Development and validation of a nomogram model for all-cause mortality risk in patients with chronic heart failure and atrial fibrillation.

BACKGROUND: As the global aging process continues to accelerate, heart failure (HF) has become an important cause of increased morbidity and mortality in elderly patients. Chronic atrial fibrillation (AF) is a major risk factor for HF. Patients with HF combined with AF are more difficult to treat and have a worse prognosis. The aim of this study was to explore the risk factors for 1-year mortality in patients with HF combined with AF and to develop a risk prediction assessment model. METHODS: We recruited hospitalized patients with HF and AF who received standardized care in the Department of Cardiology at Shengjing Hospital of China Medical University from January 2013 to December 2018. The patients were randomly divided into modeling and internal validation groups using a random number generator at a 1:1 ratio. Multivariate Cox regression analysis was used to identify risk factors for all-cause mortality during a one-year follow-up period. Then, a nomogram was constructed based on the weights of each index and validated. Receiver operating characteristic curve, the area under the curve (AUC), decision curve, and calibration curve analyses for survival were used to evaluate the model's predictive and clinical validities and calibration. RESULTS: We included 3,406 patients who met the eligibility criteria; 1,703 cases each were included in the modeling and internal validation groups. Eight statistically significant predictors were identified: age, sex, New York Heart Association cardiac function class III or IV, a history of myocardial infarction, and the albumin, triglycerides, N-terminal pro-b-type natriuretic peptide, and blood urea nitrogen levels. The AUCs were 0.793 (95% confidence interval: 0.763-0.823) and 0.794 (95% confidence interval: 0.763-0.823) in the modeling and validation cohorts, respectively. CONCLUSIONS: We present a predictive model for all-cause mortality in patients with coexisting HF and AF comprising eight key factors. This model gives clinicians a simple assessment tool that may improve the clinical management of these patients.

Effects of Empagliflozin on Gut Microbiota in Heart Failure with a Preserved Ejection Fraction: The Design of a Pragmatic Randomized, Open-Label Controlled Trial (EMPAGUM).

Although empagliflozin has been recommended for individuals with heart failure, its effects on heart failure with preserved ejection fraction (HFpEF) remain uncertain from a physiopathological standpoint. The metabolites produced by gut microbiota have been shown to have a crucial role in the development of heart failure. Sodium-glucose cotransporter-2 inhibitors (SGLT2) have been shown to change the make-up of the gut microbiota in rodent studies. There is mixed evidence from similar studies investigating whether or not SGLT2 can affect the microbiota in the human gut. This trial is a pragmatic, randomized, open-label controlled study with empagliflozin as an intervention. We will enroll 100 patients with HFpEF and randomly assign them to one of two groups to receive either empagliflozin or a placebo. Patients in the Empagliflozin group will be given 10 mg of the drug daily, while those in the Control group will not be given empagliflozin or any other SGLT2. The purpose of the trial is to validate the changes that occur in gut microbiota in patients with HFpEF who take empagliflozin and to investigate the function of gut microbiota and their metabolites in the process.

Risk prediction model of in-hospital mortality in heart failure with preserved ejection fraction and mid-range ejection fraction: a retrospective cohort study.

Aim: To develop and validate internally a multivariate risk model for predicting the in-hospital mortality of patients with heart failure with preserved ejection fraction (HFpEF) and heart failure with mid-range ejection fraction (HFmrEF). Methods & results: The clinical data of 8172 inpatients with HFpEF and HFmrEF was used to establish a retrospective database. These patients, among whom 307 in-hospital deaths (3.8%) occurred, were randomly assigned to derivation and verification cohort. Among the extracted data from the derivation cohort were nine variables significantly related to in-hospital mortality, which were scored 0-4, for a total score of 24, which allowed formation of a risk predictive model. The verification cohort was then used to validate the discrimination and calibration capacities of this predictive model: the area under curve equaled 0.8575 (0.8285, 0.8865) for the derivation cohort, and 0.8323 (0.7999, 0.8646) for the verification cohort. According to this risk score, we divided patients into four risk classes (low-, medium-, high- and extremely high-risk) and revealed that the risk of in-hospital mortality increased with increasing risk class with an obvious linear relationship between actual and predicted mortality (r = 0.998, p < 0.001). Conclusion: The model based on nine common clinical variables should provide an accurate prediction of in-hospital mortality and appears to be a reliable risk classification system for patients with HFpEF and HFmrEF.