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Risk stratification of patients with congestive heart failure (HF) is vital in clinical practice. The aim of this study was to construct a machine learning model to predict the in-hospital all-cause mortality for intensive care unit (ICU) patients with HF. eXtreme Gradient Boosting algorithm (XGBoost) was used to construct a new prediction model ( ) from the Medical Information Mart for Intensive Care IV database (MIMIC-IV) (training set). The eICU Collaborative Research Database dataset (eICU-CRD) was used for the external validation (test set). The XGBoost model performance was compared with a logistic regression model and an existing model (Get with the guideline-Heart Failure model) for mortality in the test set. Area under the receiver operating characteristic cure and Brier score were employed to evaluate the discrimination and the calibration of the three models. The SHapley Additive exPlanations (SHAP) value was applied to explain XGBoost model and calculate the importance of its features. The total of 11,156 and 9,837 patients with congestive HF from the training set and test set, respectively, were included in the study. In-hospital all-cause mortality occurred in 13.3% (1,484/11,156) and 13.4% (1,319/9,837) of patients, respectively. In the training set, of 17 features with the highest predictive value were selected into the models with LASSO regression. Acute Physiology Score III (APS III), age and Sequential Organ Failure Assessment (SOFA) were strongest predictors in SHAP. In the external validation, the XGBoost model performance was superior to that of conventional risk predictive methods, with an area under the curve of 0.771 (95% confidence interval, 0.757-0.784) and a Brier score of 0.100. In the evaluation of clinical effectiveness, the machine learning model brought a positive net benefit in the threshold probability of 0%-90%, prompting evident competitiveness compare to the other two models. This model has been translated into an online calculator which is accessible freely to the public (https://nkuwangkai-app-for-mortality-prediction-app-a8mhkf.streamlit.app). This study developed a valuable machine learning risk stratification tool to accurately assess and stratify the risk of in-hospital all-cause mortality in ICU patients with congestive HF. This model was translated into a web-based calculator which access freely.

Cardiogenic Shock can affect significantly the survival in patients with AMI or ADHF.Mechanical circulatory support is the keystone of treatment beyond standard care to improve end organ perfusion and potentially survival.Hemodynamics progression during support has not been well studied. This retrospective cohort study aims to evaluate the patterns, timingand outcomes of therapeutic circulatory support escalation and de-escalation in patients admitted to a tertiary referral cardiogenic shockcenter. Data were collected for the patients over the age of 18 that had implantation of the microaxial flow pump at the University of Iowa HealthCare. Data includes invasive hemodynamics during admission, 24 h and 72 h post intervention along different modalities ofmechanical and pharmacological hemodynamic support in addition to patient history and baseline characteristics. A total of 110 patients were included in the analysis, 53 patients (48.18%) survived. The average Impella power level was significantly lowerin the survival group (6.21 vs. 6.92, =0.01) despite similar flow rates between groups (3.16 L/min vs. 3.08 L/min, =0.31). 72 h Cardiacpower output was significantly better within survivors (1.15 watts vs. 0.69 watts, p<0.01). Multivariable logistic regression analysis identifiedelevated cardiac power at 72 h post intervention as an independent predictor of survival among patients with GCS during hospitalization(OR 0.09; 95% CI 0.02-0.45; =0.03). Our data indicate that sustained hemodynamic improvement, particularly in cardiac power output, is associated with survival, independently ofthe absolute changes in Impella flow and power.

Background: Almost all Duchenne muscular dystrophy (DMD) patients that reach their 30s present cardiomyopathy. As a result, this population remains under-treated. There is no sufficient proof of the efficacy of anti-remodeling cardiac therapy for DMD cardiomyopathy (DMDCM). We aim to assess the efficacy of anti-remodeling cardiac therapy for DMDCM by using meta-analysis. Methods: PubMed (MEDLINE), Embase, and Cochrane library were searched through January 2021. Randomized control trials, case-control studies, and observational studies that reported assessments of cardiovascular outcomes and death of participants using angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, beta-blockers, mineralocorticoid-receptor antagonists and Ivabradine, were included. The primary outcome was total mortality. Secondary outcomes included changes in left ventricular ejection fraction (LVEF), serum natriuretic peptide levels (BNP), and heart rate (HR). Data were extracted for eligibility by two independent reviewers. Random-effects meta-analysis was used to pool results. Results: Twelve studies with 439 patients were included in our meta-analysis. Treated patients have lower HR, mean difference of −17 beats per minute (CI [−25]–[−9], p < 0.01). The LVEF was improved in treated patients, with a mean difference of LVEF of 3.77% (CI 0.44–7.12, p < 0.03). Although mortality rates did not reach statistical significance there was a trend for total mortality reduction (hazard ratio 0.36, CI (0.1–1.25), p = 0.107) and for BNP reduction (SSMD: 0.141, CI ([−0.19]–[0.47]), p = 0.3). Conclusion: Pharmacologic treatment for DMDCM patients is associated with decreased HR and improved LVEF. Therefore, DMDCM patients may benefit from implementing guideline therapy for HF.

Decompensation episodes in chronic heart failure patients frequently result in unplanned outpatient or emergency room visits or even hospitalizations. Early detection of these episodes in their pre-symptomatic phase would likely enable the clinicians to manage this patient cohort with the appropriate modification of medical therapy which would in turn prevent the development of more severe heart failure decompensation thus avoiding the need for heart failure-related hospitalizations. Currently, heart failure worsening is recognized by the clinicians through characteristic changes of heart failure-related symptoms and signs, including the changes in heart sounds. The latter has proven to be largely unreliable as its interpretation is highly subjective and dependent on the clinicians’ skills and preferences. Previous studies have indicated that the algorithms of artificial intelligence are promising in distinguishing the heart sounds of heart failure patients from those of healthy individuals. In this manuscript, we focus on the analysis of heart sounds of chronic heart failure patients in their decompensated and recompensated phase. The data was recorded on 37 patients using two types of electronic stethoscopes. Using a combination of machine learning approaches, we obtained up to 72% classification accuracy between the two phases, which is better than the accuracy of the interpretation by cardiologists, which reached 50%. Our results demonstrate that machine learning algorithms are promising in improving early detection of heart failure decompensation episodes.

There is scarce information on patients with secondary heart failure diagnosis (sHF). We aimed to compare the characteristics, burden, and outcomes of sHF with those with primary HF diagnosis (pHF). Retrospective, observational study on patients ≥18 years with emergency department (ED) visits during 2018 with pHF and sHF in ED or hospital (ICD-10-CM) diagnostic codes. Baseline characteristics, 30-day and 1-year mortality, readmission and re-ED visit rates, and costs were compared between sHF and pHF. Out of the 797 patients discharged home from the ED, 45.5% had sHF, and these presented lower 1-year hospitalization, re-ED visit rates, and costs. In contrast, out of the 2,286 hospitalized patients, 55% had sHF and 45% pHF. Hospitalized sHF patients had significantly ( < 0.01) greater comorbidity, lower use of recommended HF therapies, longer length of stay (10.8 ± 10.1 vs. 9.7 ± 7.9 days), and higher in-hospital and 1-year mortality (32 vs. 25.8%) with no significant differences in readmission rates and lower 1-year re-ED visit rate. Hospitalized sHF patients had higher total costs (€12,262,422 vs. €9,144,952, < 0.001), mean cost per patient-year (€9,755 ± 13,395 vs. €8,887 ± 12,059), and average daily cost per patient. Hospitalized sHF patients have a worse initial prognosis, greater use of healthcare resources, and higher costs.

Conduction system pacing (CSP) has emerged as a promising novel delivery method for Cardiac Resynchronisation Therapy (CRT), providing an alternative to conventional biventricular epicardial (BiV) pacing in indicated patients. Despite increasing popularity and widespread uptake, CSP has rarely been specifically examined in patients with atrial fibrillation (AF), a cohort which forms a significant proportion of the heart failure (HF) population. In this review, we first examine the mechanistic evidence for the importance of sinus rhythm (SR) in CSP by allowing adjustment of atrioventricular delays (AVD) to achieve the optimal electrical response, and thus, whether the efficacy of CSP may be significantly attenuated compared to conventional BiV pacing in the presence of AF. We next evaluate the largest clinical body of evidence in this field, related to patients receiving CSP following atrioventricular nodal ablation (AVNA) for AF. Finally, we discuss how future research may be designed to address the vital question of how effective CSP in AF patients is, and the potential hurdles we may face in delivering such studies.

Cancer and heart disease are the two most common health conditions in the world, associated with high morbidity and mortality, with even worse outcomes in regional areas. Cardiovascular disease is the leading cause of death in cancer survivors. We aimed to evaluate the cardiovascular outcomes of patients receiving cancer treatment (CT) in a regional hospital. This was an observational retrospective cohort study in a single rural hospital over a ten-year period (17th February 2010 to 19th March 2019). Outcomes of all patients receiving CT during this period were compared to those who were admitted to the hospital without a cancer diagnosis. 268 patients received CT during the study period. High rates of cardiovascular risk factors: hypertension (52.2%), smoking (54.9%), and dyslipidaemia (38.4%) were observed in the CT group. Patients who had CT were more likely to be readmitted with ACS (5.9% vs. 2.8%  = 0.005) and AF (8.2% vs. 4.5%  = 0.006) when compared to the general admission cohort. There was a statistically significant difference observed for all cause cardiac readmission, with a higher rate observed in the CT group (17.1% vs. 13.2%  = 0.042). Patients undergoing CT had a higher rate of mortality (49.5% vs. 10.2%, ≤ 0.001) and shorter time (days) from first admission to death (401.06 vs. 994.91,  ≤ 0.001) when compared to the general admission cohort, acknowledging this reduction in survival may be driven at least in part by the cancer itself. There is an increased incidence of adverse cardiovascular outcomes, including higher readmission rate, higher mortality rate and shorter survival in people undergoing cancer treatment in rural environments. Rural cancer patients demonstrated a high burden of cardiovascular risk factors.