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: Myocardial injury is linked to poor outcomes in respiratory infections. This study evaluated the prognostic value of high-sensitivity troponin I (hsTnI) in predicting 30-day outcomes in patients hospitalized with seasonal influenza. : In this single-center retrospective study, 277 adults with laboratory-confirmed influenza were analyzed. Myocardial injury was defined by elevated hsTnI. The primary composite endpoint included 30-day mortality, intensive care unit (ICU) admission, and mechanical ventilation. : Patients with myocardial injury had significantly higher event rates for the composite endpoint than those without ( < 0.0001). Dynamic hsTnI elevations, reflecting acute myocardial injury, were also associated with worse outcomes ( = 0.026). Machine learning models incorporating hsTnI and laboratory data achieved excellent predictive performance (AUC = 0.99) and improved risk classification compared with conventional scores ( < 0.0001). : Among hospitalized influenza patients, myocardial injury identified by hsTnI strongly predicted short-term adverse outcomes. Routine hsTnI assessment enhances risk stratification beyond standard clinical scores and may facilitate early identification and management of high-risk patients.

Preclinical management of patients with acute chest pain and their identification as candidates for urgent coronary revascularization without the use of high sensitivity troponin essays remains a critical challenge in emergency medicine. We enrolled 2760 patients (average age 70 years, 58.6% male) with chest pain and suspected ACS, who were admitted to the Emergency Department of the University Hospital Tübingen, Germany, between August 2016 and October 2020. Using 26 features, eight Machine learning models (non-deep learning models) were trained with data from the preclinical rescue protocol and compared to the “TropOut” score (a modified version of the “preHEART” score which consists of history, ECG, age and cardiac risk but without troponin analysis) to predict major adverse cardiac event (MACE) and acute coronary artery occlusion (ACAO). In our study population MACE occurred in 823 (29.8%) patients and ACAO occurred in 480 patients (17.4%). Interestingly, we found that all machine learning models outperformed the “TropOut” score. The VC and the LR models showed the highest area under the receiver operating characteristic (AUROC) for predicting MACE (AUROC = 0.78) and the VC showed the highest AUROC for predicting ACAO (AUROC = 0.81). A SHapley Additive exPlanations (SHAP) analyses based on the XGB model showed that presence of ST-elevations in the electrocardiogram (ECG) were the most important features to predict both endpoints.

Platelet activation plays a critical role in thrombosis. Inhibition of platelet activation is a cornerstone in treatment of acute organ ischemia. Platelet ACKR3 surface expression is independently associated with all-cause mortality in CAD patients. In a novel genetic mouse strain, we show that megakaryocyte/platelet-specific deletion of ACKR3 results in enhanced platelet activation and thrombosis in vitro and in vivo. Further, we performed ischemia/reperfusion experiments (transient LAD-ligation and tMCAO) in mice to assess the impact of genetic ACKR3 deficiency in platelets on tissue injury in ischemic myocardium and brain. Loss of platelet ACKR3 enhances tissue injury in ischemic myocardium and brain and aggravates tissue inflammation. Activation of platelet-ACKR3 via specific ACKR3 agonists inhibits platelet activation and thrombus formation and attenuates tissue injury in ischemic myocardium and brain. Here we demonstrate that ACKR3 is a critical regulator of platelet activation, thrombus formation and organ injury following ischemia/reperfusion. ACKR3 is a critical regulator of platelet-mediated thrombosis and organ injury following ischemia/reperfusion. Platelet ACKR3 surface expression is independently associated with all-cause mortality in patients with cardiovascular diseases.

Coronary artery disease (CAD) often leads to adverse events resulting in significant disease burdens. Underlying risk factors often remain inapparent prior to disease incidence and the cardiovascular (CV) risk is not exclusively explained by traditional risk factors. Platelets inherently promote atheroprogression and enhanced platelet functions and distinct platelet lipid species are associated with disease severity in patients with CAD. Lipidomics data were acquired using mass spectrometry and processed alongside clinical data applying machine learning to model estimates of an increased CV risk in a consecutive CAD cohort ( n  = 595). By training machine learning models on CV risk measurements, stratification of CAD patients resulted in a phenotyping of risk groups. We found that distinct platelet lipids are associated with an increased CV or bleeding risk and independently predict adverse events. Notably, the addition of platelet lipids to conventional risk factors resulted in an increased diagnostic accuracy of patients with adverse CV events. Thus, patients with aberrant platelet lipid signatures and platelet functions are at elevated risk to develop adverse CV events. Machine learning combining platelet lipidome data and common clinical parameters demonstrated an increased diagnostic value in patients with CAD and might improve early risk discrimination and classification for CV events.

Abstract Aims Heart failure (HF) patients may lack improvement of left ventricular (LV) ejection fraction (LVEF) despite optimal HF medication comprising an angiotensin receptor–neprilysin inhibitor (ARNI). Therefore, we aimed to identify key predictors for LV functional enhancement and prognostic reverse cardiac remodelling in HF patients on ARNI treatment. Methods We retrospectively analysed 294 consecutive patients with HF with reduced (HFrEF) or mildly reduced (HFmrEF) ejection fraction in our ‘EnTruth’ patient registry. LVEF was determined by echocardiography at initiation of ARNI and at 12 months of follow-up. We assessed the predictive value of clinically relevant patient-, HF- and treatment-related parameters in regard to changes in LVEF and all-cause mortality using medoid clustering and the XGBoost machine learning algorithm. Results Cluster analysis integrating clinically relevant patient characteristics unveiled four characteristic sub-phenotypes of patients with HFrEF and HFmrEF, respectively. Distinct clusters exhibit a strong (P < 0.05) therapeutic response to ARNI treatment and enhanced LV function. Key patient criteria, such as duration and aetiology of HF, renal function and de novo ARNI treatment, were significantly (P < 0.05) associated with change of LVEF and independently predicted cardiac remodelling. By training various machine learning models on relevant clinical parameters, stratification of LVEF improvement by XGBoost resulted in a high prediction accuracy. The stratification of patients with HFrEF [area under the receiver operating characteristic curve (AUC) = 0.77] and HFmrEF (AUC = 0.70) led to an increased diagnostic accuracy of LVEF improvement in the validation cohort. Using machine learning, the likelihood of cardiac remodelling following ARNI treatment, as indicated by our newly established EnTruth score, was directly associated with absolute LVEF improvement in both HFrEF (r = 0.51, P < 0.0001) and HFmrEF (r = 0.42, P = 0.001). Ultimately, patients with HFrEF and a high EnTruth score have a lower risk of all-cause mortality (P < 0.05 in survival analysis). Conclusions Recognition of essential clinical factors by integrating machine learning and cluster analyses may help to identify HF patients benefiting from improvement of LVEF following ARNI treatment. Early identification of those patients with a high response to ARNI treatment may allow a more refined selection of patients benefiting from an early escalation of HF treatment or interventional therapy. Prediction of LVEF improvement in patients with HFrEF and HFmrEF following treatment with Sacubitril/Valsartan. Workflow of this study investigating the functional capacity improvement in response to Sacubitrail/Valsartsan in a real-world scenario of heart failure treatment.

Background Risk stratification in non-ischemic cardiomyopathies (NICM) remains challenging despite guideline-based phenotypic classification using multimodal diagnostics including endomyocardial biopsy (EMB). We aimed to identify EMB-derived histological and molecular markers that improve phenotypic characterization and long-term risk stratification in patients with NICM. Methods In this prospective cohort study, 703 consecutive patients with symptomatic NICM underwent standardized multimodal evaluation, including clinical assessment, cardiac imaging, and endomyocardial biopsy. Biopsy specimens were analyzed using histology, immunohistochemistry, and targeted myocardial mRNA profiling. Associations between endomyocardial markers, and fibroinflammatory remodeling, imaging parameters, and molecular signatures were assessed cross-sectionally. Long-term prognostic relevance was evaluated using survival and multivariable prediction analyses during follow-up of up to fifteen years for all-cause mortality, cardiovascular mortality, implantable cardioverter-defibrillator (ICD) implantation, and appropriate ICD discharge. Results Elevated myocardial Gremlin-1 expression was associated with increased fibrosis, adverse cardiac remodelling, reduced left ventricular function, and enrichment of pro-fibrotic and inflammatory mRNA signalling pathways. Myocardial and circulating Gremlin-1 expression was independently associated with all-cause and cardiovascular mortality, and ICD implantation and discharge. Machine learning–based phenotyping using histological EMB data identified Gremlin-1 as a key predictive feature of poor prognosis. Incorporation of Gremlin-1 into predictive models significantly improved long-term cardiovascular risk stratification in NICM patients. Conclusion Our results unveil that Gremlin-1 is associated with inflammation and cardiac remodelling in patients with NICM, and patients with Gremlin-1 + EMB and high plasmatic Gremlin-1 concentrations are at elevated risk to develop adverse cardiovascular events. Thus, the histological evaluation of Gremlin-1 may help to improve risk discrimination and management of NICM and HF patients. Plain Language Summary Non-ischemic cardiomyopathy (NICM) refers to diseases in which the heart muscle becomes abnormal and unable to pump effectively, without being caused by blocked coronary arteries. Predicting which patients will develop serious complications remains difficult using current clinical tests. We examined whether information from small heart tissue samples could improve long-term risk prediction. We analysed 703 NICM patients who underwent comprehensive clinical assessment and endomyocardial biopsy. Heart tissue was assessed for markers of inflammation and scarring, including the protein Gremlin-1, and patients were followed for up to fifteen years for major cardiovascular outcomes. Higher Gremlin-1 expression and circulating Gremlin-1 were associated with scarring, reduced heart function, and increased risk of adverse outcomes. These findings suggest that tissue-based biomarkers including Gremlin-1 may support more accurate risk stratification and personalized management in NICM. Harm et al. analyse endomyocardial biopsies from 703 patients with non-ischemic cardiomyopathy to test whether tissue-based markers improve long-term risk prediction. Increased myocardial Gremlin-1 is associated with adverse remodelling and predicts overall and cardiovascular mortality, defibrillator implantation, and appropriate discharge.

Major causes of morbidity in intravenous drug users are infections. In infective endocarditis, the tricuspid valve is mainly involved. Masses can cause septic embolisms and, in rare cases, they are associated with mycotic aneurysms of pulmonary arteries that lead to severe haemorrhage.We report the case of a young woman with a history of intravenous drug abuse and prolonged infective tricuspid valve endocarditis. Initially, echocardiography showed large masses on the anterior leaflet of the tricuspid valve and severe tricuspid regurgitation; blood cultures revealed staphylococcus and streptococcus species. Eight months after initial diagnosis, she presented with severe haemoptysis and fever. CT revealed a ruptured mycotic aneurysm of the right pulmonary artery. Lobectomy was performed immediately.Postoperatively, the patient fully recovered. After continued antibiotic treatment, follow-up examinations showed negative echocardiographic findings and blood cultures results.

Platelets play a significant role in atherothrombosis. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is critically involved in the regulation of LDL metabolism and interacts with platelet function. The effect of PCSK9 in platelet function is poorly understood. The authors of this article sought to characterize platelets as a major source of PCSK9 and PCSK9’s role in atherothrombosis. In a large cohort of patients with coronary artery disease (CAD), platelet count, platelet reactivity, and platelet-derived PCSK9 release were analyzed. The role of platelet PCSK9 on platelet and monocyte function was investigated in vitro. Platelet count and hyper-reactivity correlated with plasma LDL in CAD. The circulating platelets express on their surface and release substantial amounts of PCSK9. Release of PCSK9 augmented platelet-dependent thrombosis, monocyte migration, and differentiation into macrophages/foam cells. Platelets and PCSK9 accumulated in tissue derived from atherosclerotic carotid arteries in areas of macrophages. PCSK9 inhibition reduced platelet activation and platelet-dependent thrombo-inflammation. The authors identified platelets as a source of PCSK9 in CAD, which may have an impact on LDL metabolism. Furthermore, platelet-derived PCSK9 contributes to atherothrombosis, and inhibition of PCSK9 attenuates thrombo-inflammation, which may contribute to the reported beneficial clinical effects.

Patients with metabolic syndrome and coronary artery disease (CAD) are at increased risk of metabolic dysfunction-associated steatotic liver disease (MASLD), which can progress to steatohepatitis, cirrhosis, and hepatocellular carcinoma. MASLD is the most common liver disease and a significant contributor to cardiovascular morbidity. Enhanced platelet aggregation is linked to steatohepatitis, and antiplatelet therapy has been suggested as a potential treatment. In a prospective study of 51 patients with type 2 diabetes mellitus and/or obesity (BMI≥30), we evaluated the impact of antiplatelet therapy on hepatic fat content, liver volume, and iron deposition using magnetic resonance imaging (MRI) at baseline and six months. Ex vivo platelet function testing and plasma levels of proinflammatory chemotactic cytokines were measured to characterize thromboinflammatory mechanisms underlying MASLD. Increased platelet reactivity correlated with greater hepatic fat, iron deposition, and liver volume. Antiplatelet therapy was associated with reductions in hepatic volume and iron accumulation. Progression of steatosis was linked to dyslipidemia, platelet hyperreactivity, and elevated plasma levels of profibrotic, inflammatory, and apoptotic chemokines/cytokines. A distinct systemic cytokine profile corresponded with morphological features of progressive MASLD. Reduced platelet aggregation is associated with attenuation of MASLD features. Antiplatelet therapy correlates with decreased pro-inflammatory and pro-fibrotic chemokine signaling linked to the morphological characteristics of MASLD. Assessment of platelet reactivity and specific chemokines may enhance understanding of MASLD pathophysiology and support the development of novel therapeutic strategies.