4 results on '"Congestive heart failure -- Risk factors"'
Search Results
2. Impact of Myocardial Viability on Long-term Outcomes after Surgical Revascularization.
- Author
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Sohn, Suk Ho, Kang, Yoonjin, Kim, Ji Seong, Park, Eun-Ah, Lee, Whal, and Hwang, Ho Young
- Subjects
REVASCULARIZATION (Surgery) ,CORONARY artery bypass ,CARDIAC magnetic resonance imaging ,CONGESTIVE heart failure ,CHRONIC obstructive pulmonary disease ,MYOCARDIAL revascularization ,VENTRICULAR ejection fraction - Abstract
Background This study was conducted to evaluate whether myocardial viability assessed with cardiac magnetic resonance (CMR) affected long-term clinical outcomes after coronary artery bypass grafting (CABG) in patients with ischemic cardiomyopathy (ICMP). Methods Preoperative CMR with late gadolinium enhancement (LGE) was performed in 103 patients (64.9 ± 10.1 years, male:female = 82:21) with 3-vessel disease and left ventricular dysfunction (ejection fraction ≤ 0.35). Transmural extent of LGE was evaluated on a 16-segment model, and transmurality was graded on a 5-point scale: grades—0, absence; 1, 1 to 25%; 2, 26 to 50%; 3, 51 to 75%; 4, 76 to 100%. Median follow-up duration was 65.5 months (interquartile range = 27.5–95.3 months). Primary endpoint was the composite of all-cause mortality or hospitalization for congestive heart failure. Results Operative mortality was 1.9%. During the follow-up, all-cause mortality and readmission for congestive heart failure occurred in 29 and 8 patients, respectively. The cumulative incidence of the primary endpoint was 31.3 and 46.8% at 5 and 10 years, respectively. Multivariable analysis demonstrated that the number of segments with LGE grade 4 was a significant risk factor (hazard ratio 1.42, 95% confidence interval 1.10–1.83, p = 0.007) for the primary endpoint among the variables assessed by CMR. Other risk factors included age, dialysis, chronic obstructive pulmonary disease, and EuroSCORE II. Conclusion The number of myocardial segments with transmurality of LGE >75% might be a prognostic factor associated with the composite of all-cause mortality or hospitalization for congestive heart failure after CABG in patients with 3-vessel disease and ICMP. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
- View/download PDF
3. Prescribing Benzodiazepines and Opioids and Clinical Characteristics Associated With 30-Day Hospital Return in Patients Aged ≥75 Years: Secondary Data Analysis.
- Author
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Park, Juyoung, Engstrom, Gabriella, and Ouslander, Joseph G.
- Subjects
BENZODIAZEPINES ,RISK assessment ,NURSES ,PATIENT education ,PEARSON correlation (Statistics) ,MEDICAL prescriptions ,SECONDARY analysis ,HUMAN services programs ,ACADEMIC medical centers ,OCCUPATIONAL roles ,PATIENTS ,PATIENT readmissions ,LOGISTIC regression analysis ,HOSPITAL admission & discharge ,SEX distribution ,HOSPITALS ,DISCHARGE planning ,TRANQUILIZING drugs ,DESCRIPTIVE statistics ,POLYPHARMACY ,CHI-squared test ,MULTIVARIATE analysis ,HOSPITAL emergency services ,CHRONIC diseases ,CAREGIVERS ,ODDS ratio ,OPIOID analgesics ,COMMUNICATION ,TELEPHONES ,ONE-way analysis of variance ,MEDICAL appointments ,ELECTRONIC health records ,MARITAL status ,DATA analysis software ,CONFIDENCE intervals ,LENGTH of stay in hospitals ,PATIENT aftercare ,HOSPITAL wards ,DISEASE complications - Abstract
Purpose: The current study compared prevalence of opioid or benzodiazepine (BZD) prescription and co-prescription of opioids and BZD at discharge and return to a community hospital within 30 days, as well as identified clinical characteristics associated with hospital return in patients aged ≥75 years. Method: A secondary analysis of a database created during implementation of the Safe Transitions for At Risk Patients program at a 400-bed community teaching hospital in south Florida was conducted. Multivariable logistic regression analyses were performed to identify significant demographic and clinical characteristics associated with return to the hospital within 30 days of discharge. Results: A total of 24,262 participants (52.6% women) with a mean age of 85.3 (SD = 6.42) years were included. More than 20% in each central nervous system prescription group (i.e., opioids only, BZD only, opioids and BZD) returned to the hospital within 30 days of discharge. Demographic and chronic conditions (e.g., congestive heart failure, chronic obstructive pulmonary disease, diabetes) and poly-pharmacy were significant factors of a 30-day return to the hospital. Conclusion: Findings highlight the importance of hospital nurses' role in identifying high-risk patients, educating patients and caregivers, monitoring them closely, communicating with primary care physicians and specialists, and conducting intensive follow up via telephone to avoid 30-day rehospitalization. [Journal of Gerontological Nursing, 50(4), 25–33.] [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
- View/download PDF
4. Advances in Artificial Intelligence : Biomedical Engineering Applications in Signals and Imaging
- Author
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Kunal Pal, Bala Chakravarthy Neelapu, J. Sivaraman, Kunal Pal, Bala Chakravarthy Neelapu, and J. Sivaraman
- Subjects
- Artificial intelligence--Medical applications, Biomedical engineering
- Abstract
Artificial Intelligence in health care has become one of the best assisting techniques for clinicians in proper diagnosis and surgery. In biomedical applications, artificial intelligence algorithms are explored for bio-signals such as electrocardiogram (ECG/ EKG), electrooculogram (EOG), electromyogram (EMG), electroencephalogram (EEG), blood pressure, heart rate, nerve conduction, etc., and for bio-imaging modalities, such as Computed Tomography (CT), Cone-Beam Computed Tomography (CBCT), MRI (Magnetic Resonance Imaging), etc. Advancements in Artificial intelligence and big data has increased the development of innovative medical devices in health care applications. Recent Advances in Artificial Intelligence: Medical Applications provides an overview of artificial intelligence in biomedical applications including both bio-signals and bio-imaging modalities. The chapters contain a mathematical formulation of algorithms and their applications in biomedical field including case studies. Biomedical engineers, advanced students, and researchers can use this book to apply their knowledge in artificial intelligence-based processes to biological signals, implement mathematical models and advanced algorithms, as well as develop AI-based medical devices. - Covers the recent advancements of artificial intelligence in healthcare, including case studies on how this technology can be used - Provides an understanding of the design of experiments to validate the developed algorithms - Presents an understanding of the versatile application of artificial intelligence in bio-signal and bio-image processing techniques
- Published
- 2024
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