Income Inequalities and Risk of Early Rehospitalization for Diabetes, Hypertension and Congestive Heart Failure in the Canadian Working-Age Population

Comeau, Eric; Leonard, Philip S.J.; Gupta, Neeru

doi:10.1016/j.jcjd.2021.08.007

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…The effect of income on HF has been confirmed in many studies, with low‐income groups being more likely to develop HF or have a higher mortality rate from HF than higher‐income groups, possibly because low‐income groups tend to receive more limited medical resources. 29 , 30 , 31 …”

Section: Discussionmentioning

confidence: 99%

“…The effect of income on HF has been confirmed in many studies, with low-income groups being more likely to develop HF or have a higher mortality rate from HF than higherincome groups, possibly because low-income groups tend to receive more limited medical resources. [29][30][31] Age was the second most important predictor. The expected 5-year mortality from HF in diabetic people younger than 75 years of age increased dramatically with age, according to the findings of a prospective research involving 283 patients with chronic HF.…”

Section: Influence Of Variables On Prediction Performancementioning

confidence: 97%

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Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle‐aged and older US people with prediabetes or diabetes

Wang

Hou

et al. 2023

Clinical Cardiology

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BackgroundThe purpose of this study was to develop and validate a machine learning (ML) based prediction model for the risk of heart failure (HF) in patients with prediabetes or diabetes.MethodsWe used 3527 subjects aged 40 years and older with a prior diagnosis of prediabetes or diabetes from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. The search for independent risk variables linked to HF was conducted using univariate and multivariable logistic regression analysis. The 3527 subjects were randomly divided into training set and validation set in a 7:3 ratio. Five ML models were built on the training set using five ML algorithms, including random forest (RF), and then validated on the validation set. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis and Bootstrap resampling method were used to measure the predictive performance of the five ML models.ResultsMultivariate logistic regression analysis showed that age, poverty‐to‐income ratio, myocardial infarction condition, coronary heart disease condition, chest pain condition, and glucose‐lowering medication use were independent predictors of HF. By comparing the performance of the five ML models, the RF model (AUC = 0.978) was the best prediction model.ConclusionsThe risk of HF in middle‐aged and elderly patients with prediabetes or diabetes can be accurately predicted using ML models. The best prediction performance is presented by RF model, which can assist doctors in making clinical decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Influence Of Variables On Prediction Performancementioning

confidence: 97%

Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle‐aged and older US people with prediabetes or diabetes

Wang

Hou

et al. 2023

Clinical Cardiology

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Socio‐economic inequalities and heart failure morbidity and mortality: A systematic review and data synthesis

Shakoor,

van Maarschalkerwaart,

Schaap

et al. 2024

ESC Heart Failure

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Socio‐economic status (SES) has been associated with incident and prevalent heart failure (HF), as well as its morbidity and mortality. However, the precise nature of the relationship between SES and HF remains unclear due to inconsistent data. This study aims to provide a comprehensive assessment and data synthesis of the relationship between SES and HF morbidity and mortality. We performed a systematic search and data synthesis using six databases following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses Guidelines. The included studies comprised observational studies that reported on HF incidence and prevalence, HF hospitalizations, worsening HF (WHF) and all‐cause mortality, as well as treatment options (medical, device and advanced HF therapies). SES was measured on both individual and area levels, encompassing single (e.g., income, education, employment, social risk score, living conditions and housing characteristics) and composite indicators. Among the 4124 studies screened, 79 were included, with an additional 5 identified through cross‐referencing. In the majority of studies, a low SES was associated with an increased HF incidence (72%) and prevalence (75%). For mortality, we demonstrated that low SES was associated with increased mortality in 45% of the studies, with 18% of the studies showing mixed results (depending on the indicator, gender or follow‐up) and 38% showing non‐significant results. Similar patterns were observed for the association between SES, WHF, medical therapy prescriptions and the utilization of devices and advanced HF therapies. There was no clear pattern in the used SES indicators and HF outcomes. This systematic review, using contemporary data, shows that while socio‐economic disparity may influence HF incidence, management and subsequent adverse events, these associations are not uniformly predictive. Our review highlights that the impact of SES varies depending on the specific indicators used, reflecting the complexity of its influence on health disparities. Assessment and recognition of SES as an important risk factor can assist clinicians in early detection and customizing HF treatment, while also aiding policymakers in optimizing resource allocation.

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Income Inequalities and Risk of Early Rehospitalization for Diabetes, Hypertension and Congestive Heart Failure in the Canadian Working-Age Population

Cited by 2 publications

References 39 publications

Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle‐aged and older US people with prediabetes or diabetes

Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle‐aged and older US people with prediabetes or diabetes

Socio‐economic inequalities and heart failure morbidity and mortality: A systematic review and data synthesis

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