Impact of air pollution on ischemic heart disease: Evidence, mechanisms, clinical perspectives.

Ambient air pollution, and especially particulate matter (PM) air pollution <2.5 µm in diameter (PM2.5), has clearly emerged as an important yet often overlooked risk factor for atherosclerosis and ischemic heart disease (IHD). In this review, we examine the available evidence demonstrating how acute and chronic PM2.5 exposure clinically translates into a heightened coronary atherosclerotic burden and an increased risk of acute ischemic coronary events. Moreover, we provide insights into the pathophysiologic mechanisms underlying PM2.5-mediated atherosclerosis, focusing on the specific biological mechanism through which PM2.5 exerts its detrimental effects. Further, we discuss about the possible mechanisms that explain the recent findings reporting a strong association between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, increased PM2.5 exposure, and morbidity and mortality from IHD. We also address the possible mitigation strategies that should be implemented to reduce the impact of PM2.5 on cardiovascular morbidity and mortality, and underscoring the strong need of clinical trials demonstrating the efficacy of specific interventions (including both PM2.5 reduction and/or specific drugs) in reducing the incidence of IHD. Finally, we introduce the emerging concept of the exposome, highlighting the close relationship between PM2.5 and other environmental exposures (i.e.: traffic noise and climate change) in terms of common underlying pathophysiologic mechanisms and possible mitigation strategies.

Myocardial Injury Portends a Higher Risk of Mortality and Long-Term Cardiovascular Sequelae after Hospital Discharge in COVID-19 Survivors.

Background: Cardiovascular sequelae after COVID-19 are frequent. However, the predictors for their occurrence are still unknown. In this study, we aimed to assess whether myocardial injury during COVID-19 hospitalization is associated to CV sequelae and death after hospital discharge. Methods: In this prospective observational study, consecutive patients who were admitted for COVID-19 in a metropolitan COVID-19 hub in Italy, between March 2021 and January 2022, with a ≥ 1 assessment of high sensitivity cardiac troponin I (hs-cTnI) were included in the study, if they were alive at hospital discharge. Myocardial injury was defined as elevation hs-cTnI > 99th percentile of the upper reference limit. The incidence of all-cause mortality and major adverse cardiovascular and cerebrovascular events (MACCE, including cardiovascular death, admission for acute or chronic coronary syndrome, hospitalization for heart failure, and stroke/transient ischemic attack) at follow-up were the primary outcomes. Arrhythmias, inflammatory heart diseases, and/or thrombotic disorders were analyzed as well. Results: Among the 701 COVID-19 survivors (mean age 66.4 ± 14.4 years, 40.2% female), myocardial injury occurred in 75 (10.7%) patients. At a median follow-up of 270 days (IQR 165, 380), all-cause mortality (21.3% vs. 6.1%, p < 0.001), MACCE (25.3% vs. 4.5%, p < 0.001), arrhythmias (9.3% vs. 5.0%, p = 0.034), and inflammatory heart disease (8.0% vs. 1.1%, p < 0.001) were more frequent in patients with myocardial injury compared to those without. At multivariate analysis, myocardial injury (HR 1.95 [95% CI:1.05-3.61]), age (HR 1.09 [95% CI:1.06-1.12]), and chronic kidney disease (HR 2.63 [95% CI:1.33-5.21]) were independent predictors of death. Myocardial injury (HR 3.92 [95% CI:2.07-7.42]), age (HR 1.05 [95% CI:1.02-1.08]), and diabetes (HR 2.35 [95% CI:1.25-4.43]) were independent predictors of MACCE. Conclusion: In COVID-19 survivors, myocardial injury during the hospital stay portends a higher risk of mortality and cardiovascular sequelae and could be considered for the risk stratification of COVID-19 sequelae in patients who are successfully discharged.

CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.

CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research.

Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes.

'Here comes the story of the Hurricane': a case report of AL cardiac amyloidosis and myocardial bridging.

Background: Cardiac amyloidosis (CA) is a rapidly progressive infiltrative cardiomyopathy, whose role is emerging as a not-so-rare disorder leading to heart failure (HF). Myocardial bridge (MB) is the most common inborn coronary artery variant, and its clinical relevance is still matter of debate. The exceptional coexistence of these two conditions could accelerate disease progression and worsen the already compromised clinical conditions. Case summary: We present the case of a 76-year-old female patient experiencing relapsing HF decompensation and presenting to our centre with dyspnoea at rest and severe peripheral congestion. Echocardiogram showed severe concentric hypertrophy, severe biventricular contractile dysfunction, and third-degree diastolic dysfunction. Coronary angiography excluded epicardial atherosclerotic disease, though displaying a long intramyocardial course of left anterior descending artery. Physiological invasive test was achieved in terms of instantaneous wave-free ratio (iFR), both at baseline and after inotropic and chronotropic stimuli, and attested haemodynamic significance. Concurrently, the diagnostic flow chart for CA was accomplished, by means of both invasive (periumbilical fat biopsy, bone marrow aspiration) and non-invasive tests (99mTc-diphosphonate scintigraphy, serum-urine immunofixation) that confirmed the suspect of primary amyloidosis. Acute HF therapy was personalized according to the singularity of the case, avoiding both nitrates and beta-blockers, then first cycle of chemotherapy was started. Discussion: Our clinical case shows a unique interaction between infiltrative cardiomyopathy and coronary artery abnormality. Amyloidosis can contribute to the ischaemic burden of the MB and this may, in turn, abbreviate the path to HF decompensation.

Let Us Not Forget Cardiovascular Diseases During COVID-19 Pandemic: the Role of Cardiac Prevention and Rehabilitation.

During novel coronavirus disease (COVID-19) pandemic, major focus of health service is on mitigating the spread of infection and treating the acute severe respiratory syndrome of affected patients. However, from available initial data, it has been shown that cardiovascular and metabolic diseases are responsible for a worse clinical outcome of COVID-19 patients and, on the other hand, myocardial damage might occur as a consequence of infection. Therefore, we propose not to forget the heart during pandemic and to focus on cardiac care in at least three phases: prevention, acute phase, and rehabilitation. We report rationale, scientific evidence, and clinical model for the proposed three-phase program.

A real-time integrated framework to support clinical decision making for covid-19 patients.

BACKGROUND: The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies. METHODS: The information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented. RESULTS: The Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level. INTERPRETATION: The COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.

Acute coronary syndrome in neoatherosclerosis with major stent malapposition and OCT-guided PCI

Aims Due to its bidimensional nature, angiography is not always sufficient to accurately define coronary lesions, in particular when they are ambiguous or indeterminate. Intracoronary imaging, such as intravascular ultrasound or optical coherence tomography (OCT), is often useful in these cases to better characterize the ambiguous angiographic images, to identify the culprit lesion during acute coronary syndrome (ACS) and to guide percutaneous coronary intervention (PCI). Methods and results We report a case of a 61-year-old male with multiple cardiovascular risk factors and a previous ST-segment elevation myocardial infarction treated by PCI of the right coronary artery (RCA) about 7 years before, wo was admitted to our emergency department after acute onset chest pain. At the time of admission, his ECG was normal and cardiac troponin was below the upper reference limit of normality with positive molecular SARS-CoV-2 diagnostic test. Echocardiogram disclosed a mild left ventricular dysfunction with inferior wall hypokinesia. Coronary angiography showed a moderate in-stent restenosis at mid RCA and a hazy, undetermined image at the proximal edge of the previously implanted stent. Left coronary artery angiography showed only diffuse atherosclerotic disease without significant stenoses and a myocardial bridge at the mid tract of left anterior descending artery. OCT pullback of RCA to better characterize the undetermined lesions shown by angiography. OCT revealed significant neointima hyperplasia and a focal area of neoatherosclerosis with unstable features (fissure/microthrombi) at mid RCA. Severe stent strut malapposition embedded neointimal hyperplasia was observed at the proximal stent edge, resulting in ‘dual’ lumen appearance. The two lesions were treated with a single 3.5/48 mm everolimus-eluting stent (stent-in-stent), which was post-dilated with a 3.5/20 mm non-compliant balloon (18 atm) in the mid-to-distal segments, and 4.5/15 mm (16 atm) and 5.0/8 mm (14 atm) semi-compliant balloons in the proximal stent segment. Post-PCI OCT imaging confirmed good stent expansion and apposition. Our case demonstrates the utility of OCT in clarifying the aetiology of ambiguous angiographic lesions and as a guide for PCI. Indeed, the ‘hazy’ appearance on the angiograms corresponded to the major stent malapposition covered by neointima disclosed by OCT as a ‘dual-lumen’. Of note, OCT allowed to confirm the correct guidewire position in the ‘true’ lumen preventing a crush of the previously implanted stent. OCT was also useful as a diagnostic modality for the identification and characterization of the mechanism underlying the ACS (neoatherosclerosis instability). Conclusions Due to its unprecedented spatial resolution, OCT enables an ‘optical biopsy’ of the coronary artery wall and intrastent tissue. Therefore, OCT imaging should be considered when lesions are ambiguous or indetermined by coronary angiography to guide the diagnosis and treatments of ACS patients. OCT imaging is also useful to guide stenting and to optimize PCI result, and its impact on clinical outcome is under investigation in large randomized clinical trials.

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.

Incidence and Predictors of Thrombotic Complications in 4742 Patients with COVID-19 or Other Acute Infectious Respiratory Diseases: A Propensity Score-Matched Study.

BACKGROUND: A prothrombotic state, attributable to excessive inflammation, cytokine storm, hypoxia, and immobilization, is a feature of SARS-CoV-2 infection. Up to 30% of patients with severe COVID-19 remain at high risk of thromboembolic events despite anticoagulant administration, with adverse impact on in-hospital prognosis. METHODS: We retrospectively studied 4742 patients with acute infectious respiratory disease (AIRD); 2579 were diagnosed to have COVID-19 and treated with heparin, whereas 2163 had other causes of AIRD. We compared the incidence and predictors of total, arterial, and venous thrombosis, both in the whole population and in a propensity score-matched subpopulation of 3036 patients (1518 in each group). RESULTS: 271 thrombotic events occurred in the whole population: 121 (4.7%) in the COVID-19 group and 150 (6.9%) in the no-COVID-19 group (p < 0.001). No differences in the incidence of total (p = 0.11), arterial (p = 0.26), and venous (p = 0.38) thrombosis were found between the two groups after adjustment for confounding clinical variables and in the propensity score-matched subpopulation. Likewise, there were no significant differences in bleeding rates between the two groups. Clinical predictors of arterial thrombosis included age (p = 0.006), diabetes mellitus (p = 0.034), peripheral artery disease (p < 0.001), and previous stroke (p < 0.001), whereas history of solid cancer (p < 0.001) and previous deep vein thrombosis (p = 0.007) were associated with higher incidence of venous thrombosis. CONCLUSIONS: Hospitalized patients with COVID-19 treated with heparin do not seem to show significant differences in the cumulative incidence of thromboembolic events as well as in the incidence of arterial and venous thrombosis separately, compared with AIRD patients with different etiological diagnosis.

2019 novel Coronavirus: current knowledge, cardiovascular implications and management.

From first cases reported on December 31, 2019, in Wuhan, Hubei-China, SARS-CoV2 has spread worldwide and finally the World Health Organization declared the pandemic status. We summarize what makes SARS-CoV2 different from previous highly pathogenic coronaviruses and why it is so contagious, with focus on its clinical presentation and diagnosis, which is mandatory to start the appropriate management and reduce the transmission. As far as infection pathophysiology is still not completely clarified, this review focuses also on the cardiovascular (CV) implication of COVID-19 and the capability of this virus to cause direct myocardial injury, myocarditis and other CV manifestations. Furthermore, we highlight the relationship between the virus, enzyme ACE2 and ACE inhibitors. Clinical management involves the intensive care approach with intubation and mechanical ventilation in the most serious cases and drug therapy with several apparently promising old and new molecules. Aim of this review is then to summarize what is actually known about the SARS-CoV2 and its cardiovascular implications.

Electrocardiographic findings at presentation and clinical outcome in patients with SARS-CoV-2 infection.

AIMS: The main severe complications of SARS-CoV-2 infection are pneumonia and respiratory distress syndrome. Recent studies, however, reported that cardiac injury, as assessed by troponin levels, is associated with a worse outcome in these patients. No study hitherto assessed whether the simple standard electrocardiogram (ECG) may be helpful for risk stratification in these patients. METHODS AND RESULTS: We studied 324 consecutive patients admitted to our Emergency Department with a confirmed diagnosis of SARS-CoV-2 infection. Standard 12-lead ECG recorded on admission was assessed for cardiac rhythm and rate, atrioventricular and intraventricular conduction, abnormal Q/QS wave, ST segment and T wave changes, corrected QT interval, and tachyarrhythmias. At a mean follow-up of 31 ± 11 days, 44 deaths occurred (13.6%). Most ECG variables were significantly associated with mortality, including atrial fibrillation (P = 0.002), increasing heart rate (P = 0.002), presence of left bundle branch block (LBBB; P < 0.001), QRS duration (P <0 .001), a QRS duration of ≥110 ms (P < 0.001), ST segment depression (P < 0.001), abnormal Q/QS wave (P = 0.034), premature ventricular complexes (PVCs; P = 0.051), and presence of any ECG abnormality [hazard ratio (HR) 4.58; 95% confidence interval (CI) 2.40-8.76; P < 0.001]. At multivariable analysis, QRS duration (P = 0.002), QRS duration ≥110 ms (P = 0.03), LBBB (P = 0.014) and presence of any ECG abnormality (P = 0.04) maintained a significant independent association with mortality. CONCLUSION: Our data show that standard ECG can be helpful for an initial risk stratification of patients admitted for SARS-CoV-2 infectious disease.