Incremental Value of Clinical Data Beyond Claims Data in Predicting 30-Day Outcomes After Heart Failure Hospitalization
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[1] E. DeLong,et al. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.
[2] N. Nagelkerke,et al. A note on a general definition of the coefficient of determination , 1991 .
[3] K. Labresh,et al. Get with the guidelines. , 2013, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.
[4] C. O'connor,et al. Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF): rationale and design. , 2004, American heart journal.
[5] Lisa I. Iezzoni,et al. Risk Adjustment of Medicare Capitation Payments Using the CMS-HCC Model , 2004, Health care financing review.
[6] Peter C Austin,et al. Bootstrap Methods for Developing Predictive Models , 2004 .
[7] W John Boscardin,et al. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis. , 2005, JAMA.
[8] A. Jha,et al. Care in U.S. hospitals--the Hospital Quality Alliance program. , 2005, The New England journal of medicine.
[9] David C Hoaglin,et al. The Hazards of Using Administrative Data to Measure Surgical Quality , 2006, The American surgeon.
[10] Harlan M Krumholz,et al. An Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30-Day Mortality Rates Among Patients With Heart Failure , 2006, Circulation.
[11] Yuling Hong,et al. Overview of the American Heart Association "Get with the Guidelines" programs: coronary heart disease, stroke, and heart failure. , 2006, Critical pathways in cardiology.
[12] D. Hoaglin,et al. Enhancement of claims data to improve risk adjustment of hospital mortality. , 2007, JAMA.
[13] G. Fonarow,et al. Predictors of in-hospital mortality in patients hospitalized for heart failure: insights from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF). , 2008, Journal of the American College of Cardiology.
[14] Sharon-Lise T. Normand,et al. An Administrative Claims Measure Suitable for Profiling Hospital Performance on the Basis of 30-Day All-Cause Readmission Rates Among Patients With Heart Failure , 2008, Circulation. Cardiovascular quality and outcomes.
[15] K. Schulman,et al. Early and long-term outcomes of heart failure in elderly persons, 2001-2005. , 2008, Archives of internal medicine.
[16] M. Pencina,et al. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond , 2008, Statistics in medicine.
[17] Adrian F Hernandez,et al. Representativeness of a National Heart Failure Quality-of-Care Registry: Comparison of OPTIMIZE-HF and Non–OPTIMIZE-HF Medicare Patients , 2009, Circulation. Cardiovascular quality and outcomes.
[18] Adrian F Hernandez,et al. Linking inpatient clinical registry data to Medicare claims data using indirect identifiers. , 2009, American heart journal.
[19] Li Liang,et al. A Validated Risk Score for In-Hospital Mortality in Patients With Heart Failure From the American Heart Association Get With the Guidelines Program , 2010, Circulation. Cardiovascular quality and outcomes.
[20] Health information technology: initial set of standards, implementation specifications, and certification criteria for electronic health record technology. Final rule. , 2010, Federal register.
[21] Harlan M. Krumholz,et al. Recent National Trends in Readmission Rates After Heart Failure Hospitalization , 2010, Circulation. Heart failure.
[22] Matthew M Davis,et al. The Patient Protection and Affordable Care Act of 2010 , 2010, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.