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Risk Predictors of 3-Month and 1-Year Outcomes in Heart Failure Patients with Prior Ischemic Stroke. J Clin Med 2022; 11:jcm11195922. [PMID: 36233790 PMCID: PMC9573085 DOI: 10.3390/jcm11195922] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/01/2022] [Accepted: 10/02/2022] [Indexed: 11/20/2022] Open
Abstract
Background: Despite available therapy, mortality, and readmission rates within 60–90 days of discharge for patients hospitalized with heart failure (HF) are higher compared to the 1-year rates. This study sought to identify the risk factors of the combined endpoint of all-cause readmission or death among HF patients. Methods: Patients with a diagnosis of HF aged 65 or older were included in this prospective observational cohort study. The outcomes were estimated within 3-months and 1 year of discharge. Risk modeling was performed using a multivariable Cox regression analysis of HF patients older than 65 who had experienced ischemic stroke. Results: A total of 951 HF patients enrolled, of whom 340 (35.8%) had suffered a prior ischemic stroke. Significant predictors of increased 3-month all-cause readmission or death included DBP (p = 0.045); serum albumin (p = 0.025), TSH (p = 0.017); and discharge without ACE-inhibitor/ARB/ARNI (p = 0.025), β-blockers (p = 0.029), and antiplatelet drugs (p = 0.005). Heart rate (p = 0.040), laboratory parameters—including serum albumin (p = 0.003), CRP p = 0.028), and FT4 (p = 0.018)—and discharge without β-blockers (p = 0.003), were significant predictors of increased 1-year all-cause readmission and death. Conclusions: Without β-blockers, lower serum albumin and abnormal thyroid function increase the risks of readmission and death in elderly HF patients who have had an ischemic stroke by 3 months and 1 year after discharge. The other factors, such as being without ACEI/ARB and a high heart rate, only increase risks before 3 months or 1 year, not both.
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Gong E, Gu W, Sun C, Turner EL, Zhou Y, Li Z, Bettger JP, Oldenburg B, Amaya-Burns A, Wang Y, Xu LQ, Yao J, Dong D, Xu Z, Li C, Hou M, Yan LL. System-integrated technology-enabled model of care to improve the health of stroke patients in rural China: protocol for SINEMA-a cluster-randomized controlled trial. Am Heart J 2018; 207:27-39. [PMID: 30408621 DOI: 10.1016/j.ahj.2018.08.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 08/29/2018] [Indexed: 01/01/2023]
Abstract
BACKGROUND Despite the significant burden of stroke in rural China, secondary prevention of stroke is suboptimal. This study aims to develop a SINEMA for the secondary prevention of stroke in rural China and to evaluate the effectiveness of the model compared with usual care. METHODS The SINEMA model is being implemented and evaluated through a 1-year cluster-randomized controlled trial in Nanhe County, Hebei Province in China. Fifty villages from 5 townships are randomized in a 1:1 ratio to either the intervention or the control arm (usual care) with a target to enroll 25 stroke survivors per village. Village doctors in the intervention arm (1) receive systematic cascade training by stroke specialists on clinical guidelines, essential medicines and behavior change; (2) conduct monthly follow-up visits with the support of a mobile phone application designed for this study; (3) participate in virtual group activities with other village doctors; 4) receive performance feedback and payment. Stroke survivors participate in a health education and project briefing session, receive monthly follow-up visits by village doctors and receive a voice message call daily as reminders for medication use and physical activities. Baseline and 1-year follow-up survey will be conducted in all villages by trained staff who are blinded of the randomized allocation of villages. The primary outcome will be systolic blood pressure and the secondary outcomes will include diastolic blood pressure, medication adherence, mobility, physical activity level and quality of life. Process and economic evaluation will also be conducted. DISCUSSION This study is one of very few that aim to promote secondary prevention of stroke in resource-constrained settings and the first to incorporate mobile technologies for both healthcare providers and patients in China. The SINEMA model is innovative as it builds the capacity of primary healthcare workers in the rural area, uses mobile health technologies at the point of care, and addresses critical health needs for a vulnerable community-dwelling patient group. The findings of the study will provide translational evidence for other resource-constrained settings in developing strategies for the secondary prevention of stroke.
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Affiliation(s)
- Enying Gong
- Global Health Research Center, Duke Kunshan University, Jiangsu, China; School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Wanbing Gu
- Global Health Research Center, Duke Kunshan University, Jiangsu, China
| | - Cheng Sun
- Global Health Research Center, Duke Kunshan University, Jiangsu, China
| | - Elizabeth L Turner
- Duke Global Health Institute, Duke University, North Carolina; Department of Biostatistics & Bioinformatics, Duke University, North Carolina
| | - Yun Zhou
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zixiao Li
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Janet Prvu Bettger
- Duke Global Health Institute, Duke University, North Carolina; Department of Orthopedic Surgery, Duke University, North Carolina
| | - Brian Oldenburg
- School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Alba Amaya-Burns
- Global Health Research Center, Duke Kunshan University, Jiangsu, China
| | - Yilong Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Li-Qun Xu
- Center of Excellence for mHealth and Smart Healthcare, China Mobile Research Institute, Beijing, China
| | | | - Dejin Dong
- Xingtai Center for Disease Control and Prevention, Hebei, China
| | - Zhenli Xu
- Nanhe Center for Disease Control and Prevention, Hebei, China
| | - Chaoyun Li
- Global Health Research Center, Duke Kunshan University, Jiangsu, China
| | - Mobai Hou
- Health Bureau of Nanhe County, Hebei, China
| | - Lijing L Yan
- Global Health Research Center, Duke Kunshan University, Jiangsu, China; Duke Global Health Institute, Duke University, North Carolina.
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