Sex-stratified patterns of emergency cardiovascular admissions prior and during the COVID-19 pandemic.

The COVID-19 pandemic has had a significant impact on global public health, with long-term consequences that are still largely unknown. This study aimed to assess the data regarding acute cardiovascular hospital admissions in five European centers before and during the pandemic. A multicenter, multinational observational registry was created, comparing admissions to the emergency departments during a 3-months period in 2020 (during the pandemic) with the corresponding period in 2019 (pre-pandemic). Data on patient demographics, COVID-19 test results, primary diagnosis, comorbidities, heart failure profile, medication use, and laboratory results were collected. A total of 8778 patients were included in the analysis, with 4447 patients in 2019 and 4331 patients in 2020. The results showed significant differences in the distribution of cardiovascular diseases between the two years. The frequency of pulmonary embolism (PE) increased in 2020 compared to 2019, while acute heart failure (AHF) and other cardiovascular diseases decreased. The odds of PE incidence among hospitalized patients in 2020 were 1.316-fold greater than in 2019. The incidence of AHF was 50.83% less likely to be observed in 2020, and the odds for other cardiovascular diseases increased by 17.42% between the 2 years. Regarding acute coronary syndrome (ACS), the distribution of its types differed between 2019 and 2020, with an increase in the odds of ST-segment elevation myocardial infarction (STEMI) in 2020. Stratification based on sex revealed further insights. Among men, the incidence of AHF decreased in 2020, while other cardiovascular diseases increased. In women, only the incidence of STEMI showed a significant increase. When analyzing the influence of SARS-CoV-2 infection, COVID-positive patients had a higher incidence of PE compared to COVID-negative patients. COVID-positive patients with ACS also exhibited symptoms of heart failure more frequently than COVID-negative patients. These findings provide valuable information on the impact of the COVID-19 pandemic on acute cardiovascular hospital admissions. The increased incidence of PE and changes in the distribution of other cardiovascular diseases highlight the importance of monitoring and managing cardiovascular health during and post pandemic period. The differences observed between sexes emphasize the need for further research to understand potential sex-specific effects of COVID-19 on cardiovascular outcomes.

An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review.

Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.