PURPOSE: The purposes of this study were to (1) describe the characteristics of the population with congestive heart failure (CHF) who were admitted to a large, southeastern, acute-care hospital and (2) determine which patients are at risk for readmissions within 6 months. METHODS: A descriptive correlational design, using variables maintained in a computerized data bank on patients with CHF (N = 557, 39% were black) who were admitted between October 2000 and March 2002, was used to describe the adult population with CHF and identify variables associated with a likelihood of readmission. RESULTS: In the 6 months after the index admission, 224 (40%) of the patients were readmitted to the hospital for CHF. Variables significantly associated with readmission included lack of cardiology consult during admission, living status, point of entry of index admission, receiving Medicare, and having pulmonary hypertension. Four models, composed of subsets of variable from the data bank were developed and tested with logistic regression. The model composed of discharge variables was the only model that predicted readmission at a significant level. CONCLUSIONS: There is a need to develop comprehensive data banks to describe patterns of care and their outcomes. Such data should inform plans to manage this vulnerable population.
PURPOSE: The purposes of this study were to (1) describe the characteristics of the population with congestive heart failure (CHF) who were admitted to a large, southeastern, acute-care hospital and (2) determine which patients are at risk for readmissions within 6 months. METHODS: A descriptive correlational design, using variables maintained in a computerized data bank on patients with CHF (N = 557, 39% were black) who were admitted between October 2000 and March 2002, was used to describe the adult population with CHF and identify variables associated with a likelihood of readmission. RESULTS: In the 6 months after the index admission, 224 (40%) of the patients were readmitted to the hospital for CHF. Variables significantly associated with readmission included lack of cardiology consult during admission, living status, point of entry of index admission, receiving Medicare, and having pulmonary hypertension. Four models, composed of subsets of variable from the data bank were developed and tested with logistic regression. The model composed of discharge variables was the only model that predicted readmission at a significant level. CONCLUSIONS: There is a need to develop comprehensive data banks to describe patterns of care and their outcomes. Such data should inform plans to manage this vulnerable population.
Authors: Melissa O'Connor; Christopher M Murtaugh; Shivani Shah; Yolanda Barrón-Vaya; Kathryn H Bowles; Timothy R Peng; Carolyn W Zhu; Penny H Feldman Journal: Med Care Res Rev Date: 2015-07-14 Impact factor: 3.929
Authors: Matthew E Dupre; Hanzhang Xu; Bradi B Granger; Scott M Lynch; Alicia Nelson; Erik Churchill; Janese M Willis; Lesley H Curtis; Eric D Peterson Journal: Am Heart J Date: 2017-10-05 Impact factor: 4.749
Authors: Jia-Rong Wu; Darren A DeWalt; David W Baker; Dean Schillinger; Bernice Ruo; Kristen Bibbins-Domingo; Aurelia Macabasco-O'Connell; George M Holmes; Kimberly A Broucksou; Brian Erman; Victoria Hawk; Crystal W Cene; Christine DeLong Jones; Michael Pignone Journal: J Clin Nurs Date: 2013-12-20 Impact factor: 3.036