Vasiliki Betihavas1, Steven A Frost2, Phillip J Newton3, Peter Macdonald4, Simon Stewart5, Melinda J Carrington5, Yih Kai Chan5, Patricia M Davidson6. 1. The University of Sydney, Sydney Nursing School, Sydney, NSW, Australia. Electronic address: vasiliki.betihavas@sydney.edu.au. 2. The University of Western Sydney, School of Nursing and Midwifery, Sydney, NSW Australia & Intensive Care Liverpool Hospital, University of NSW, Sydney, NSW Australia. 3. Centre for Cardiovascular and Chronic Care, University of Technology, Sydney, NSW, Australia. 4. St Vincent's Hospital, Sydney, NSW, Australia and Victor Chang Cardiac Research Institute, Sydney, NSW, Australia. 5. Preventative Health, Baker IDI Heart and Diabetes Institute, Melbourne, Vic, Australia. 6. School of Nursing, Johns Hopkins University, Washington, DC, USA.
Abstract
BACKGROUND: Frequent readmissions are a hallmark of chronic heart failure (CHF). We sought to develop an absolute risk prediction model for unplanned cardiovascular readmissions following hospitalisation for CHF. METHODS: An inception cohort was obtained from the WHICH? trial, a prospective, multi-centre randomised controlled trial which was a head-to-head comparison of the efficacy of a home-based intervention versus clinic-based intervention for adults with CHF. A Cox's proportional hazards model (taking into account the competing risk of death) was used to develop a prediction model. Bootstrap methods were used to identify factors for the final model. Based on these data a nomogram was developed. RESULTS: Of the 280 participants in the WHICH? trial 37 (13%) were readmitted for a cardiovascular event (including CHF) within 28 days, and a further 149 (53%) were readmitted within 18 months for a cardiovascular event. In the proposed competing risk model, factors associated with an increased risk of hospitalisation for CHF were: age (HR 1.07, 95% CI 0.90-1.26) for each 10-year increase in age; living alone (HR 1.09, 95% CI 0.74-1.59); those with a sedentary lifestyle (HR 1.44, 95% CI, 0.92-2.25) and the presence of multiple co-morbid conditions (HR 1.69, 95% CI 0.38-7.58) for five or more co-morbid conditions (compared to individuals with one documented co-morbidity). The C-statistic of the final model was 0.80. CONCLUSION: We have developed a practical model for individualising the risk of short-term readmission for CHF. This model may provide additional information for targeting and tailoring interventions and requires future prospective evaluation.
RCT Entities:
BACKGROUND: Frequent readmissions are a hallmark of chronic heart failure (CHF). We sought to develop an absolute risk prediction model for unplanned cardiovascular readmissions following hospitalisation for CHF. METHODS: An inception cohort was obtained from the WHICH? trial, a prospective, multi-centre randomised controlled trial which was a head-to-head comparison of the efficacy of a home-based intervention versus clinic-based intervention for adults with CHF. A Cox's proportional hazards model (taking into account the competing risk of death) was used to develop a prediction model. Bootstrap methods were used to identify factors for the final model. Based on these data a nomogram was developed. RESULTS: Of the 280 participants in the WHICH? trial 37 (13%) were readmitted for a cardiovascular event (including CHF) within 28 days, and a further 149 (53%) were readmitted within 18 months for a cardiovascular event. In the proposed competing risk model, factors associated with an increased risk of hospitalisation for CHF were: age (HR 1.07, 95% CI 0.90-1.26) for each 10-year increase in age; living alone (HR 1.09, 95% CI 0.74-1.59); those with a sedentary lifestyle (HR 1.44, 95% CI, 0.92-2.25) and the presence of multiple co-morbid conditions (HR 1.69, 95% CI 0.38-7.58) for five or more co-morbid conditions (compared to individuals with one documented co-morbidity). The C-statistic of the final model was 0.80. CONCLUSION: We have developed a practical model for individualising the risk of short-term readmission for CHF. This model may provide additional information for targeting and tailoring interventions and requires future prospective evaluation.
Authors: Amit Kumar; Indrakshi Roy; Pamela R Bosch; Corey R Fehnel; Nicholas Garnica; Jon Cook; Meghan Warren; Amol M Karmarkar Journal: J Gen Intern Med Date: 2021-10-26 Impact factor: 6.473
Authors: S Neubauer; T Schilling; J Zeidler; A Lange; S Engel; R Linder; F Verheyen; J-M Graf von der Schulenburg; A Haverich Journal: Herz Date: 2016-02-16 Impact factor: 1.443