Disparities in prevalence of heart failure according to age, multimorbidity level and socioeconomic status in southern Sweden: a cross-sectional study

Comorbidities in heart failure patients that predict cardiovascular readmissions within 100 days-An observational study

BACKGROUND

Heart failure (HF) commonly arises as a complication to cardiovascular diseases and is closely associated with various comorbidities. The impacts of these comorbidities in patients with HF are diverse. We aimed to analyze the increased risk for cardiovascular-related readmission within 100 days after discharge in patients with HF depending on their different comorbidities.

METHODS

A population-based retrospective study was conducted in Region Halland with 5029 patients admitted to hospital with a diagnosis of HF during 2017-2019. The occurrence and number of comorbidities were recorded. Competing risk regression was employed to analyze the hazard ratio (HR) of 10 comorbidities for cardiovascular-related readmission within 100 days after discharge. A composite measure of the 10 common comorbidities was constructed with the comorbidities as dichotomous indicator variables and Rasch analysis. Receiver operating characteristic (ROC) and area under curve (AUC) after logistic regression were used to estimate how well the model explained the probability of death or readmission within 100 days after discharge according to their individual comorbidity level.

RESULTS

HF patients with atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, peripheral artery disease or diabetes mellitus as comorbidities had an increased HR for readmission within 100 days after discharge. When these comorbidities were adjusted together, only atrial fibrillation, chronic kidney disease and chronic obstructive pulmonary disease had an increased HR for readmission. ROC analysis after the most complete models using logistic regression with the comorbidities as dichotomous indicator variables or Rasch analysis had a low AUC.

CONCLUSIONS

Atrial fibrillation, chronic kidney disease or chronic obstructive pulmonary disease were significantly associated with increased risk for readmission in HF patients, but ROC analysis showed a low AUC, which indicates that other factors are more important for predicting the increased risk of readmission.

Associations of heart failure to prevalence of haematologic- and solid malignancies in southern Sweden: A cross-sectional study

BACKGROUND

Heart failure (HF) and cancer are common diseases among the elderly population. Many chronic diseases, including diabetes mellitus (DM), share risk factors and increase the incidence of HF and cancer. The aim of this study was to investigate if there was an association between HF and the prevalence of haematologic- and solid malignancies.

METHODS

The study population was comprised of almost one million adults living in southern Sweden in 2015. All participants were divided into seven age groups from 20 and onwards, and 10 percentiles according to their socioeconomic status (SES). All data concerning diagnoses from each consultation in both primary- and secondary health care were collected during 18 months. The prevalence of haematologic and solid malignancies was measured separately for men and women, age groups, SES and multimorbidity levels. Multivariable logistic regression was used to determine the associations between HF and the probability of having haematologic- and solid malignancies in more complex models including stratifying variables.

RESULTS

People with HF had a higher prevalence of haematologic- and solid malignancies than the general population, but a lower prevalence of solid malignancies than the multimorbid population. The people with HF had an increased OR for haematologic malignancies, 1.69 (95% CI 1.51-1.90), and solid malignancies, OR 1.21 (95% CI 1.16-1.26), when adjusted for gender and age. In more complex multivariate models, multimorbidity explained the increased OR for haematologic- and solid malignancies in people with HF. Increasing socioeconomic deprivation was associated with a decreased risk for solid malignancies, with the lowest risk in the most socioeconomically deprived CNI-percentile.

CONCLUSIONS

HF was shown to be associated with malignancies, especially haematologic malignancies. Multimorbidity, however, was an even more important factor for both haematologic- and solid malignancies than HF in our study, but not socioeconomic deprivation. Further research on the interactions between the chronic conditions in people with HF is warranted to examine the strength of association between HF and malignancies.

A systematic comparison of machine learning algorithms to develop and validate prediction model to predict heart failure risk in middle-aged and elderly patients with periodontitis (NHANES 2009 to 2014)

Periodontitis is increasingly associated with heart failure, and the goal of this study was to develop and validate a prediction model based on machine learning algorithms for the risk of heart failure in middle-aged and elderly participants with periodontitis. We analyzed data from a total of 2876 participants with a history of periodontitis from the National Health and Nutrition Examination Survey (NHANES) 2009 to 2014, with a training set of 1980 subjects with periodontitis from the NHANES 2009 to 2012 and an external validation set of 896 subjects from the NHANES 2013 to 2014. The independent risk factors for heart failure were identified using univariate and multivariate logistic regression analysis. Machine learning algorithms such as logistic regression, k-nearest neighbor, support vector machine, random forest, gradient boosting machine, and multilayer perceptron were used on the training set to construct the models. The performance of the machine learning models was evaluated using 10-fold cross-validation on the training set and receiver operating characteristic curve (ROC) analysis in the validation set. Based on the results of univariate logistic regression and multivariate logistic regression, it was found that age, race, myocardial infarction, and diabetes mellitus status were independent predictors of the risk of heart failure in participants with periodontitis. Six machine learning models, including logistic regression, K-nearest neighbor, support vector machine, random forest, gradient boosting machine, and multilayer perceptron, were built on the training set, respectively. The area under the ROC for the 6 models was obtained using 10-fold cross-validation with values of 0 848, 0.936, 0.859, 0.889, 0.927, and 0.666, respectively. The areas under the ROC on the external validation set were 0.854, 0.949, 0.647, 0.933, 0.855, and 0.74, respectively. K-nearest neighbor model got the best prediction performance across all models. Out of 6 machine learning models, the K-nearest neighbor algorithm model performed the best. The prediction model offers early, individualized diagnosis and treatment plans and assists in identifying the risk of heart failure occurrence in middle-aged and elderly patients with periodontitis.

Impact of Sodium–Glucose Cotransporter 2 (SGLT2) Inhibitors on Arterial Stiffness and Vascular Aging—What Do We Know So Far? (A Narrative Review)

Vascular aging, early vascular aging or supernormal vascular aging are concepts used for estimating the cardiovascular risk at a certain age. From the famous line of Thomas Sydenham that “a man is as old as his arteries” to the present day, clinical studies in the field of molecular biology of the vasculature have demonstrated the active role of vascular endothelium in the onset of cardiovascular diseases. Arterial stiffness is an important cardiovascular risk factor associated with the occurrence of cardiovascular events and a high risk of morbidity and mortality, especially in the presence of diabetes. Sodium–glucose cotransporter 2 inhibitors decrease arterial stiffness and vascular resistance by decreasing endothelial cell activation, stimulating direct vasorelaxation and ameliorating endothelial dysfunction or expression of pro-atherogenic cells and molecules.