Sodium Intake and Heart Failure

Patel, Yash; Joseph, Jacob

doi:10.3390/ijms21249474

Cited by 87 publications

(59 citation statements)

References 69 publications

(75 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The study’s findings are self-evident: H01, the Survival-Status was linearly correlated with SC and SS ( p < 0.05), supporting other author reports [ 12 , 13 , 14 , 42 ]. In the case of (

, and

) refering to complications such as anemia, diabetes, and high blood pressure, SC and SS influences were not statistically significant ( p > 0.05), contradicting autor reports [ 10 , 15 ] but supporting this author report [ 42 ].…”

Section: Discussionsupporting

confidence: 85%

“…That’s why a sodium-free diet is recommended for the general public to avoid the HF disease, and also it was found to be linked with BP and hypertension [ 10 , 11 , 12 ]. Moreover, the sodium intake significantly impacts the renin, aldosterone, noradrenaline, adrenaline, cholesterol, and triglycerides [ 13 ]. Mohammed W Akhtar et al [ 14 ] found that the renal insufficiency (abnormality in SC) in HF patients also increased five times more risk of death.…”

Section: Introduction and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning

Kumar

Verma

Dahiya

et al. 2021

Sensors

View full text Add to dashboard Cite

The incidence of cardiovascular diseases and cardiovascular burden (the number of deaths) are continuously rising worldwide. Heart disease leads to heart failure (HF) in affected patients. Therefore any additional aid to current medical support systems is crucial for the clinician to forecast the survival status for these patients. The collaborative use of machine learning and IoT devices has become very important in today’s intelligent healthcare systems. This paper presents a Public Key Infrastructure (PKI) secured IoT enabled framework entitled Cardiac Diagnostic Feature and Demographic Identification (CDF-DI) systems with significant Models that recognize several Cardiac disease features related to HF. To achieve this goal, we used statistical and machine learning techniques to analyze the Cardiac secondary dataset. The Elevated Serum Creatinine (SC) levels and Serum Sodium (SS) could cause renal problems and are well established in HF patients. The Mann Whitney U test found that SC and SS levels affected the survival status of patients (p < 0.05). Anemia, diabetes, and BP features had no significant impact on the SS and SC level in the patient (p > 0.05). The Cox regression model also found a significant association of age group with the survival status using follow-up months. Furthermore, the present study also proposed important features of Cardiac disease that identified the patient’s survival status, age group, and gender. The most prominent algorithm was the Random Forest (RF) suggesting five key features to determine the survival status of the patient with an accuracy of 96%: Follow-up months, SC, Ejection Fraction (EF), Creatinine Phosphokinase (CPK), and platelets. Additionally, the RF selected five prominent features (smoking habits, CPK, platelets, follow-up month, and SC) in recognition of gender with an accuracy of 94%. Moreover, the five vital features such as CPK, SC, follow-up month, platelets, and EF were found to be significant predictors for the patient’s age group with an accuracy of 96%. The Kaplan Meier plot revealed that mortality was high in the extremely old age group (χ2 (1) = 8.565). The recommended features have possible effects on clinical practice and would be supportive aid to the existing medical support system to identify the possibility of the survival status of the heart patient. The doctor should primarily concentrate on the follow-up month, SC, EF, CPK, and platelet count for the patient’s survival in the situation.

show abstract

“…The study’s findings are self-evident: H01, the Survival-Status was linearly correlated with SC and SS ( p < 0.05), supporting other author reports [ 12 , 13 , 14 , 42 ]. In the case of (

, and

Section: Discussionsupporting

confidence: 85%

Section: Introduction and Related Workmentioning

confidence: 99%

Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning

Kumar

Verma

Dahiya

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Whereas it is often recommended that patients with chronic cardiovascular conditions follow a sodium-restricted diet, the latter often involves a poorer taste of the foods consumed, which explains the low adherence rates to a great extent [38][39][40][41]. In our study cohort, both CHF as well PAH participants consumed large amounts of sodium daily.…”

Section: Discussionmentioning

confidence: 89%

Diet in Scleroderma: Is There a Need for Intervention?

et al. 2021

View full text Add to dashboard Cite

Systemic sclerosis (SSc) patients exhibit a plethora of risk factors for nutritional decline, including the presence of chronic inflammation and the progressive nature of disease-related multisystem involvement. The prevalence and consequences of nutritional decline in scleroderma are frequently underestimated, its management currently remaining a subject of debate. The main objective of the present study was to perform a detailed assessment of scleroderma patients’ diet as well as their eating habits and to describe the relationships with weight loss and malnutrition risk in the absence of professional nutritional counseling. Methods: We used a translated and validated version of the EPIC-Norfolk FFQ (European Prospective Investigation into Cancer and Nutrition Norfolk Food Frequency Questionnaire) to evaluate the patients’ diet and MUST (Malnutrition Universal Screening Tool) to investigate the risk of malnutrition. Disease activity was estimated using the EUSTAR-AI (European Scleroderma Trials and Research group Activity Index). Results: We included 69 patients with SSc, of which 42 underwent a detailed dietary assessment. Dietary factors were connected to body composition and digestive symptoms. We found high sodium intake and frequent suboptimal energy consumption in our study group, including patients with cardiopulmonary involvement. Liver transaminases were inversely correlated with the consumption of nuts and seeds. Malnutrition and weight loss were significantly associated with pulmonary hypertension, heart failure, albumin levels, and the extent of skin fibrosis, but not advanced age. Although the patients with EUSTAR-AI ≥ 2.5 were more frequently included in the moderate and high malnutrition risk categories, these results did not reach statistical significance. Conclusions: Currently, there is an unmet need for longitudinal and interventional research focusing on the long-term significance, ramifications, and management of nutritional impairment in SSc patients with various clinical manifestations. Our results indicate that scleroderma patients could benefit from personalized nutritional counseling in an interdisciplinary setting.

show abstract

“…There is more evidence present which linking to sodium intake with BP [10], the occurrence of hypertension, [11], CVD, [12] other HF risk factors, even the latest American Heart Association guidelines for 1500 mg/d sodium tend to be sufficiently relevant for patients with stage A and B HF. Yash Patel et.al [13] have found in their review study that due to reduced renal perfusion, sodium, and water reabsorption from renal tubules, HF is characterized by sympathetic system activation and renin-angiotensin-aldosterone system (RAAS) activation. The activation of the antidiuretic and anti-natriuretic systems has been linked to a sodium-restricted diet in HF patients.…”

Section: Effect Of Abnormal Sc and Ss On Hf Patientmentioning

confidence: 99%

Cardiac Diagnostic Feature and Demographic Identification Models: A Futuristic Approach for Smart Healthcare Using Machine Learning

Kumar¹,

Verma²,

Dahiya³

et al. 2021

Preprint

View full text Add to dashboard Cite

Around the world, every year, about 17 million people death cause happen due to CardioVascular Diseases (CVD). As per clinical records, primarily sufferers exhibit myocardial infarctions and Heart Failures (HF). Creatinine is a Musculo - skeletal waste product. The kidneys filter creatinine from the blood and excrete it through the urine in a healthy body. High creatinine levels can suggest renal problems. Elevated Serum Creatinine (SC) has been well established in the HF. Patients’ electronic medical records can be used to quantify symptoms and other related clinical laboratory test values, which would then be utilized to direct biostatistics exploration to uncover patterns and associations that doctors would otherwise miss. The latest American Heart Association guidelines for 1500 mg/d sodium tend to be sufficiently relevant for patients with stage A and B with HF. In this article, we used a dataset of the year 2015 of heart patients records of 299 patients. The present paper used the data analytic and statistical tools to verify the significant differences between alive and dead patients’ SC and Serum Sodium (SS). It also demonstrates the impact of significant features on abnormal SC and SS on the Survival-Status levels. The Age-Group feature, which is derived from age attribute and, Ejection Fraction (EF), anemia, platelets, Creatinine Phosphokinase (CPK), Blood-Pressure (BP), gender, diabetes, and smoking-status were utilized to determine the potential contributing features to mortality with Cox regression model. The Kaplan Meier plot was used to investigate the overall pattern of survival concerning age-group. During pre-processing of the dataset, Age and SS were removed due to multicollinear features during performing machine learning algorithms experiments. This paper also predicted patients’ survival, age group, and gender using supervised machine learning classifiers. Detection of significant features would help in making informed decisions to balance the lifestyle of heart patients. The author revealed that the patient’s follow-up months, as well as SC, EF, CPK, and platelets, are sufficient key features to predict heart patient survival using Random Forest (RF) stratified 10-fold CV method with accuracy (96%) with 5% Standard Deviation (SD) from medical records dataset. We identified the age-group and gender of the patient, and the RF model outperformed others with the best accuracy 96% and 94% in both cases having 11% SD. Also, prominent features such as CPK, SC, follow-up month, platelets, and ejection were found to be significant factors in predicting the patient’s age-group. Smoking habits, CPK, platelets, follow-up month, and SC of each patient were discovered to be significant predictors of patient gender. The hypothetical study proved that SC and SS making substantial differences in the survival of patients (p &lt; 0.05) and failed to reject that anemia, diabetes, and BP making a significant impact on the creatinine and sodium of each patient (p &gt; 0.05). With χ2(1) = 8.565, the Kaplan Meier plot revealed that mortality was high in the extremely elder age-group. The finding has possible effects on clinical practice and becomes a new medical support system when predicting whether a patient can survive a heart attack or not. The doctor should primarily concentrate on follow-up month, SC and EF, CPK, and platelet count since the aim is to understand whether a patient survives after HF.

show abstract

Sodium Intake and Heart Failure

Cited by 87 publications

References 69 publications

Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning

Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning

Diet in Scleroderma: Is There a Need for Intervention?

Cardiac Diagnostic Feature and Demographic Identification Models: A Futuristic Approach for Smart Healthcare Using Machine Learning

Contact Info

Product

Resources

About