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Showing 4 results for Heart Failure
Designing a Mobile-Based Self-Care Application for Patients with Heart Failure
Marjan Ghazisaeedi, Leila Shahmoradi, Abdolmohammad Ranjbar, Zohreh Sahraei, Farideh Tahmasebi,
Volume 3, Issue 3 (12-2016)
Abstract

Introduction: Patients with heart failure experience debilitating symptoms lowering their life quality. Patients must use several methods of self-care in order to reduce symptoms and the rate of hospitalization and to improve their quality of life. The main purpose of this study was to design and create a mobile-based self-care application for patients with heart failure.

Methods: This was an applied developmental study. Initially, a questionnaire was prepared to assess information requierments, determine data elements and necessary capacities of the application. Then, 20 cardiologists working in Shahid Madany hospital, the specialty center of cardiovascular surgery affiliated to Tabriz University of Medical Sciences were asked to fill out the questionnaire. The obtained data were analysed and the application was developed accordingly.  Finally, in order to evaluate its practical capability and patients’ satisfaction rate, it was used by 10 patients with heart failure.

Results: The majority of data elements in the questionnaire were found necessary by the cardiologists. The main features of the designed application included alerts for daily monitoring, visit time and reminders of time of drug use, hypertention, increase of heart rate and weight gain. In evaluation of practical capability of the application by patients, it was evaluated in a good level with the mean score of 7.8 (out of 9).

Conclusion: Mobile-based self-care applications can be used to help patients with heart failure to manage their disease and to obtain self-care skills.


Heart Failure Management via Mobile Phones: A Systematic Review
Zakie Piri, Mahsa Dehghani Sufi, Zeynab Salimzadeh, Bahareh Ashragh, Ghasem Alizadeh ,
Volume 4, Issue 3 (12-2017)
Abstract
Introduction: Advances in telecommunication technologies have created new opportunities to provide telemedical care as an adjunct to medical management of patients with heart failure. One of the proposed strategies for helping heart failure patients in managing their disease is using mobile phone which is now growing rapidly worldwide. However, the efficacy of this system is not well known. The objective of this systematic review was to determine the effects of mobile use on heart failure management.
Methods: In this review, published studies from 2009 to January 2015 that were accessible in PubMed, ScienceDirect, Scopus and Proquest were searched and from 1021 related studies, 69 articles were identified by their titles and finally, after applying inclusion and exclusion criteria, 11 articles were selected for review.
Results: In whole, 32 outcomes had been studied in the selected papers and the review showed that in 14 cases, the intervention had led to significant positive effect and in 11 cases, the intervention had non-significant positive effect. While, in 5 cases, the intervention had no effect and in 2 cases, it had led to a negative effect.
Conclusion: According to the results of this review, using mobile phones could be beneficial in the management of Heart Failure and delivery of health services to the affected patients.
 

Prediction of 30-day Mortality for Heart Failure Patients with Cardiogenic Shock Using Random Forest Hyperparameter Tuning
Shiva Kanani, Iraj Mahdavi, Naghmeh Ziaie, Bagher Rahimpour Cami,
Volume 11, Issue 1 (6-2024)
Abstract
Introduction: Heart failure is a clinical syndrome resulting from structural or functional abnormalities of the heart, leading to reduced cardiac output or increased intracardiac pressure. When combined with cardiogenic shock, it becomes an emergency condition with a high mortality rate, necessitating immediate diagnosis and treatment. Accurate prediction of 30-day mortality in these patients is vital for timely care and patient survival. This study aimed to optimize the Random Forest algorithm by adjusting hyperparameters to more accurately predict 30-day mortality in heart failure patients with cardiogenic shock.
Method: In this research, data from 201 cardiac patients aged over 18 years who experienced cardiogenic shock at Rouhani Hospital in Babol in 2020, were used. Thirty-four selected features such as age, history of cardiac surgery, pH, lactate levels, diabetes, etc., were examined, and their one-month mortality was tracked through telephone follow-ups.
Results: The results showed that increasing age (above 57 years), decreasing pH (below 7.3), and elevating lactate levels (above 2) significantly increased the risk of 30-day mortality. By optimizing the hyperparameters of the Random Forest algorithm (ntree=1000 and mtry=14), prediction accuracy improved from 66.0% to 71.8%.
Conclusion: This study demonstrates that the accuracy of the Random Forest algorithm depends on its input hyperparameters and that optimizing these parameters can lead to a more precise prediction of mortality in heart failure patients with cardiogenic shock. With appropriate optimization, this algorithm can serve as an effective tool for the early detection of high-risk patients and timely provision.

Identification of Important Features to Predict 30-Day Mortality in Heart Failure Patients with Cardiogenic Shock
Shiva Kanani, Iraj Mahdavi, Naghmeh Ziaie, Bagher Rahimpour Cami,
Volume 11, Issue 3 (12-2024)
Abstract
Introduction: Cardiovascular diseases remain a leading global cause of mortality, with ischemic heart disease projected to account for 23.3 million deaths by 2030. Heart failure and cardiogenic shock account for a significant proportion of these deaths and require timely treatment as medical emergencies. This study aims to predict mortality within one month in patients experiencing cardiogenic shock secondary to heart failure using a concise set of predictive features.
Method: An analytical cross-sectional study was conducted at Babol Razi Hospital, involving 201 adult patients (≥18 years) treated for cardiogenic shock in 2020. Data from 34 clinical variables, including age, history of cardiac surgery, pH levels, lactate concentration, diabetes status, and blood pressure, were meticulously analyzed. Mortality outcomes within one month were assessed via structured telephone follow-up. Logistic regression and Gradient Boosting Machine (GBM) algorithms were used for predictive modeling.
Results: The average age of patients was 69.44 ±15.71 years. Among them, 47.7% died. The study identified age, lactate levels, diabetes, and initial confusion as significant predictors of mortality risk. Each additional year of age was associated with a 7% higher probability of mortality. Diabetic patients faced more than double the mortality risk compared to non-diabetics. Confusion at presentation increased the mortality risk fourfold, while elevated lactate levels raised it by 1.5 times.
Conclusion: Logistic regression and GBM algorithms demonstrated effectiveness in predicting one-month mortality among cardiogenic shock patients with heart failure based on selected features. This approach holds promise for improving referral processes and reducing costs in healthcare settings.

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