
8. Echouffo-Tcheugui J.B., Kengne A.P. Risk models to predict chronic kidney disease and its progression: a systematic review. PLoS Med 2012, 9(11):e1001344. 10.1371/journal.pmed.1001344.
9. Hallan S.I., et al. Combining GFR and albuminuria to classify CKD improves prediction of ESRD. J Am Soc Nephrol 2009, 20(5):1069-1077. 10.1681/ASN.2008070730.
10. Hauskrecht MaL, Zitao, Wu, Lei, Modeling clinical time series using Gaussian process sequences. In: SIAM international conference on data mining; 2013. p. 623-31.
11. Luo L., et al. Methods for estimating kidney disease stage transition probabilities using electronic medical records. eGEMs (Generating Evidence & Methods to improve patient outcomes) 2013, 1(3). 10.13063/2327-9214.104.
12. Toma T., Abu-Hanna A., Bosman R. Discovery and inclusion of SOFA score episode in mortality prediction. J Biomed Inform 2007, 40:649-660. 10.1016/j.jbi.2007.03.007.
13. Tudor Toma R.-J.B., Arno Siebes, Niels Peek, Ameen Abu-Hanna Learning predictive models that use pattern discovery - a bootstrap evaluative approach applied in organ functioning sequences. J Biomed Inform 2010, 43:578-586. 10.1016/j.jbi.2010.03.00.
14. Levey A.S., Coresh J. Chronic kidney disease. The Lancet 2012, 379(9811):165-180. 10.1016/S0140-6736(11)60178-5.
15. Levey A.S., et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009, 150(9):604-612. 10.1371/journal.pone.0079675.
16. Nathans Laura L., Oswald F.L., Nimon Kim Interpreting multiple linear regression: a guidebook of variable importance. Pract Assess, Res Eval 2012, 9.
17. Kissmeyer L., et al. Community nephrology: audit of screening for renal insufficiency in a high risk population. Nephrol Dial Transplant 1999, 14(9):2150-2155. 10.1093/ndt/14.9.2150.
18. Kidney disease outcomes quality initiative, K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 40(6): E19-E22.
19. Ginès P., Schrier R.W. Renal failure in cirrhosis. N Engl J Med 2009, 361(13):1279-1290. 10.1056/NEJMra0809139.
20. Stengel B. Chronic kidney disease and cancer: a troubling connection. J Nephrol 2010, 23(3):253-262.