Prognostic Impact of Diabetes on Long-term Survival Outcomes in Patients With Heart Failure: A Meta-analysis

Dauriz, Marco; Mantovani, Alessandro; Bonapace, Stefano; Verlato, Giuseppe; Zoppini, Giacomo; Bonora, Enzo; Targher, Giovanni

doi:10.2337/dc17-0697

Cited by 91 publications

(69 citation statements)

References 77 publications

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“…A recent meta‐analysis of 31 registries and 12 clinical trials with 381 725 patients with acute and chronic HF, with a median follow‐up of 3 years confirms that T2DM is independently associated with a higher risk of all‐cause death (random‐effects HR 1.28), CV death (HR 1.34), hospitalization (HR 1.35), and the combined endpoint of all‐cause death or hospitalization (HR 1.41), and the observed long‐term risk appears greater in patients with chronic than in those with acute HF …”

Section: Type 2 Diabetes Mellitus Clinical Status and Outcomes In Pamentioning

confidence: 98%

Type 2 diabetes mellitus and heart failure: a position statement from the Heart Failure Association of the European Society of Cardiology

Seferović

Petrie

Filippatos

et al. 2018

European J of Heart Fail

509

379

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The coexistence of type 2 diabetes mellitus (T2DM) and heart failure (HF), either with reduced (HFrEF) or preserved ejection fraction (HFpEF), is frequent (30-40% of patients) and associated with a higher risk of HF hospitalization, all-cause and cardiovascular (CV) mortality. The most important causes of HF in T2DM are coronary artery disease, arterial hypertension and a direct detrimental effect of T2DM on the myocardium. T2DM is often unrecognized in HF patients, and vice versa, which emphasizes the importance of an active search for both disorders in the clinical practice. There are no specific limitations to HF treatment in T2DM. Subanalyses of trials addressing HF treatment in the general population have shown that all HF therapies are similarly effective regardless of T2DM. Concerning T2DM treatment in HF patients, most guidelines currently recommend metformin as the first-line choice. Sulphonylureas and insulin have been the traditional second- and third-line therapies although their safety in HF is equivocal. Neither glucagon-like preptide-1 (GLP-1) receptor agonists, nor dipeptidyl peptidase-4 (DPP4) inhibitors reduce the risk for HF hospitalization. Indeed, a DPP4 inhibitor, saxagliptin, has been associated with a higher risk of HF hospitalization. Thiazolidinediones (pioglitazone and rosiglitazone) are contraindicated in patients with (or at risk of) HF. In recent trials, sodium-glucose co-transporter-2 (SGLT2) inhibitors, empagliflozin and canagliflozin, have both shown a significant reduction in HF hospitalization in patients with established CV disease or at risk of CV disease. Several ongoing trials should provide an insight into the effectiveness of SGLT2 inhibitors in patients with HFrEF and HFpEF in the absence of T2DM.

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Section: Type 2 Diabetes Mellitus Clinical Status and Outcomes In Pamentioning

confidence: 98%

Type 2 diabetes mellitus and heart failure: a position statement from the Heart Failure Association of the European Society of Cardiology

Seferović

Petrie

Filippatos

et al. 2018

European J of Heart Fail

509

379

View full text Add to dashboard Cite

show abstract

“…A recent metaanalysis, including 31 registries and 12 clinical trials (combining studies of both acute and chronic heart failure), concluded that diabetes mellitus is independently associated with a higher risk of all-cause death (HR 1.28; 95% CI, 1.21-1.35). 73 Diabetes likely has deleterious effects in all forms of heart failure, including HFrEF and HFpEF, and both ischemic and nonischemic cardiomyopathy. Therefore, it is important to identify and treat risk factors for diabetes early to avoid the morbidity and mortality associated with diabetes and associated conditions, especially heart failure.…”

Section: S8mentioning

confidence: 99%

Heart Failure and Diabetes Mellitus: Defining the Problem and Exploring the Interrelationship

Zadourian

Taub

2019

The American Journal of Cardiology

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Type 2 diabetes mellitus and congestive heart failure are highly prevalent diseases with significant morbidity and mortality. These 2 diseases often occur concurrently because of shared risk factors such as coronary artery disease, and also because type 2 diabetes mellitus has direct cardiotoxic effects. Type 2 diabetes mellitus likely has a causative role in the development and prognosis of patients with heart failure. Optimal prevention and treatment of type 2 diabetes mellitus and heart failure likely involves identifying and treating their shared pathophysiologic features. Novel drug therapies, such as sodium-glucose co-transporter 2 inhibitors, offer an exciting potential to better understand the relationship between type 2 diabetes mellitus and heart failure, and may prove to have beneficial effects on cardiovascular outcomes in patients affected by these diseases.

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“…Such uncertainty has led to the proliferation of new risk scores appearing in the literature in the last years, with mixed results [41][42][43][44][45][46][47]. As a partial solution to improve models' effectiveness, recent published studies included cohorts restricted to specific classes of patients (for example, elderly or diabetic) [48,49]. These attempts have led to tailored models and risk scores [50,51] with better but still not optimal performance.…”

Section: Introductionmentioning

confidence: 99%

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Chicco

Jurman

2020

BMC Med Inform Decis Mak

379

171

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Background: Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body. Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records. Methods: In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. Results: Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival. Conclusions: This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.

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Prognostic Impact of Diabetes on Long-term Survival Outcomes in Patients With Heart Failure: A Meta-analysis

Abstract: This updated meta-analysis shows that the presence of diabetes per se adversely affects long-term survival and risk of hospitalization in patients with acute and chronic HF.

Cited by 91 publications

References 77 publications

Type 2 diabetes mellitus and heart failure: a position statement from the Heart Failure Association of the European Society of Cardiology

Type 2 diabetes mellitus and heart failure: a position statement from the Heart Failure Association of the European Society of Cardiology

Heart Failure and Diabetes Mellitus: Defining the Problem and Exploring the Interrelationship

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

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