Self-Care Agency Power Components Among Patients with Heart Failure: A Qualitative Directed Content Analysis Based on the Orem Self-Care Theory

Abotalebidariasari, Ghasem; Memarian, Robabeh; Vanaki, Zohreh; Kazemnejad, Anoshirvan; Naderi, Nasim

doi:10.5812/ccn.9150

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…One of the most popular chronic diseases in the world is heart failure affecting 1-2% of population [1]. Acute Decompensated Heart Failure refers to the gradual development of cardiac breakdown as a clinical condition of new worsening symptoms that need hospitalization.…”

Section: Introductionmentioning

confidence: 99%

A Study On Assessment Of Clinical Profile And Quality Of Life Of Acute Decompensated Heart Failure In Tertiary Care Hospital

Sireesha,

Fatima,

Sultana

et al. 2024

jaz

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Heart failure is a prevalent chronic disease worldwide, and acute decompensated heart failure (ADHF) is characterized by sudden or gradual onset of heart failure symptoms requiring unplanned medical attention. The study aims to examine the clinical presentation, and Quality of life of ADHF in a tertiary care hospital, along with the demographic characteristics. The study highlights the impact of ADHF on physical and emotional wellbeing and help identify potential risk factors and complications. It was a prospective observational study conducted at a tertiary care hospital for a period of 6 months and 75 Patients were included. Patients were evaluated clinically and with laboratory tests. Data were collected and analysed. The majority of patients were female (84%). A combination of Diabetes and Hypertension (25.3%) was the most common past medical illness found in the patients. The most common presenting symptom was shortness of breath 42(56%). Ischemic stroke was the most common complication (20%). The overall score of Qol was found to be mostly between 51-75 (61.4%) and the mean was 52.81. In conclusion the findings will provide valuable insights into the clinical characteristics and QoL of ADHF patients. Periodic QoL assessments could contribute to better patient management and overall outcomes in ADHF. However, as the study is conducted in a single center with a limited sample size, the results should be interpreted with caution and cannot be generalised to the broader population.

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Section: Introductionmentioning

confidence: 99%

A Study On Assessment Of Clinical Profile And Quality Of Life Of Acute Decompensated Heart Failure In Tertiary Care Hospital

Sireesha,

Fatima,

Sultana

et al. 2024

jaz

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show abstract

“…Heart failure (HF) is one of the most common chronic diseases worldwide, and its incidence has increased in recent years [1]. Despite recent innovations in drug use and clinical diagnosis management, HF patients remain difficult to treat because their condition is often complex and influenced by multiple factors [2].…”

Section: Introductionmentioning

confidence: 99%

DU-ResNet to Predict Survival in Patient of Heart Failure

Zhang,

Tang

2023

Preprint

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Predicting survival in patients with heart disease clinically is a challenging task. Predicting the survival state is very important among patients with heart failure. In this paper, we propose a prediction model for the survival of heart failure patients based on deep learning combined with clinical data of heart failure patients. The proposed model is named DU-ResNet which is designed by combining ResUNet with ResNet50. Most clinical data of patients with heart failure are only numerical heart failure datasets. If one-dimensional clinical data can be converted into two-dimensional image data, the advantages of a deep convolutional neural network in extracting spatial features can be fully realized. For this reason, in this paper, the clinical data of all original patients with heart failure were normalized first, and then, each normalized clinical data point was placed in a certain area of the grid image. Therefore, according to the value of each clinical data point, a gray image with different brightness regions was constructed. After data enhancement was performed on the constructed image dataset of clinical data of heart failure patients to expand the number of samples, DU-ResNet is used to binary classify the expanded dataset, and ten-fold cross-validation and ablation experiments are performed on the dataset. Then, ten-fold cross-validation was used to verify the performance of the proposed DU-ResNet model. The results show that the proposed DU-ResNet model has the best result with the use of four features, with the accuracy was 96.47%, the Specificity was 97.22%, the Sensitivity was 95.71%, the Precision was 96.87%, the F1-score was 96.27% and the MCC was 92.97% after ten-fold cross-validation. In addition, the comprehensive performance of the proposed DU-ResNet model for predicting the survival of patients with heart failure is better than several typical deep learning methods.

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Effect of educational interventions on health literacy in patients with heart failure

Sany

Peyman

Zadehahmad

et al. 2018

International Journal of Health Promotion and Education

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Self-Care Agency Power Components Among Patients with Heart Failure: A Qualitative Directed Content Analysis Based on the Orem Self-Care Theory

Cited by 3 publications

References 15 publications

A Study On Assessment Of Clinical Profile And Quality Of Life Of Acute Decompensated Heart Failure In Tertiary Care Hospital

A Study On Assessment Of Clinical Profile And Quality Of Life Of Acute Decompensated Heart Failure In Tertiary Care Hospital

DU-ResNet to Predict Survival in Patient of Heart Failure

Effect of educational interventions on health literacy in patients with heart failure

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