Dataset Information

A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus.

ABSTRACT:

Background

Antidiabetic therapies have shown disparate effects on hospitalization for heart failure (HHF) in clinical trials. This study developed a prediction model for HHF in type 2 diabetes mellitus (T2DM) using real world data to identify patients at high risk for HHF.

Hypothesis

Type 2 diabetics at high risk for HHF can be identified using information generated during usual clinical care.

Methods

This electronic medical record- (EMR-) based retrospective cohort study included patients with T2DM free of HF receiving healthcare through a single, large integrated healthcare system. The primary endpoint was HHF, defined as a hospital admission with HF as the primary diagnosis. Cox regression identified the strongest predictors of HHF from 80 candidate predictors derived from EMRs. High risk patients were defined according to the 90th percentile of estimated risk.

Results

Among 54,452 T2DM patients followed on average 6.6?years, estimated HHF rates at 1, 3, and 5?years were 0.3%, 1.1%, and 2.0%. The final 9-variable model included: age, coronary artery disease, blood urea nitrogen, atrial fibrillation, hemoglobin A1c, blood albumin, systolic blood pressure, chronic kidney disease, and smoking history (c = 0.782). High risk patients identified by the model had a >5% probability of HHF within 5?years.

Conclusions

The proposed model for HHF among T2DM demonstrated strong predictive capacity and may help guide therapeutic decisions.

SUBMITTER: Williams BA

PROVIDER: S-EPMC7068070 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus.

Williams Brent A BA Geba Daniela D Cordova Jeanine M JM Shetty Sharash S SS

Clinical cardiology 20191214 3

<h4>Background</h4>Antidiabetic therapies have shown disparate effects on hospitalization for heart failure (HHF) in clinical trials. This study developed a prediction model for HHF in type 2 diabetes mellitus (T2DM) using real world data to identify patients at high risk for HHF.<h4>Hypothesis</h4>Type 2 diabetics at high risk for HHF can be identified using information generated during usual clinical care.<h4>Methods</h4>This electronic medical record- (EMR-) based retrospective cohort study i ...[more]

PMID: 31837035

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Project description:Diabetes and heart failure are closely related: patients with diabetes have an increased risk of developing heart failure and those with heart failure are at higher risk of developing diabetes. Furthermore, antidiabetic medications increase the risk of mortality and hospitalisation for heart failure in patients with and without pre-existing heart failure. When the two diseases are considered individually, heart failure has a much poorer prognosis than diabetes mellitus; therefore heart failure has to be a priority for treatment in patients presenting with the two conditions, and the diabetic patient with heart failure should be managed by the heart failure team. No specific randomised clinical trials have been conducted to test the effect of cardiovascular drugs in diabetic patients with heart failure, but a wealth of evidence suggests that all interventions effective at improving prognosis in patients with heart failure are equally beneficial in patients with and without diabetes. The negative effect of glucose-lowering agents in patients with heart failure or at increased risk of heart failure has become evident after the withdrawal of rosiglitazone, a thiazolidinedione, from the EU market due to evidence of increased risk of cardiovascular events and hospitalisations for heart failure. An important issue that remains unresolved is the optimal target level of glycated haemoglobin, as recent studies have demonstrated significant reductions in total mortality, morbidity and risk of heart failure despite achieving HbA1c levels similar to those observed in the UKPDS study conducted some decades ago. Meta-analyses showed that intensive glucose lowering is not associated with any significant reduction in cardiovascular risk but conversely results in a significant increase in heart failure risk. Different medications have different risk: benefit ratios in diabetic patients with heart failure; therefore, the heart failure team must judge the required intensity of glycaemic control, the type and dose of glucose lowering agents and any change in glucose-lowering therapy, according to the clinical conditions present.

Dataset Information

A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus.

Background

Hypothesis

Methods

Results

Conclusions

Publications

A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus.

Similar Datasets

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