Secondary prevention and follow-up of patients with ACS and not-at-target LDL: An Italian real-world retro-prospective analysis by the inertia group

Background

In patients with recent ACS, the latest ESC/EAS guidelines for management of dyslipidaemia recommend intensification of LDL-C-lowering therapy.

Objective

Report a real-world picture of lipid-lowering therapy prescribed and cholesterol targets achieved in post-ACS patients before and after a specific educational program.

Methods

Retrospective data collection prior to the educational course and prospective data collection after the course of consecutive very high-risk patients with ACS admitted in 2020 in 13 Italian cardiology departments, and with a non-target LDL-C level at discharge.

Results

Data from 336 patients were included, 229 in the retrospective phase and 107 in the post-course prospective phase. At discharge, statins were prescribed in 98.1% of patients, alone in 62.3% of patients (65% of which at high doses) and in combination with ezetimibe in 35.8% of cases (52% at high doses). A significant reduction was obtained in total and LDL cholesterol (LDL-C) from discharge to the first control visit. Thirty-five percent of patients achieved a target LDL-C <55 mg/dL according to ESC 2019 guidelines. Fifty percent of patients achieved the <55 mg/dL target for LDL-C after a mean of 120 days from the ACS event.

Conclusions

Our analysis, though numerically and methodologically limited, suggests that management of cholesterolaemia and achievement of LDL-C targets are largely suboptimal and need significant improvement to comply with the lipid-lowering guidelines for very high CV risk patients. Earlier high intensity statin combination therapy should be encouraged in patients with high residual risk.

A prospective registry to get insights into profile, management and outcome of cardiogenic shock patients

Background

Cardiogenic shock (CS) is the most severe form of acute heart failure, characterized by life-threatening end-organ hypoperfusion resulting from a low cardiac output state. Data on epidemiology of CS has been mostly drawn from registries focusing on acute myocardial infarction (AMI). However, recent evidence in a contemporary cohort in North America has shown that more than two thirds of all CS cases were related to causes other than AMI and that these patients had outcomes at least as poor as patients with AMICS.

Purpose

To provide data on profile, management, outcome, and evolution over time of CS patients admitted to ICCU/ICU and to compare them between patients with AMICS and acute decompensated heart failure (ADHF-CS).

Methods

The Altshock-2 Registry is a multicenter national prospective data collection, part of the Italian Altshock-2 program. Recruitment started on 2 March 2020 with 11 Italian Centers contributing to patients' enrolment. A total of 238 patients were hospitalized with confirmed diagnosis of CS between March 2020 and February 2022 in a multicenter national initiative. The mean age of this patient population was 64 years (interquartile range [IQR] 54–74) and 76% were male. Ninety-seven patients (41%) were admitted for AMICS, whereas 84 patients (35.3%) had ADHF-CS; 57 patients (24%) had other causes. As compared to AMICS patients, those admitted for ADHF-CS were younger, but with a higher burden of comorbidities (renal, liver, thyroid disease, atrial fibrillation, anemia), pre-existing decreased ejection fraction and a higher number of chronic drugs. Patients with ADHF-CS had a prevalent cardio-metabolic phenotype upon admission with prevalent congestion. Mechanical ventilation was more commonly used in patients with AMICS, compared to ADHF-CS, along with an increased inotropic score. Conversely, sodium nitroprusside was used in about sixty percent of patients with ADHF-CS. Sixty percent of the included population received a temporary mechanical circulatory support (MCS) device, which was intra-aortic balloon pump (IABP) in the eighty percent of the supported patients. Pulmonary artery catheter was used for monitoring only in the 18% of the included patients whereas an extensive echocardiographic approach was applied. Twenty-one patients (25%) underwent heart replacement therapy in the ADHF-CS patients versus 2 (2%) in the AMICS. Thirty-day mortality occurred in 32 patients (33%) in the AMICS group versus 23 (27%) in the ADHF-CS group (p=0.41).

Conclusions

Different diagnostic approaches and uses of mechanical circulatory support devises and inotropes are implemented in transatlantic settings. Uniform definitions and more homogenous protocols tailored on CS etiologies and clinical and biochemical phenotypes are needed in prospective initiatives in order to effectively compared results and outcome.

Funding Acknowledgement

Type of funding sources: None.

AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients

Purpose

To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification.

Material and Methods

In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled.

In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival.

A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020).

Results

The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766).

Conclusions

AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11547-022-01518-0.

The disposal of dying cells in living tissues

Cells continuously die and disappear from the midst of living tissues. However, some of their constituents survive. DNA is horizontally transferred to phagocytic cells, and apoptotic cell antigens shape the immune repertoire. When massive apoptosis occurs, which overwhelms tissue scavenger cells, or when the function of phagocytes abates, dying cells escape clearance in vivo. Remnant dying cells come to phagocytes disguised: factors capable to envelop their membranes pervade the entire organism, or are generated in given tissues. Some are constitutively present, while other are generated during early or late phases of the inflammatory response, possibly to face the further burden of the dead inflammatory cells. This camouflage influences the disposal of the corpses: decoying molecules either bridge the corpse to the phagocyte or hide it. Furthermore, factors associated to the plasma membrane of the apoptotic cell shape the signals the phagocyte releases in situ. Finally, molecules contained or released by the dying cell alter the apprehension by the phagocyte of its prey, influencing its immunogenicity.

Anti-beta2 glycoprotein I antibodies cause inflammation and recruit dendritic cells in platelet clearance

Scavenger phagocytes are mostly responsible for the in vivo clearance of activated or senescent platelets. In contrast to other particulate substrates, the phagocytosis of platelets does not incite proinflammatory responses in vivo. This study assessed the contribution of macrophages and dendritic cells (DCs) to the clearance of activated platelets. Furthermore, we verified whether antibodies against the beta2 Glycoprotein I (beta2GPI), which bind to activated platelets, influence the phenomenon. DCs did not per se intemalise activated platelets. In contrast, macrophages efficiently phagocytosed platelets. In agreement with the uneventful nature of the clearance of platelets in vivo, phagocytosing macrophages did not release IL-1beta, TNF-alpha, or IL-10, beta2GPI bound to activated platelets and was required for their recognition by anti-beta2GPI antibodies. DCs internalised platelets opsonised by anti-beta2GPI antibodies. The phagocytosis of opsonised platelets determined the release of TNF-alpha and IL-1beta by DCs and macrophages. Phagocytosing macrophages, but not DCs, secreted the antiinflammatory cytokine IL-10. We conclude that anti-beta2GPI antibodies cause inflammation during platelet clearance and shuttle platelet antigens to antigen presenting DCs.