Cardiovascular biomarkers predict post-discharge re-hospitalization risk and mortality among Swedish heart failure patients

Prediction of 90 day readmission in heart failure with preserved ejection fraction by interpretable machine learning

AIMS

Certain critical risk factors of heart failure with preserved ejection fraction (HFpEF) patients were significantly different from those of heart failure with reduced ejection fraction (HFrEF) patients, resulting in the limitations of existing predictive models in real-world situations. This study aimed to develop a machine learning model for predicting 90 day readmission for HFpEF patients.

METHODS AND RESULTS

Data were extracted from electronic health records from 1 August 2020 to 1 August 2021 and follow-up records of patients with HFpEF within 3 months after discharge. Feature extraction was performed by univariate analysis combined with the least absolute shrinkage and selection operator (LASSO) algorithms. Machine learning models like eXtreme Gradient Boosting (XGBoost), random forest, neural network and logistic regression were adopted to construct models. The discrimination and calibration of each model were compared, and the Shapley Additive exPlanations (SHAP) method was used to explore the interpretability of the model. The cohort included 746 patients, of whom 103 (13.8%) were readmitted within 90 days. XGBoost owned the best performance [area under the curve (AUC) = 0.896, precision-recall area under the curve (PR-AUC) = 0.868, sensitivity = 0.817, specificity = 0.837, balanced accuracy = 0.827]. The Kolmogorov-Smirnov (KS) statistic was 0.694 at 0.468 in the XGBoost model. SHAP identified the top 12 risk features, including activities of daily living (ADL), left atrial dimension (LAD), left ventricular end-diastolic diameter (LVDD), shortness, nitrates, length of stay, nutritional risk, fall risk, accompanied by other symptoms, educational level, anticoagulants and edema.

CONCLUSIONS

Our model could help medical agencies achieve the early identification of 90 day readmission risk in HFpEF patients and reveal risk factors that provide valuable insights for treatments.

Value of the triglyceride-glucose index and related parameters in heart failure patients

The triglyceride-glucose (TyG) index, proven to be a crucial insulin resistance biomarker (better than the Homeostasis Model Assessment for Insulin Resistance), is simple and non-invasive. Recently, indisputable evidence has shown that the TyG index is strongly associated with cardiovascular disease [CVD, including atherosclerosis, heart failure (HF), and hypertension] prognosis and mortality. Nevertheless, the value of the TyG index in HF patients treated with sodium-glucose cotransporter 2 inhibitors (SGLT2is) has not been systematically evaluated. Therefore, in this review, we summarized the value of the TyG index and its related parameters as markers of CVD, especially HF. Furthermore, we addressed the use of SGLT2is and GLP-1 receptor antagonists in HF patients. Finally, we summarized the mechanism of the "obesity paradox."

Biomarkers in heart failure: a focus on natriuretic peptides

While progress has been made in the management of most aspects of cardiovascular disease, the incidence and prevalence of heart failure (HF) remains high. HF affects around a million people in the UK and has a worse prognosis than most cancers. Patients with HF are often elderly with complex comorbidities, making accurate assessment of HF challenging. A timely diagnosis and initiation of evidence-based treatments are key to prevent hospitalisation and improve outcomes in this population. Biomarkers have dramatically impacted the way patients with HF are evaluated and managed. The most studied biomarkers in HF are natriuretic peptides (NPs). Since their discovery in the 1980s, there has been an explosion of work in the field of NPs and they have become an important clinical tool used in everyday practice to guide diagnosis and prognostic assessment of patients with HF. In this article, we will review the physiology of NPs and study their biological effects. Then, we will discuss the role of NPs in the diagnosis, management and prognostication of patients with HF. We will also explore the role of NPs as a potential therapeutic agent.

Prognostic effect of systematic geriatric assessment on patients with acute heart failure

AIMS

Frailty and dependence are frequent in patients admitted for acute heart failure (AHF), but their prognostic significance is unknown, especially in young adults. We aimed to study in adults admitted for AHF, regardless of age, the effect of frailty and dependence on the incidence of mortality and a combined event of mortality, readmissions for AHF, and visits to the emergency room (ER) for AHF at 1 and 6 months.

METHODS AND RESULTS

We designed a prospective cohort study by including all the patients with AHF admitted in our Cardiology Department from July 2020 through May 2021. A multidimensional geriatric assessment was performed during the admission. We clinically followed up the patients 6 months after discharge. We enrolled 202 patients. The mean age was 73 ± 12.32 years, and 100 (49.5%) of the patients were elderly (>75 years). Just 78 patients (38.6%) were women, and 100 (49.5%) had previous HF. Frailty (FRAIL ≥ 3) was observed in 68 (33.7%) patients (mean FRAIL score: 1.88 ± 1.48). Dependence (Barthel < 100) was observed in 65 (32.2%) patients (mean Barthel index: 94.38 ± 11.21). Frailty and dependence showed a significant association with both prognostic events at 1 and 6 months. In the multivariable analysis, frailty was associated with higher mortality at 1 month [hazard ratio (HR) 12.61, 95% confidence interval (CI) 1.57-101.47, P = 0.017] but not at 6 months (HR 2.25, 95% CI 0.61-8.26, P = 0.224) or with the combined endpoint at neither 1 month (HR 1.64, 95% CI 0.54-5.03, P = 0.384) nor 6 months (HR 1.35, 95% CI 0.75-2.46, P = 0.320). Dependence was related to higher mortality at 1 month (HR 13.04, 95% CI 1.62-104.75, P = 0.016) and 6 months (HR 7.18, 95% CI 1.99-25.86, P = 0.003) and to higher incidence of the combined event at 1 month (HR 5.93, 95% CI 1.63-21.50, P = 0.007) and 6 months (HR 2.62, 95% CI 1.49-4.61, P = 0.001).

CONCLUSIONS

In AHF patients, frailty and dependence implied a worse prognosis, rising mortality, readmissions, and ER visits for AHF.

Risk factors of readmission and the impact of outpatient management in heart failure patients: A national study in Japan

AIMS

Heart failure is a significant disease, and its high readmission rate is a big concern. We must identify readmission risk factors and optimize outpatient management to prevent them. This study aims to investigate the readmission risk factors, including outpatient management represented by the number of outpatient visits, and to identify the factors related to frequent outpatient visits.

METHODS AND RESULTS

We used the diagnosis-procedure-combination database between April 2016 and March 2022. Based on the number of outpatient visits within 60 days after discharge, we categorized patients into <1 visits/month, (1<, ≦2) visits/month, and <2 visits/month and observed the occurrence of 60 days readmission. We performed multiple logistic regression analyses to reveal the readmission risk factors and the association between the number of outpatient visits and readmission. As a subgroup analysis, we conducted the same research in the low- and high-readmission risk groups. We compared medical contents between (1<, ≦2) visits/month and <2 visits/month. We analysed 101 239 patients and identified the following factors as a risk of readmission: older age (P < 0.001), female (P = 0.009), longer length-of-hospital-stay (P < 0.001), artificial ventilator (P < 0.001), tolvaptan (P < 0.001), top 50% dosage of loop diuretics (P = 0.036), bottom 50% dosage of class III antiarrhythmic agents (P < 0.001), hypertension (P = 0.005), atrial fibrillation (P < 0.001), dilated cardiomyopathy (P < 0.001), valvular disease (P = 0.021), myocardial infarction (P < 0.001), diabetes (P < 0.001), and renal disease (P < 0.001). We revealed that the risk of readmission increases in <2 visits/month compared to (1<, ≦2) visits/month (P < 0.001), whereas the risk of readmission decreases in ≦1 visits/month compared with (1<, ≦2) visits/month (P < 0.001). In the subgroup analysis, we found the possibility that some risk factors are specific to the subgroup. We identified that the following factors were related to frequent outpatient visits: older age (P < 0.001), home medical care (P = 0.007), tolvaptan (P < 0.001), top 50% dosage of loop diuretics (P < 0.001), diabetes (P < 0.001), renal disease (P = 0.009), 0-2 weeks follow-up (P < 0.001), 2-4 weeks follow-up (P < 0.001), cardiac rehabilitation (P < 0.001), and echocardiography (P < 0.001).

CONCLUSIONS

This study comprehensively identified risk factors for readmission and found outpatient visit is personalized by readmission risk. There is still room to optimize outpatient management. We suggest optimizing outpatient management according to our identified characteristics.

Development of a prediction model for the risk of 30-day unplanned readmission in older patients with heart failure: A multicenter retrospective study

BACKGROUND AND AIM

Heart failure (HF) imposes significant global health costs due to its high incidence, readmission, and mortality rate. Accurate assessment of readmission risk and precise interventions have become important measures to improve health for patients with HF. Therefore, this study aimed to develop a machine learning (ML) model to predict 30-day unplanned readmissions in older patients with HF.

METHODS AND RESULTS

This study collected data on hospitalized older patients with HF from the medical data platform of Chongqing Medical University from January 1, 2012, to December 31, 2021. A total of 5 candidate algorithms were selected from 15 ML algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC) and accuracy. Then, the 5 candidate algorithms were hyperparameter tuned by 5-fold cross-validation grid search, and performance was evaluated by AUC, accuracy, sensitivity, specificity, and recall. Finally, an optimal ML model was constructed, and the predictive results were explained using the SHapley Additive exPlanations (SHAP) framework. A total of 14,843 older patients with HF were consecutively enrolled. CatBoost model was selected as the best prediction model, and AUC was 0.732, with 0.712 accuracy, 0.619 sensitivity, and 0.722 specificity. NT.proBNP, length of stay (LOS), triglycerides, blood phosphorus, blood potassium, and lactate dehydrogenase had the greatest effect on 30-day unplanned readmission in older patients with HF, according to SHAP results.

CONCLUSIONS

The study developed a CatBoost model to predict the risk of unplanned 30-day special-cause readmission in older patients with HF, which showed more significant performance compared with the traditional logistic regression model.

Fast systematic geriatric assessment in acute heart failure patients admitted in Cardiology

BACKGROUND

Heart failure prevalence is increasing in elder adults. These patients usually present geriatric syndromes, especially frailty. The effect of frailty on heart failure is under discussion but there are few data about the clinical characterization of frail patients who are admitted for acute heart failure decompensation.

OBJECTIVE

The purpose of this study was to study the differences in clinical baseline variables and geriatric scales between frail and non-frail patients admitted to the Cardiology unit via the Emergency Department for acute heart failure.

METHODS

We enrolled all patients with acute heart failure who were admitted to the Cardiology unit from the Emergency Department of our hospital from July 2020 through May 2021. A multidimensional and comprehensive geriatric assessment was performed at the moment of admission. We studied differences in baseline variables and geriatric scales according to the frailty status determined by the FRAIL scale.

RESULTS

A total of 202 patients were included. In the whole population, 68 (33.7%) patients presented frailty defined by a FRAIL score ≥ 3. The frail patients were older (80±9 vs. 69±12 years; p<0.001), and had a worse quality of life (58.31±12.18 vs.39.26±13.71 points; p<0.001) according to the Minnesota scale, presented high comorbidity (47 (69.1%) vs. 67 (50.4%) patients; p = 0.011) defined as ≥3 points according to the Charlson scale and were more dependent (40 (58.8%) vs. 25 (18.8%) patients; p<0.001) according to the Barthel scale. The frail patients presented higher MAGGIC risk scores (24.09±4.99 vs. 18.89±6.26; p<0.001). Despite this adverse profile, the treatments prescribed during the admission and at the hospital discharge were similar.

CONCLUSIONS

The prevalence of geriatric syndromes, especially frailty, is very high in patients admitted for acute heart failure. Frail patients with acute heart failure had an adverse clinical profile with more prevalence of concomitant geriatric syndromes. Therefore, we consider that a geriatric assessment should be performed during the admission of acute heart failure patients to improve care and attention.

Role of copeptin in diagnosis and outcome prediction in patients with heart failure: a systematic review and meta-analysis

Introduction: This systematic review and meta-analysis of 19 studies, was conducted to evaluate the role of copeptin in diagnosis and outcome prediction in HF patients. Materials and Methods: A systematic literature search for clinical trials reporting copeptin levels in HF patients was performed using EMBASE, PubMed, Cochrane Register of Controlled Trials, and Google Scholar. Articles from databases published by January 2nd, 2022, that met the selection criteria were retrieved and reviewed. The random effects model was used for analyses. Results: Pooled analysis found higher mean copeptin levels in HF vs. non-HF populations (43.6 ± 46.4 vs. 21.4 ± 21.4; MD= 20.48; 95%CI: 9.22 to 31.74; p < 0.001). Pooled analysis of copeptin concentrations stratified by ejection fraction showed higher concentrations in HFrEF vs. HFpEF (17.4 ± 7.1 vs. 10.1 ± 5.5; MD= -4.69; 95%CI: -7.58 to -1.81; p = 0.001). Copeptin level was higher in patients with mortality/acute HF-related hospitalization vs. stable patients (31.3 ± 23.7 vs. 20.4 ± 12.8; MD= -13.06; 95%CI: -25.28 to -0.84; p = 0.04). Higher copeptin concentrations were associated with mortality and observed in all follow-up periods (p <0.05). Discussion and Conclusions: Present meta-analysis showed that elevated copeptin plasma concentrations observed in HF patients are associated with increased risk of all-cause mortality, thus copeptin may serve as predictor of outcome in HF.

Copeptin as a Diagnostic and Prognostic Biomarker in Cardiovascular Diseases

Copeptin is the carboxyl-terminus of the arginine vasopressin (AVP) precursor peptide. The main physiological functions of AVP are fluid and osmotic balance, cardiovascular homeostasis, and regulation of endocrine stress response. Copeptin, which is released in an equimolar mode with AVP from the neurohypophysis, has emerged as a stable and simple-to-measure surrogate marker of AVP and has displayed enormous potential in clinical practice. Cardiovascular disease (CVD) is currently recognized as a primary threat to the health of the population worldwide, and thus, rapid and effective approaches to identify individuals that are at high risk of, or have already developed CVD are required. Copeptin is a diagnostic and prognostic biomarker in CVD, including the rapid rule-out of acute myocardial infarction (AMI), mortality prediction in heart failure (HF), and stroke. This review summarizes and discusses the value of copeptin in the diagnosis, discrimination, and prognosis of CVD (AMI, HF, and stroke), as well as the caveats and prospects for the application of this potential biomarker.

Arterial Stiffness, Subendocardial Impairment, and 30-Day Readmission in Heart Failure Older Patients

Arterial stiffness and subendocardial perfusion impairment may play a significant role in heart failure (HF) outcomes. The aim of the study was to examine the main predictors of 30-day readmission in geriatric patients, hospitalized with HF, explore hemodynamical parameters, arterial stiffness indexes, and subendocardial viability ratio (SEVR). In total, 41 hospitalized patients, affected by HF, were included; they underwent clinical evaluation, routine laboratory testing, and echocardiography. At the time of admission, after the achievement of clinical stability (defined as switching from intravenous to oral diuretic therapy), and at discharge, arterial tonometry was performed to evaluate carotid-femoral pulse wave velocity (PWVcf) and SEVR (then corrected for hemoglobin concentration and oxygen saturation). Through the evaluations, a significant progressive decrease in PWVcf was described (17.79 ± 4.49, 13.54 ± 4.54, and 9.94 ± 3.73 m/s), even after adjustment for age, gender, mean arterial pressure (MAP) variation, and left ventricular ejection fraction (LVEF). A significant improvement was registered for both SEVR (83.48 ± 24.43, 97.94 ± 26.84, and 113.29 ± 38.02) and corrected SEVR (12.74 ± 4.69, 15.71 ± 5.30, and 18.55 ± 6.66) values, and it was still significant when adjusted for age, gender, MAP variation, and LVEF. After discharge, 26.8% of patients were readmitted within 30 days. In a multivariate binary logistic regression analysis, PWVcf at discharge was the only predictor of 30-day readmission (odds ratio [OR] 1.957, 95% CI 1.112–3.443). In conclusion, medical therapy seems to improve arterial stiffness and subendocardial perfusion in geriatric patients hospitalized with heart failure. Furthermore, PWVcf is a valid predictor of 30-day readmission. Its feasibility in clinical practice may provide an instrument to detect patients with HF at high risk of rehospitalization.

Association of C-Terminal Pro-Endothelin-1 with Mortality in the Population-Based KORA F4 Study

Introduction

Endothelin-1 and its prohormone C-terminal pro-endothelin-1 (CT-proET-1) have been linked to metabolic alterations, inflammatory responses and cardiovascular events in selected study populations. We analyzed the association of CT-proET-1 with cardiovascular events and mortality, carotid intima-media-thickness as surrogate for early atherosclerotic lesions, biomarkers of subclinical inflammation and adipokines in a population-based study.

Methods

The cross-sectional and prospective analyses used data from the KORA F4 study with a median follow-up time of 9.1 (8.8–9.4) years. Data on CT-proET-1 and mortality were available for 1554 participants, data on the other outcomes in subgroups (n = 596–1554). The associations were estimated using multivariable linear regression and Cox proportional hazard models adjusted for sex, age, body mass index, estimated glomerular filtration rate, arterial hypertension, diabetes, low-density and high-density lipoprotein cholesterol, current and former smoking and physical activity. The Bonferroni method was used to correct for multiple testing.

Results

In the fully adjusted model, CT-proET-1 was associated with cardiovascular (hazard ratio (HR) per standard deviation increase: 1.66; 95% confidence interval (CI): 1.10–2.51; p = 0.017) and all-cause mortality (HR: 2.03; 95% CI 1.55–2.67; p < 0.001), but not with cardiovascular events, and was inversely associated with the intima-media thickness (β: −0.09 ± 0.03; p = 0.001). CT-proET-1 was positively associated with five out of ten biomarkers of subclinical inflammation and with two out of five adipokines after correction for multiple testing. After inclusion of biomarkers of subclinical inflammation in the Cox proportional hazard model, the association of CT-proET-1 with all-cause mortality persisted (p < 0.001).

Conclusion

These results emphasize the complexity of endothelin-1 actions and/or indicator functions of CT-proET-1. CT-proET-1 is a risk marker for all-cause mortality, which is likely independent of vascular endothelin-1 actions, cardiovascular disease and inflammation.

Differences According to Age in the Diagnostic Performance of Cardiac Biomarkers to Predict Frailty in Patients with Acute Heart Failure

Frailty has traditionally been studied in the elderly population but scarcely in younger individuals. The objective of the present study is to analyze differences according to age in the diagnostic performance of cardiac biomarkers to predict frailty in patients admitted to the hospital for acute heart failure (AHF). A frailty assessment was performed with the SPPB and FRAIL scales (score > 3). We included 201 patients who were divided according to age: those older and younger than 75 years. In the younger group, no biomarker was related to the presence of frailty. This was mainly determined by age and comorbidities. In the elderly group, NT-proBNP was significantly related to the presence of frailty, but none of the baseline characteristics were. The best cut-off point in the elderly group for NT-proBNP was 4000 pg/mL. The area under the curve (AUC) for proBNP for frailty detection was 0.62 in the elderly. Another similar frailty scale, the SPPB, also showed a similar AUC in this group; however, adding the NT-proBNP (one point if NT-proBNP < 4000 pg/mL), it showed a slightly higher yield (AUC 0.65). The addition of biomarkers could improve frailty detection in members of the elderly population who are admitted to the hospital for AHF.

Depression and Hospital Readmissions in Patients with Heart Failure

Depression increases the risk of mortality in patients with heart failure (HF). Less is known about whether depression predicts multiple readmissions or whether multiple hospitalizations worsen depression in patients with HF. This study aimed to test the hypotheses that depression predicts multiple readmissions in patients hospitalized with HF, and conversely that multiple readmissions predict persistent or worsening depression. All-cause readmissions were ascertained over a 2-year follow-up of a cohort of 400 patients hospitalized with HF. The Patient Health Questionnaire-9 was used to assess depression at index and 3-month intervals. At enrollment in the study, 21% of the patients were mildly depressed and 22% were severely depressed. Higher Patient Health Questionnaire-9 depression scores predicted a higher rate of readmissions (adjusted hazard ratio 1.02, 95% confidence interval 1.00 to 1.04, p = 0.03). The readmission rate was higher in those who were severely depressed than in those without depression (p = 0.0003), but it did not differ between patients who were mildly depressed and patients without depression. Multiple readmissions did not predict persistent or worsening depression, but younger patients in higher New York Heart Association classes were more depressed than other patients. Depression is an independent risk factor for multiple all-cause readmissions in patients hospitalized with HF. Severe depression is a treatable psychiatric co-morbidity that warrants ongoing clinical attention in patients with HF.

A year in heart failure: an update of recent findings

Major changes have occurred in these last years in heart failure (HF) management. Landmark trials and the 2021 European Society of Cardiology guidelines for the diagnosis and treatment of HF have established four classes of drugs for treatment of HF with reduced ejection fraction: angiotensin-converting enzyme inhibitors or an angiotensin receptor-neprilysin inhibitor, beta-blockers, mineralocorticoid receptor antagonists, and sodium-glucose co-transporter 2 inhibitors, namely, dapagliflozin or empagliflozin. These drugs consistently showed benefits on mortality, HF hospitalizations, and quality of life. Correction of iron deficiency is indicated to improve symptoms and reduce HF hospitalizations. AFFIRM-AHF showed 26% reduction in total HF hospitalizations with ferric carboxymaltose vs. placebo in patients hospitalized for acute HF (P = 0.013). The guanylate cyclase activator vericiguat and the myosin activator omecamtiv mecarbil improved outcomes in randomized placebo-controlled trials, and vericiguat is now approved for clinical practice. Treatment of HF with preserved ejection fraction (HFpEF) was a major unmet clinical need until this year when the results of EMPEROR-Preserved (EMPagliflozin outcomE tRial in Patients With chrOnic HFpEF) were issued. Compared with placebo, empagliflozin reduced by 21% (hazard ratio, 0.79; 95% confidence interval, 0.69 to 0.90; P < 0.001), the primary outcome of cardiovascular death or HF hospitalization. Advances in the treatment of specific phenotypes of HF, including atrial fibrillation, valvular heart disease, cardiomyopathies, cardiac amyloidosis, and cancer-related HF, also occurred. Coronavirus disease 2019 (COVID-19) pandemic still plays a major role in HF epidemiology and management. All these aspects are highlighted in this review.

Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model

Background

Chronic heart failure (CHF) comorbid with atrial fibrillation (AF) is a serious threat to human health and has become a major clinical burden. This prospective cohort study was performed to design a risk stratification system based on the light gradient boosting machine (LightGBM) model to accurately predict the 1- to 3-year all-cause mortality of patients with CHF comorbid with AF.

Methods

Electronic medical records of hospitalized patients with CHF comorbid with AF from January 2014 to April 2019 were collected. The data set was randomly divided into a training set and test set at a 3:1 ratio. In the training set, the synthetic minority over-sampling technique (SMOTE) algorithm and fivefold cross validation were used for LightGBM model training, and the model performance was performed on the test set and compared using the logistic regression method. The survival rate was presented on a Kaplan–Meier curve and compared by a log-rank test, and the hazard ratio was calculated by a Cox proportional hazard model.

Results

Of the included 1796 patients, the 1-, 2-, and 3-year cumulative mortality rates were 7.74%, 10.63%, and 12.43%, respectively. Compared with the logistic regression model, the LightGBM model showed better predictive performance, the area under the receiver operating characteristic curve for 1-, 2-, and 3-year all-cause mortality was 0.718 (95%CI, 0.710–0.727), 0.744(95%CI, 0.737–0.751), and 0.757 (95%CI, 0.751–0.763), respectively. The net reclassification index was 0.062 (95%CI, 0.044–0.079), 0.154 (95%CI, 0.138–0.172), and 0.148 (95%CI, 0.133–0.164), respectively. The differences between the two models were statistically significant (P < 0.05). Patients in the high-risk group had a significantly higher hazard of death than those in the low-risk group (hazard ratios: 12.68, 13.13, 14.82, P < 0.05).

Conclusion

Risk stratification based on the LightGBM model showed better discriminative ability than traditional model in predicting 1- to 3-year all-cause mortality of patients with CHF comorbid with AF. Individual patients’ prognosis could also be obtained, and the subgroup of patients with a higher risk of mortality could be identified. It can help clinicians identify and manage high- and low-risk patients and carry out more targeted intervention measures to realize precision medicine and the optimal allocation of health care resources.

Copeptin is associated with mortality in elderly people

BACKGROUND

Elevated copeptin, a marker for vasopressin release, has been associated with impaired prognosis in acute myocardial infarction (MI). The aim was to investigate whether this association extends beyond the acute phase and whether it is related to markers of stress (cortisol) and heart failure (NTproBNP).

METHODS

Copeptin, cortisol and NTproBNP were measured in 926 participants (age: 76.0; male: 48.5%) in the ICELAND MI study whereof 246 had a previous MI (91 recognizable (RMI) and 155 previously unrecognizable (UMI) detected by cardiac magnetic resonance imaging). The primary endpoint was cardiovascular events (CVEs), and secondary endpoints were total mortality, heart failure and MI (median follow-up was 9.1 years). The relation between copeptin and prognosis was assessed with the Cox proportional hazard regression (unadjusted, adjusted for cortisol and NTproBNP, respectively, and a multiple model: copeptin, cortisol, NTproBNP, age, sex, serum creatinine, heart failure).

RESULTS

Copeptin was higher in participants with MI (8.9 vs. 6.4 pmol/L; P < .01), with no difference between RMI vs. UMI. Increased copeptin correlated with evening cortisol (r = .11; P < .01) and NTproBNP (r = .07; P = .04). Copeptin was associated with CVE and total mortality after adjusting for cortisol and NTproBNP separately, and remained significantly associated with total mortality in the multiple model.

CONCLUSIONS

Copeptin was higher in subjects with previous MI regardless whether previously recognized or not. Copeptin correlated weakly with cortisol and NTproBNP, and was independently associated with total mortality. This indicates that the prognostic implications of copeptin are not only mediated by heart failure or stress, supporting the assumption that copeptin is a marker of general vulnerability.

Natriuretic peptide level at heart failure diagnosis and risk of hospitalisation and death in England 2004-2018

Objective

Heart failure (HF) is a malignant condition requiring urgent treatment. Guidelines recommend natriuretic peptide (NP) testing in primary care to prioritise referral for specialist diagnostic assessment. We aimed to assess association of baseline NP with hospitalisation and mortality in people with newly diagnosed HF.

Methods

Population-based cohort study of 40 007 patients in the Clinical Practice Research Datalink in England with a new HF diagnosis (48% men, mean age 78.5 years). We used linked primary and secondary care data between 1 January 2004 and 31 December 2018 to report one-year hospitalisation and 1-year, 5-year and 10-year mortality by NP level.

Results

22 085 (55%) participants were hospitalised in the year following diagnosis. Adjusted odds of HF-related hospitalisation in those with a high NP (NT-proBNP >2000 pg/mL) were twofold greater (OR 2.26 95% CI 1.98 to 2.59) than a moderate NP (NT-proBNP 400–2000 pg/mL). All-cause mortality rates in the high NP group were 27%, 62% and 82% at 1, 5 and 10 years, compared with 19%, 50% and 77%, respectively, in the moderate NP group and, in a competing risks model, risk of HF-related death was 50% higher at each timepoint. Median time between NP test and HF diagnosis was 101 days (IQR 19–581).

Conclusions

High baseline NP is associated with increased HF-related hospitalisation and poor survival. While healthcare systems remain under pressure from the impact of COVID-19, research to test novel strategies to prevent hospitalisation and improve outcomes—such as a mandatory two-week HF diagnosis pathway—is urgently needed.

Copeptin combined with National Early Warning Score for predicting survival in elderly critical ill patients at emergency department

OBJECTIVE

Copeptin, reflecting vasopressin release, as well as the National Early Warning Score (NEWS), reflecting the severity of critical illness, might qualify for survival prediction in elderly patients with critical illness. This prospective observational study aims at assessing the predictive value of copeptin combined with NEWS on the prognosis of elderly critical ill patients at emergency department (ED).

METHODS

We analyzed serum copeptin levels and the NEWS at admission to the ED in a prospective, single-center, and observational study comprising 205 elderly patients with critical illness. Death within 30 days after admission to the ED was the primary end point.

RESULTS

The serum copeptin levels and the NEWS in the non-survivor patients group were higher than those in the survivor group [30.35 (14.20, 38.91) vs 17.53 (13.01, 25.20), P = 0.001 and 9.0 (7.0-10.0) vs 7.0 (6.0-8.0), P = 0.001]. Multivariate logistic regression analysis showed that copeptin, NEWS and copeptin combined with NEWS were all independent risk factors for 30-day mortality in elderly patients with critical illness. Copeptin, NEWS and copeptin combined with NEWS all performed well in predicting 30-day survival, with area under the ROC curve (AUC) values of 0.766 (95%CI, 0.702-0.822), 0.797 (95%CI, 0.744-0.877) and 0.854 (95%CI, 0.798-0.899) respectively. Using the Z test to compare the areas under the above three curves, copeptin combined with NEWS showed a higher predictive value for 30-day survival (P < 0.05). As we calculated, the optimal cut-off values of copeptin and NEWS using the Youden index were 19.78 pg/mL and 8.5 points, respectively. Risk stratification analysis showed that patients with both copeptin levels higher than 19.78 pg/mL and NEWS points higher than 8.5 points had the highest risk of death.

CONCLUSIONS

Copeptin combined with NEWS have a stronger predictive power on the prognosis of elderly patients with critical illness at ED, comparing to either factor individually.

Prevalence and Prognostic Value of Heart Failure Stages: An Elderly Inpatient Based Cohort Study

Background: The prevalence and prognostic value of heart failure (HF) stages among elderly hospitalized patients is unclear.

Methods: We conducted a prospective, observational, multi-center, cohort study, including hospitalized patients with the sample size of 1,068; patients were age 65 years or more, able to cooperate with the assessment and to complete the echocardiogram. Two cardiologists classified all participants in various HF stages according to 2013 ACC/AHA HF staging guidelines. The outcome was rate of 1-year major adverse cardiovascular events (MACE). The Kaplan–Meier method and Cox proportional hazards models were used for survival analyses. Survival classification and regression tree analysis were used to determine the optimal cutoff of N-terminal pro-brain natriuretic peptide (NT-proBNP) to predict MACE.

Results: Participants' mean age was 75.3 ± 6.88 years. Of them, 4.7% were healthy and without HF risk factors, 21.0% were stage A, 58.7% were stage B, and 15.6% were stage C/D. HF stages were associated with worsening 1-year survival without MACE (log-rank χ² = 69.62, P < 0.001). Deterioration from stage B to C/D was related to significant increases in HR (3.636, 95% CI, 2.174–6.098, P < 0.001). Patients with NT-proBNP levels over 280.45 pg/mL in stage B (HR 2; 95% CI 1.112–3.597; P = 0.021) and 11,111.5 pg/ml in stage C/D (HR 2.603, 95% CI 1.014–6.682; P = 0.047) experienced a high incidence of MACE adjusted for age, sex, and glomerular filtration rate.

Conclusions : HF stage B, rather than stage A, was most common in elderly inpatients. NT-proBNP may help predict MACE in stage B.

Trial Registration: ChiCTR1800017204; 07/18/2018.

Prevalence, risk factors and proteomic bioprofiles associated with heart failure in rheumatoid arthritis: The RA-HF study

BACKGROUND

Rheumatoid arthritis (RA) patients have high risk of heart failure (HF).

AIMS

Identifying the risk factors and mechanistic pathways associated with HF in patients with RA.

METHODS

Cohort study enrolling 355 RA patients. HF was defined according to the ESC criteria. 93 circulating protein-biomarkers (91CVDIIOlink®+troponin-T+c-reactive protein) were measured. Regression modeling (multivariate and multivariable) were built and network analyses were performed - based on the identified relevant protein biomarkers.

RESULTS

115 (32.4%) patients fulfilled the ESC criteria for HF, but only 24 (6.8%) had a prior HF diagnosis. Patients with HF were older (67 vs. 55yr), had a longer RA duration (10 vs. 14yr), had more frequently diabetes, hypertension, obesity, dyslipidemia, atrial fibrillation, and ischemic arterial disease. Several protein-biomarkers remained independently associated with HF, the top (FDR1%) were adrenomedullin, placenta-growth-factor, TNF-receptor-11A, and angiotensin-converting-enzyme-2. The networks underlying the expression of these biomarkers pointed towards congestion, apoptosis, inflammation, immune system signaling and RAAS activation as central determinants of HF in RA. Similar HF-associated biomarker-pathways were externally found in patients without RA. Having RA plus HF increased the risk of cardiovascular events compared to RA patients without RF; adjusted-HR (95%CI)=2.37 (1.07-5.30), p=0.034 CONCLUSION: Age, cardiovascular risk factors, and RA duration increase the HF odds in patients with RA. Few RA patients had a correct prior HF diagnosis, but the presence of HF increased the patients` risk. RA patients with HF largely share the mechanistic pathways of HF patients without RA. Randomized HF trials should include patients with RA. CLINICALTRIALS.

GOV ID

NCT03960515.

Mid-Regional Proadrenomedullin as a New Biomarker of Kidney and Cardiovascular Diseases-Is It the Future?

The increasing prevalence of cardiovascular disease and concomitant chronic kidney disease among the aging populations is responsible for considerable growth of mortality. Additionally, frequent, prolonged hospitalizations and long-term treatment generates progressive decline in bodily functions as well as substantial public health and economic burden. Accessibility to easy, non-invasive prognostic markers able to detect patients at risk of cardiovascular events may improve effective therapy and mitigate disease progression. Moreover, an early diagnosis allows time for implementation of prophylactic and educational programs that may result in decreased morbidity, improved quality of life and reduced public health expenditure. One of the promising candidates for a novel cardiovascular biomarker is mid-regional proadrenomedullin, a derivative of adrenomedullin. Adrenomedullin is a peptide hormone known for its vasodilatory, antioxidant, antiapoptotic and antifibrotic effects. A remarkable advantage of mid-regional proadrenomedullin is its longer half-life which is a prerequisite for plasma measurements. These review aims to discuss the importance of mid-regional proadrenomedullin with reference to its usefulness as a biomarker of increased cardiovascular risk and kidney disease progression.

Weight loss, malnutrition, and cachexia in COVID-19: facts and numbers

Patients with COVID-19 disease are prone to develop significant weight loss and clinical cachexia. Three reports with altogether 589 patients that reported on weight loss and cachexia in COVID-19 were identified. Disease severity of patients and the timing of the assessment during the disease course in these patients were variable-65 patients (11%) were intensive care treated at the time of assessment, and 183 (31%) were cared for in sub-intensive or intermediate care structures. The frequency of weight loss ≥5% (that defines cachexia) was 37% (range 29-52%). Correlates of weight loss occurrence were reported to be raised C-reactive protein levels, impaired renal function status, and longer duration of COVID-19 disease. Underweight status by WHO criteria (BMI < 18.5 kg/m² ) was only observed in 4% of patients analysing data from seven studies with 6661 patients. Cachexia assessment in COVID-19 needs assessment of weight loss. COVID-19 associated cachexia is understood to affect muscle and fat tissue as is also seen in many other chronic illness-associated forms of cachexia. There are many factors that can contribute to body wasting in COVID-19, and they include loss of appetite and taste, fever and inflammation, immobilization, as well as general malnutrition, catabolic-anabolic imbalance, endocrine dysfunction, and organ-specific complications of COVID-19 disease such as cardiac and renal dysfunction. Treatment of COVID-19 patients should include a focus on nutritional support and rehabilitative exercise whenever possible. Specific anti-cachectic therapies for COVID-19 do not exist, but constitute a high medical need to prevent long-term disability due to acute COVID-19 disease.

Prevalence and Prognostic Significance of Frailty in Gerontal Inpatients With Pre-clinical Heart Failure: A Subgroup Analysis of a Prospective Observational Cohort Study in China

Objective: To evaluate the prognostic value of frailty in gerontal pre-clinical heart failure (stage B heart failure, SBHF) inpatients.

Background: The association between frailty and SBHF remains unknown.

Methods: We conducted a subgroup analysis of a prospective observational cohort study on frailty. The previous study recruited 1,000 elderly inpatients who were consecutively admitted to a tertiary referral hospital in Beijing, China, from September 2018 to February 2019. The outcomes were all-cause death or readmission at 1-year follow-up. SBHF was diagnosed for asymptomatic cardiac structural or functional abnormalities. Frailty was assessed using the Comprehensive Geriatric Assessment-Frailty Index (CGA-FI).

Results: Overall, 531 inpatients aged ≥65 years were deemed to have SBHF and followed up for 1 year. Of them, 34.5% exhibited frailty. During the follow-up period, all-cause death or readmission occurred in 157 (29.5%) participants. Of these participants, 36.6% (67/183) and 25.9% (90/348) belonged to the frail and non-frail groups, respectively (χ² = 6.655, P = 0.010). Frailty, defined by the CGA-FI, rather than Fried frailty phenotype, could independently predict 1-year all-cause death or readmission (hazard ratio, 1.56; 95% confidence interval, 1.03–2.35; P = 0.034) and was more suitable for predicting all-cause death or readmission than N-terminal pro-B-type natriuretic peptide in female SBHF inpatients aged 80 years or over(AUC_CGA−FI vs. AUC_NT−proBNP 0.654 vs. 0.575, P = 0.017).

Conclusions: Frailty is highly prevalent even among SBHF inpatients aged ≥65 years. The CGA-FI can independently predict 1-year all-cause death or readmission, rather than Fried frailty phenotype. Frailty in gerontal SBHF inpatients deserves more attention.

Clinical Trial registration: ChiCTR1800017204; date of registration: 07/18/2018.

Multiple biomarkers measurement to estimate the duration of atrial fibrillation

BACKGROUND

An accurate estimation of the duration of atrial fibrillation is critical for its safe management. Recent studies suggested that copeptin, carbohydrate antigen-125, galectin-3 and growth differentiation factor-15 are increased in atrial fibrillation. We examined the ability of these markers to identify patients presenting with atrial fibrillation of ≤48 versus >48 h duration.

METHODS

Retrospective analysis of a prospective study that included patients with atrial fibrillation of known duration.

RESULTS

A total of 98 patients were analysed, 47 with atrial fibrillation ≤48 h and 51 with >48 h. In patients presenting with atrial fibrillation of ≤48 versus >48 h duration, the mean carbohydrate antigen-125 concentration was 16.9 ± 12.5 versus 30.9 ± 36.3 U/mL (P = 0.01), and growth differentiation factor-15 concentration was 1320 ± 889 versus 2608 ±2163 pg/mL (P < 0.001). Copeptin concentration was not independently associated with atrial fibrillation duration. The galectin-3 concentration did not differ between groups. Area under the ROC curve to identify patients with atrial fibrillation ≤48 h was 0.869 for carbohydrate antigen-125, 0.853 for growth differentiation factor-15.

CONCLUSION

The plasma concentrations of carbohydrate antigen-125, growth differentiation factor-15 and copeptin, but not galectin-3, are higher in patients presenting with atrial fibrillation of >48 h duration than in those with atrial fibrillation ≤48 h. The ability to discriminate recent atrial fibrillation offered by carbohydrate antigen-125 and growth differentiation factor-15 seems high.

Cardiovascular biomarkers predict post-discharge re-hospitalization risk and mortality among Swedish heart failure patients

AIM

The aim of this study was to assess the predictive role of biomarkers, associated with cardiovascular stress and its neuroendocrine response as well as renal function, in relation to mortality and risk of re-hospitalization among consecutive patients admitted because of heart failure (HF).

METHODS AND RESULTS

A total of 286 patients (mean age, 75 years; 29% women) hospitalized for newly diagnosed or exacerbated HF were analysed. Associations between circulating levels of mid-regional pro-adrenomedullin (MR-proADM), copeptin, C-terminal pro-endothelin-1, N-terminal pro-brain natriuretic peptide (NT-proBNP), cystatin C, and all-cause mortality as well as risk of re-hospitalization due to cardiac causes were assessed using multivariable Cox regression models. A two-sided Bonferroni-corrected P-value of 0.05/5 = 0.010 was considered statistically significant. All biomarkers were related to echocardiographic measurements of cardiac dimensions and function. A total of 57 patients died (median follow-up time, 17 months). In the multivariable-adjusted Cox regression analyses, all biomarkers, except C-terminal pro-endothelin-1, were significantly associated with increased mortality: NT-proBNP [hazard ratio (HR) 1.85, 95% confidence interval (CI) 1.17-2.17; P = 4.0 × 10^-4 ], MR-proADM (HR 1.94, 95% CI 1.36-2.75; P = 2.2 × 10^-4 ), copeptin (HR 1.70, 95% CI 1.22-2.36; P = 0.002), and cystatin C (HR 2.11, 95% CI 1.56-2.86; P = 1.0 × 10^-6 ). A total of 90 patients were re-hospitalized (median time to re-hospitalization, 5 months). In multivariable Cox regression analyses, NT-proBNP was the only biomarker that showed significant association with risk of re-hospitalization due to cardiac causes (HR 1.43, 95% CI 1.10-1.87; P = 0.009).

CONCLUSIONS

Among patients hospitalized for HF, elevated plasma levels of NT-proBNP, MR-proADM, copeptin, and cystatin C are associated with higher mortality after discharge, whereas NT-proBNP is the only biomarker that predicts the risk of re-hospitalization due to cardiac causes.