Changes in the investigation and management of suspected myocardial infarction and injury during COVID-19: a multi-centre study using routinely collected healthcare data

Objective: The COVID-19 pandemic was associated with a reduction in the incidence of myocardial infarction (MI) diagnosis, in part because patients were less likely to present to hospital. Whether changes in clinical decision making with respect to the investigation and management of patients with suspected MI also contributed to this phenomenon is unknown. Methods: Multicentre retrospective cohort study in three UK centres contributing data to the National Institute for Health Research Health Informatics Collaborative. Patients presenting to the Emergency Department (ED) of these centres between 1st January 2020 and 1st September 2020 were included. Three time epochs within this period were defined based on the course of the first wave of the COVID-19 pandemic: pre-pandemic (epoch 1), lockdown (epoch 2), post-lockdown (epoch 3). Results: During the study period, 10,670 unique patients attended the ED with chest pain or dyspnoea, of whom 6,928 were admitted. Despite fewer total ED attendances in epoch 2, patient presentations with dyspnoea were increased (p < 0.001), with greater likelihood of troponin testing in both chest pain (p = 0.001) and dyspnoea (p < 0.001). There was a dramatic reduction in elective and emergency cardiac procedures (both p < 0.001), and greater overall mortality of patients (p < 0.001), compared to the pre-pandemic period. Positive COVID-19 and/or troponin test results were associated with increased mortality (p < 0.001), though the temporal risk profile differed. Conclusions: The first wave of the COVID-19 pandemic was associated with significant changes not just in presentation, but also the investigation, management, and outcomes of patients presenting with suspected myocardial injury or MI

Cardiovascular effects of covid-19 – what do we know and where should we go?

Amongst the many challenges of the global pandemic, understanding the effects of coronavirus disease 2019 (COVID-19) on the cardiovascular system has emerged as a key priority. Emerging data indicate possible roles for cardiac biomarkers and cardiac imaging in the prognostic assessment of these patients, as well as implications of the vascular endothelium in the pathogenesis of the condition. From a therapeutic perspective, early data suggest that the provision of well-established treatments for cardiovascular disease, whether with angiotensin-converting enzyme inhibitors or primary angioplasty, is likely to be beneficial. These early data are of limited quality however, and robust studies are needed to address many of the key remaining questions

Coronary Artery Disease in Patients with Severe Mental Illness

Severe mental illnesses (SMI), such as schizophrenia and bipolar disorder, are associated with a decrease in life expectancy of up to two decades compared with the general population, with cardiovascular disease as the leading cause of death. SMI is associated with increased cardiovascular risk profile and early onset of incident cardiovascular disease. Following an acute coronary syndrome, patients with SMI have a worse prognosis, but are less likely to receive invasive treatment. In this narrative review, the management of coronary artery disease in patients with SMI is discussed, and avenues for future research are highlighted

Diagnostic signature for heart failure with preserved ejection fraction (HFpEF):a machine learning approach using multi-modality electronic health record data

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is thought to be highly prevalent yet remains underdiagnosed. Evidence-based treatments are available that increase quality of life and decrease hospitalization. We sought to develop a data-driven diagnostic model to predict from electronic health records (EHR) the likelihood of HFpEF among patients with unexplained dyspnea and preserved left ventricular EF. METHODS AND RESULTS: The derivation cohort comprised patients with dyspnea and echocardiography results. Structured and unstructured data were extracted using an automated informatics pipeline. Patients were retrospectively diagnosed as HFpEF (cases), non-HF (control cohort I), or HF with reduced EF (HFrEF; control cohort II). The ability of clinical parameters and investigations to discriminate cases from controls was evaluated by extreme gradient boosting. A likelihood scoring system was developed and validated in a separate test cohort. The derivation cohort included 1585 consecutive patients: 133 cases of HFpEF (9%), 194 non-HF cases (Control cohort I) and 1258 HFrEF cases (Control cohort II). Two HFpEF diagnostic signatures were derived, comprising symptoms, diagnoses and investigation results. A final prediction model was generated based on the averaged likelihood scores from these two models. In a validation cohort consisting of 269 consecutive patients [with 66 HFpEF cases (24.5%)], the diagnostic power of detecting HFpEF had an AUROC of 90% (P < 0.001) and average precision of 74%. CONCLUSION: This diagnostic signature enables discrimination of HFpEF from non-cardiac dyspnea or HFrEF from EHR and can assist in the diagnostic evaluation in patients with unexplained dyspnea. This approach will enable identification of HFpEF patients who may then benefit from new evidence-based therapies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12872-022-03005-w