Assessment of a university of California, Los Angeles 4-variable risk score for advanced heart failure

Journal of the American Heart Association

Volumn 3, Issue 3, 2014, Pages

  Sartipy, Ulrik ^a,b   Goda, Ayumi ^c   Mancini, Donna M ^d   Lund, Lars H ^a,b  

^a KAROLINSKA UNIVERSITY HOSPITAL   (Sweden)

^b KAROLINSKA INSTITUTE   (Sweden)

^c KYORIN UNIVERSITY   (Japan)

^d NewYork Presbyterian Hospital   (United States)

Author keywords

Heart failure; Heart transplantation; Prognostic risk models

Indexed keywords

ANGIOTENSIN RECEPTOR ANTAGONIST; BRAIN NATRIURETIC PEPTIDE; DIPEPTIDYL CARBOXYPEPTIDASE INHIBITOR;

ADULT; ARTICLE; CALIBRATION; CARDIOVASCULAR DISEASE ASSESSMENT; CARDIOVASCULAR RISK; CLINICAL ASSESSMENT TOOL; COHORT ANALYSIS; CONTROLLED STUDY; DRUG USE; EVENT FREE SURVIVAL; FEMALE; HEART FAILURE; HEART TRANSPLANTATION; HIGH RISK PATIENT; HUMAN; LEFT VENTRICULAR ASSIST DEVICE; MAJOR CLINICAL STUDY; MALE; NEW YORK HEART ASSOCIATION CLASS; OXYGEN CONSUMPTION; PRIORITY JOURNAL; PROTEIN BLOOD LEVEL; REPEAT PROCEDURE; RISK ASSESSMENT; TREATMENT INDICATION; UNITED STATES; VALIDATION PROCESS; BLOOD; DECISION SUPPORT SYSTEM; HEART ASSIST DEVICE; KAPLAN MEIER METHOD; MIDDLE AGED; PROPORTIONAL HAZARDS MODEL; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ANGIOTENSIN RECEPTOR ANTAGONISTS; ANGIOTENSIN-CONVERTING ENZYME INHIBITORS; DECISION SUPPORT TECHNIQUES; FEMALE; HEART FAILURE; HEART TRANSPLANTATION; HEART-ASSIST DEVICES; HUMANS; KAPLAN-MEIER ESTIMATE; MALE; MIDDLE AGED; NATRIURETIC PEPTIDE, BRAIN; OXYGEN CONSUMPTION; PROPORTIONAL HAZARDS MODELS; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; ROC CURVE; SENSITIVITY AND SPECIFICITY;

EID: 84939461892     PISSN: None     EISSN: 20479980     Source Type: Journal    
DOI: 10.1161/JAHA.114.000998     Document Type: Article

References (15)

1. Chyu J, Fonarow GC, Tseng CH, Horwich TB. Four-variable risk model in men and women with heart failure. Circ Heart Fail. 2014;7:88-95.
2. Moons KG, Kengne AP, Grobbee DE, Royston P, Vergouwe Y, Altman DG, Woodward M. Risk prediction models: II. External validation, model updating, and impact assessment. Heart. 2012;98:691-698.
3. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30:377-399.
4. Goda A, Williams P, Mancini D, Lund LH. Selecting patients for heart transplantation: comparison of the Heart Failure Survival Score (HFSS) and the Seattle Heart Failure Model (SHFM). J Heart Lung Transplant. 2011;30:1236-1243.
5. Goda A, Lund LH, Mancini D. The Heart Failure Survival Score outperforms the peak oxygen consumption for heart transplantation selection in the era of device therapy. J Heart Lung Transplant. 2011;30:315-325.
6. Gorodeski EZ, Chu EC, Chow CH, Levy WC, Hsich E, Starling RC. Application of the Seattle Heart Failure Model in ambulatory patients presented to an advanced heart failure therapeutics committee. Circ Heart Fail. 2010;3:706-714.
7. Kalogeropoulos AP, Georgiopoulou VV, Giamouzis G, Smith AL, Agha SA, Waheed S, Laskar S, Puskas J, Dunbar S, Vega D, Levy WC, Butler J. Utility of the Seattle Heart Failure Model in patients with advanced heart failure. J Am Coll Cardiol. 2009;53:334-342.
8. Sartipy U, Goda A, Yuzefpolskaya M, Mancini DM, Lund LH. Utility of the Seattle Heart Failure Model in patients with cardiac resynchronization therapy and implantable cardioverter defibrillator referred for heart transplantation. Am Heart J. 2014 (In press).
9. Leening MJ, Vedder MM, Witteman JC, Pencina MJ, Steyerberg EW. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide. Ann Intern Med. 2014;160:122-131.
10. Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150:795-802.
11. Hilden J, Gerds TA. A note on the evaluation of novel biomarkers: do not rely on integrated discrimination improvement and net reclassification index. Stat Med. 2013 (In press).
12. Vickers AJ, Pepe M. Does the net reclassification improvement help us evaluate models and markers? Ann Intern Med. 2014;160:136-137.
13. Pocock SJ, Ariti CA, McMurray JJ, Maggioni A, Kober L, Squire IB, Swedberg K, Dobson J, Poppe KK, Whalley GA, Doughty RN; Meta-Analysis Global Group in Chronic Heart Failure. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J. 2013; 34:1404-1413.
14. Sartipy U, Dahlstrom U, Edner M, Lund LH. Predicting survival in heart failure: validation of the MAGGIC heart failure risk score in 51 043 patients from the Swedish heart failure registry. Eur J Heart Fail. 2014;16:173-179.
15. Lund LH, Edwards LB, Kucheryavaya AY, Dipchand AI, Benden C, Christie JD, Dobbels F, Kirk R, Rahmel AO, Yusen RD, Stehlik J. The registry of the International Society for Heart and Lung Transplantation: thirtieth official adult heart transplant report-2013; focus theme: age. J Heart Lung Transplant. 2013;32:951-964.