Factors Affecting Chronic Heart Failure in Patients with End-Stage Renal Disease at Bhayangkara Hospital Denpasar

Dewi, Ni Gusti Ayu Putu Lestari Santika; Yasmin, Aiman; Wulan, Ni Made Citra Riesti; Natanagara, I Gede Catur Wira

doi:10.37275/bsm.v6i7.545

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…By inhibiting beta-receptor activation leading to decreased inotropy and sinus velocity and slowing atrioventricular (AV) conduction, beta-blockers are effective for relieving angina symptoms and preventing ischemic events. [12][13][14][15] Reduction in resting and exercise heart rate, contractility, and arterial pressure with beta-blockers can lead to decreased myocardial oxygen demand. 13 Decreasing heart rate increases diastolic perfusion time, which can increase left ventricular (LV) perfusion.…”

Section: Methodsmentioning

confidence: 99%

“…[12][13][14][15] Reduction in resting and exercise heart rate, contractility, and arterial pressure with beta-blockers can lead to decreased myocardial oxygen demand. 13 Decreasing heart rate increases diastolic perfusion time, which can increase left ventricular (LV) perfusion. [14][15][16][17] However, there are also some side effects of beta-blockers, such as physical and mental fatigue, nightmares, nausea, impotence, cold extremities, and decreased patient adherence to taking medication.…”

Section: Methodsmentioning

confidence: 99%

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Effectiveness of Nitrate and Beta-Blocker Drugs in Patients with Angina Pectoris: A Systematic Literature Review

Maya Arfania,

Adinda Khoirun Nissa,

Khoirul Haniatin

et al. 2023

Eu Herb Ina

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Angina pectoris is the most common clinical manifestation of myocardial ischemia and often occurs when the heart needs more blood. The main goals of treatment in patients with unstable angina are relief of symptoms, slowing disease progression, and reducing future events, especially myocardial infarction, and death. This study aimed to explore the literature regarding the effectiveness of nitrate and beta-blocker drugs in angina pectoris patients. The literature search process was carried out on various databases (PubMed, Web of Sciences, EMBASE, Cochrane Libraries, and Google Scholar) regarding risk factors and clinical overview of acute kidney injury. The search was performed using the terms: (1) "nitric oxide" OR "beta-blockers" OR "morbidity" OR "mortality" AND (2) "angina pectoris". The literature is limited to clinical studies and published in English. Sublingual nitroglycerin has become the mainstay of treatment for angina pectoris. This drug can be used to relieve acute angina or as a prophylaxis, namely before activities that can trigger angina. Beta-blockers are a type of drug that can be used to relieve angina symptoms and prevent ischemic events through the mechanism of reducing myocardial oxygen demand, reducing heart rate, and myocardial contractility. These drugs work by competitively inhibiting the action of circulating catecholamines at cell membrane beta-adrenergic receptors. In conclusion, nitrate and beta-blocker drugs are the main choices in relieving angina pectoris symptoms.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Effectiveness of Nitrate and Beta-Blocker Drugs in Patients with Angina Pectoris: A Systematic Literature Review

Maya Arfania,

Adinda Khoirun Nissa,

Khoirul Haniatin

et al. 2023

Eu Herb Ina

View full text Add to dashboard Cite

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“…According to previous studies the most important behavioral risk factors of CVDs, particularly for heart disease and stroke, are unhealthy diet, physical inactivity, tobacco use and harmful use of alcohol [8]. Dyslipidemia, tobacco use, diabetes, hypertension and overweight are also reported as potential risk factors for heart failure in previous studies [9], [10]. Another study conducted by Hossain et al (2023) found that age, gender, smoking, obesity, diet, physical activity, stress, chest pain type, previous chest pain, blood pressure diastolic, diabetes, and troponin are the most important factors for identifying the CVD risk [11].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach to Predict Cardiovascular Disease in Bangladesh: Evidence from a Cross-Sectional Study in 2023.

Hossain,

Hasan,

Faruk

et al. 2023

Preprint

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Background Cardiovascular disorders (CVDs) are widely considered the leading cause of death worldwide. Lower and middle-income countries (LMICs) like Bangladesh are also affected by several types of CVDs such as heart failure and stroke. The leading factors of death in Bangladesh have increasingly switched from severe infections and parasitic illness to CVDs recently. Materials and methods The study dataset is a random sample of the 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling. Moreover, 260 data are also collected from individuals with no CVD problem for comparison purposes. Crosstabs and chi-square are used to find the association between CVD and explanatory variables. Logistic regression, Naïve Bayes classifier, Decision Tree, AdaBoost classifier, Random Forest, Bagging Tree, and Ensemble learning classifiers are used to predict CVD in this study. The performance evaluations encompassed accuracy, sensitivity, specificity, and the area under the receiver operator characteristic (AU-ROC) curve. Result Random Forest has the highest precision among the five techniques considered. The precision rates for the mentioned classifiers are as follows: Logistic Regression (93.67%), Naïve Bayes (94.87%), Decision Tree (96.1%), AdaBoost (94.94%), Random Forest (96.15%), and Bagging Tree (94.87%). The Random Forest classifier maintains the highest balance between correct and incorrect predictions. With 98.04% accuracy, the Random Forest Classifier achieves the best precision (96.15%), robust recall (100%), and a high F1 score (97.7%). In contrast, the Logistic Regression model achieves the lowest accuracy at 95.42%. Remarkably, the Random Forest classifier attains the highest AUC value (0.989). Conclusion This research is mainly focused on identifying factors that are critical in impacting CVD patients and predicting CVD risk. It is strongly advised that the Random Forest technique be implemented in the system for predicting cardiac disease. This research may change clinical practice by giving doctors a new instrument to determine a patient's prognosis for CVD.

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Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023

Hossain,

Hasan,

Faruk

et al. 2024

BMC Cardiovasc Disord

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Background Cardiovascular disorders (CVDs) are the leading cause of death worldwide. Lower- and middle-income countries (LMICs), such as Bangladesh, are also affected by several types of CVDs, such as heart failure and stroke. The leading cause of death in Bangladesh has recently switched from severe infections and parasitic illnesses to CVDs. Materials and methods The study dataset comprised a random sample of 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling. Moreover, 260 data points were collected from individuals with no CVD problems for comparison purposes. Crosstabs and chi-square tests were used to determine the association between CVD and the explanatory variables. Logistic regression, Naïve Bayes classifier, Decision Tree, AdaBoost classifier, Random Forest, Bagging Tree, and Ensemble learning classifiers were used to predict CVD. The performance evaluations encompassed accuracy, sensitivity, specificity, and area under the receiver operator characteristic (AU-ROC) curve. Results Random Forest had the highest precision among the five techniques considered. The precision rates for the mentioned classifiers are as follows: Logistic Regression (93.67%), Naïve Bayes (94.87%), Decision Tree (96.1%), AdaBoost (94.94%), Random Forest (96.15%), and Bagging Tree (94.87%). The Random Forest classifier maintains the highest balance between correct and incorrect predictions. With 98.04% accuracy, the Random Forest classifier achieved the best precision (96.15%), robust recall (100%), and high F1 score (97.7%). In contrast, the Logistic Regression model achieved the lowest accuracy of 95.42%. Remarkably, the Random Forest classifier achieved the highest AUC value (0.989). Conclusion This research mainly focused on identifying factors that are critical in impacting patients with CVD and predicting CVD risk. It is strongly advised that the Random Forest technique be implemented in a system for predicting cardiac diseases. This research may change clinical practice by providing doctors with a new instrument to determine a patient’s CVD prognosis.

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Factors Affecting Chronic Heart Failure in Patients with End-Stage Renal Disease at Bhayangkara Hospital Denpasar

Cited by 3 publications

References 25 publications

Effectiveness of Nitrate and Beta-Blocker Drugs in Patients with Angina Pectoris: A Systematic Literature Review

Effectiveness of Nitrate and Beta-Blocker Drugs in Patients with Angina Pectoris: A Systematic Literature Review

Machine Learning Approach to Predict Cardiovascular Disease in Bangladesh: Evidence from a Cross-Sectional Study in 2023.

Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023

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