Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients.

Grayscale statistical attributes analysed for 513 extract images taken from pulmonary computed tomography (CT) scan slices of 57 individuals (49 confirmed COVID-19 positive; eight confirmed COVID-19 negative) are able to accurately predict a visual score (VS from 0 to 4) used by a clinician to assess the severity of lung abnormalities in the patients. Some of these attributes can be used graphically to distinguish useful but overlapping distributions for the VS classes. Using machine and deep learning (ML/DL) algorithms with twelve grayscale image attributes as inputs enables the VS classes to be accurately distinguished. A convolutional neural network achieves this with better than 96% accuracy (only 18 images misclassified out of 513) on a supervised learning basis. Analysis of confusion matrices enables the VS prediction performance of ML/DL algorithms to be explored in detail. Those matrices demonstrate that the best performing ML/DL algorithms successfully distinguish between VS classes 0 and 1, which clinicians cannot readily do with the naked eye. Just five image grayscale attributes can also be used to generate an algorithmically defined scoring system (AS) that can also graphically distinguish the degree of pulmonary impacts in the dataset evaluated. The AS classification illustrated involves less overlap between its classes than the VS system and could be exploited as an automated expert system. The best-performing ML/DL models are able to predict the AS classes with better than 99% accuracy using twelve grayscale attributes as inputs. The decision tree and random forest algorithms accomplish that distinction with just one classification error in the 513 images tested.

Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients

Grayscale statistical attributes analysed for 513 extract images taken from pulmonary computed tomography (CT) scan slices of 57 individuals (49 confirmed COVID-19 positive;eight confirmed COVID-19 negative) are able to accurately predict a visual score (VS from 0 to 4) used by a clinician to assess the severity of lung abnormalities in the patients. Some of these attributes can be used graphically to distinguish useful but overlapping distributions for the VS classes. Using machine and deep learning (ML/DL) algorithms with twelve grayscale image attributes as inputs enables the VS classes to be accurately distinguished. A convolutional neural network achieves this with better than 96% accuracy (only 18 images misclassified out of 513) on a supervised learning basis. Analysis of confusion matrices enables the VS prediction performance of ML/DL algorithms to be explored in detail. Those matrices demonstrate that the best performing ML/DL algorithms successfully distinguish between VS classes 0 and 1, which clinicians cannot readily do with the naked eye. Just five image grayscale attributes can also be used to generate an algorithmically defined scoring system (AS) that can also graphically distinguish the degree of pulmonary impacts in the dataset evaluated. The AS classification illustrated involves less overlap between its classes than the VS system and could be exploited as an automated expert system. The best-performing ML/DL models are able to predict the AS classes with better than 99% accuracy using twelve grayscale attributes as inputs. The decision tree and random forest algorithms accomplish that distinction with just one classification error in the 513 images tested.

Evidence on Internal Audit Quality from Transitioning to Remote Audits because of COVID-19

As in-person audits were banned by governments and by company policies due to the COVID-19 pandemic, internal auditors had to transition to remote audits to perform their work. Based on survey responses of internal auditors who have conducted both remote and in-person audits, we find that internal auditors perceive no difference in the efficiency and effectiveness of and stakeholders' reliance on results from remote and in-person audits when considering all responses. However, we also find that perceived efficiency and effectiveness increase the more experience internal auditors have with remote audits. Supplemental analyses show that support from the auditee, but not management or the audit committee, is a central determinant of perceived remote audit success. It is important for internal auditors to consider this later finding in the design of remote audits as it indicates the importance of building support with the auditee to have a successful remote auditing experience.

Ozone air concentration trend attributes assist hours-ahead forecasts from univariate recorded data avoiding exogenous data inputs

Multiple trend attributes extracted from univariate hourly ozone recorded data can be effective in forecasting ozone concentrations at near surface sites for t0 to t + 12 h ahead without recourse to exogeneous variables. The method is evaluated with datasets from three cities less than 100 km apart in central/eastern England, each with more than forty thousand data records for the period 2016 to 2020. The 2020 recorded ozone distribution values are higher in all three cities than for 2016 to 2019 due, at least in part, to COVID-19 lockdowns limiting vehicle emissions. Fifteen attributes extracted from the recorded ozone trend for the past twelve hours are added to each hourly data record. The attributes include seasonal components, some prior-hour values, averages, differences and rates of change. Two multi-linear regression and eight machine-learning (ML) models are used to predict 2019 and 2020 hourly ozone values with the attribute-endowed datasets. The forecasting accuracy of all but one of these models outperforms that of an autoregression model applied to the univariate recorded ozone trends. The support vector machine model achieves the highest ozone forecasting accuracy for hours ahead t0 to t + 12. However, nine of the other models also providing credible and consistent forecasts for the datasets for all three cities. Coefficient analysis of the multi-linear regression models reveals the flexibility with which each of the trend attributes is used in predicting different hours ahead in the t0 to t + 12 range. The attribute-endowed datasets also enable the ML models to assign different relative weights to each attribute for the different hours-ahead being forecasts. This capability introduces additional dimensions that are not available to autoregressive or moving-average models applied to univariate ozone trends.

Grayscale image statistics of COVID‐19 patient CT scans characterize lung condition with machine and deep learning

Background Grayscale image attributes of computed tomography (CT) of pulmonary scans contain valuable information relating to patients with respiratory ailments. These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID‐19. Method Five hundred thirteen CT images relating to 57 patients (49 with COVID‐19;8 free of COVID‐19) were collected at Namazi Medical Centre (Shiraz, Iran) in 2020 and 2021. Five visual scores (VS: 0, 1, 2, 3, or 4) are clinically assigned to these images with the score increasing with the severity of COVID‐19‐related lung conditions. Eleven deep learning and machine learning techniques (DL/ML) are used to distinguish the VS class based on 12 grayscale image attributes. Results The convolutional neural network achieves 96.49% VS accuracy (18 errors from 513 images) successfully distinguishing VS Classes 0 and 1, outperforming clinicians’ visual inspections. An algorithmic score (AS), involving just five grayscale image attributes, is developed independently of clinicians’ assessments (99.81% AS accuracy;1 error from 513 images). Conclusion Grayscale CT image attributes can be successfully used to distinguish the severity of COVID‐19 lung damage. The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes. Grayscale CT image statistics accurately distinguish the severity of COVID‐19‐related lung conditions Highlights Grayscale image statistics of CT scans can effectively classify lung abnormalities Graphical trends of grayscale statistics distinguish visual assessments COVID‐19 classes Machine/deep learning algorithms predict severity from image grayscale attributes Algorithmic class systems can be established using just five grayscale attributes Confusion matrices provide detailed insight to algorithm prediction capabilities

Same-Day Discharge Post-Transcatheter Aortic Valve Replacement During the COVID-19 Pandemic: The Multicenter PROTECT TAVR Study.

OBJECTIVES: The aim of this study was to determine the safety and efficacy of same-day discharge (SDD) after transcatheter aortic valve replacement (TAVR) during the COVID-19 pandemic. BACKGROUND: The COVID-19 pandemic has placed significant stress on health care systems worldwide. SDD in highly selected TAVR patients can facilitate the provision of essential cardiovascular care while managing competing COVID-19 resource demands. METHODS: Patient selection for SDD was at the discretion of the local multidisciplinary heart team, across 7 international sites. The primary outcome was a composite of cardiovascular death, stroke, myocardial infarction, all-cause readmission, major vascular complications, and new permanent pacemaker (PPM) implantation. RESULTS: From March 2020 to August 2021, 124 of 2,100 patients who underwent elective transfemoral TAVR were selected for SDD. The average age was 78.9 ± 7.8 years, the median Society of Thoracic Surgeons score was 2.4 (IQR: 1.4-4.2), and 32.3% (n = 40) had preexisting PPMs. There were no major vascular complications, strokes, or deaths during the index admission. One patient (0.8%) required PPM implantation for complete heart block and was discharged the same day. No patient required a PPM between discharge home and 30-day follow-up. The composite of cardiovascular death, stroke, myocardial infarction, all-cause readmission, major vascular complications, and new PPM at 30 days occurred in 5.7% patients (n = 6 of 106). CONCLUSIONS: SDD post-TAVR is safe and feasible in selected patients at low risk for adverse clinical events postdischarge. This strategy may have a potential role in highly selected patients even when the COVID-19 pandemic abates.

Local Integrated Air Quality Predictions from Meteorology (2015 to 2020) with Machine and Deep Learning Assisted by Data Mining

Overall air quality local indices can usefully be established by combining normalised values of common individual pollutant values. This reveals distinctive seasonal trends that are strongly influenced by local meteorological conditions. A newly compiled dataset for 2015 to 2020 covering Dallas County (USA), combining six pollutants into a combined local area benchmark (CLAB), is assessed in terms of eleven meteorological variables. It is possible to distinguish the effects of lock-down induced impacts in the CLAB index and some of its component pollutants during 2020. Nine machine learning and three deep learning algorithms are compared in their abilities to predict CLAB from the meteorological variables on supervised and unseen bases. Prediction results for 2019 and 2020 are distinctive for annual and quarterly timeframes. In-depth prediction outlier analysis using a transparent data-matching algorithm provides insight to the few data records for which CLAB is not accurately predicted from ground-level meteorological data.

Same Day Discharge during the COVID-19 Pandemic in Highly Selected Transcatheter Aortic Valve Replacement Patients.

Background: Transcatheter aortic valve replacement (TAVR) with a standardized clinical pathway allows most patients to achieve safe next-day discharge. This approach has been successfully implemented across global centers as part of the Benchmark Program. Considering restricted hospital resources resulting from the COVID-19 pandemic, a modified same day discharge (SDD) clinical pathway was implemented for selected TAVR patients at a single Benchmark site. Methods: All patients accepted for TAVR were assessed for the SDD clinical pathway. Eligibility criteria included adequate social support and accessibility to the TAVR program post-discharge. Patients with preexisting conduction disease were excluded. The clinical pathway comprised of mobilization, bloodwork and electrocardiogram 4 hours post-TAVR and discharge ≥8 hours following groin hemostasis. Results: From June to December 2020, 142 patients underwent TAVR at a single community Benchmark site. Of those, 29 highly selected patients were successfully discharged the same day using the SDD clinical pathway. There were no vascular access complications, permanent pacemaker (PPM) implantation, or mortality in the SDD group during index admission or at 30-day follow-up. When compared to a standard therapy group, there was no statistically significant difference in 30-day cardiovascular readmission. Conclusions: This study demonstrates the safety and feasibility of same day discharge post-TAVR in a highly selected cohort of patients, with no observable difference in safety outcomes when compared to patients who were discharged according to standard institutional practice.Abbreviations: AS: aortic stenosis; ACT: Activated clotting time; AV: atrioventricular; AVB: atrioventricular block; BBB: bundle branch block; CAIC: Canadian Society for Cardiovascular Angiography; CCL: cardiac catheterization laboratory; CT: Computed topography; CV: cardiovascular; IQR: Interquartile Range; IVCD: intraventricular conduction delay; LBBB: left bundle branch block; LOS: length of stay; NDD: next day discharge; PPM: permanent pacemaker; RBBB: right bundle branch block; SCAI: Society for Cardiovascular Angiography and Intervention; SD: standard deviation; SDD: same day discharge; ST: standard therapy; STS PROM: society of thoracic surgeons predicted risk of mortality; TAVR: transcatheter aortic valve replacement; TF: transfemoral; THV: transcatheter heart valve; TTE: transthoracic echocardiogram; VARC: Valve Academic Research Consortium.

INTERASPIRE: an International Survey of Coronary Patients; Their Cardiometabolic, Renal and Biomarker Status; and the Quality of Preventive Care Delivered in All WHO Regions : In Partnership with the World Heart Federation, European Society of Cardiology, Asia Pacific Society of Cardiology, InterAmerican Society of Cardiology, and PanAfrican Society of Cardiology.

PURPOSE OF REVIEW: To describe the INTERASPIRE scientific protocol-an international survey of secondary prevention of coronary heart disease (CHD). RECENT FINDINGS: This international survey is being conducted through National Societies of Cardiology in selected countries from each of the six WHO regions and has the following overall aims: (i) describe prevalence of cardiometabolic and renal risk factors together with biomarkers in CHD patients; (ii) describe current risk factor management through lifestyle changes and cardioprotective drug therapies; (iii) provide an objective assessment of clinical implementation of preventive care by comparison with the lifestyle and risk factor targets defined in international and national guidelines; (iv) investigate the reasons for variation in preventive cardiology practice between regions and countries; and (v) promote the principles of best preventive cardiology practice. This international survey will provide a unique picture of CHD patients; their cardiometabolic, renal and biomarker status; lifestyle and therapeutic management; and the quality of preventive care provided in all WHO regions.

The COVID-19 Pandemic and Coronary Angiography for ST-Elevation Myocardial Infarction, Use of Mechanical Support, and Mechanical Complications in Canada: A Canadian Association of Interventional Cardiology National Survey.

BACKGROUND: As a result of the COVID-19 pandemic first wave, reductions in ST-elevation myocardial infarction (STEMI) invasive care, ranging from 23% to 76%, have been reported from various countries. Whether this change had any impact on coronary angiography (CA) volume or on mechanical support device use for STEMI and post-STEMI mechanical complications in Canada is unknown. METHODS: We administered a Canada-wide survey to all cardiac catheterization laboratory directors, seeking the volume of CA use for STEMI performed during the period from March 1 2020 to May 31, 2020 (pandemic period), and during 2 control periods (March 1, 2019 to May 31, 2019 and March 1, 2018 to May 31, 2018). The number of left ventricular support devices used, as well as the number of ventricular septal defects and papillary muscle rupture cases diagnosed, was also recorded. We also assessed whether the number of COVID-19 cases recorded in each province was associated with STEMI-related CA volume. RESULTS: A total of 41 of 42 Canadian catheterization laboratories (98%) provided data. There was a modest but statistically significant 16% reduction (incidence rate ratio [IRR] 0.84; 95% confidence interval 0.80-0.87) in CA for STEMI during the first wave of the pandemic, compared to control periods. IRR was not associated with provincial COVID-19 caseload. We observed a 26% reduction (IRR 0.74; 95% confidence interval 0.61-0.89) in the use of intra-aortic balloon pump use for STEMI. Use of an Impella pump and mechanical complications from STEMI were exceedingly rare. CONCLUSIONS: We observed a modest 16% decrease in use of CA for STEMI during the pandemic first wave in Canada, lower than the level reported in other countries. Provincial COVID-19 caseload did not influence this reduction.

INTRODUCTION: Après la première vague de la pandémie de COVID-19, de nombreux pays ont déclaré une réduction de 23 % à 76 % des soins invasifs de l'infarctus du myocarde avec élévation du segment ST (STEMI). On ignore si ce changement a entraîné des répercussions sur le volume d'angiographies coronariennes (AC) ou sur l'utilisation des dispositifs d'assistance mécanique lors de STEMI et des complications mécaniques post-STEMI au Canada. MÉTHODES: Nous avons réalisé un sondage pancanadien auprès de tous les directeurs de laboratoire de cathétérisme cardiaque pour obtenir le volume d'utilisation des AC lors des STEMI réalisées durant la période du 1er mars 2020 au 31 mai 2020 (période de pandémie) et durant 2 périodes témoins (1er mars 2019 au 31 mai 2019 et 1er mars 2018 au 31 mai 2018). Le nombre de dispositifs d'assistance ventriculaire gauche utilisés et le nombre de cas de communications interventriculaires et de ruptures du muscle papillaire diagnostiqués ont également été enregistrés. Nous avons aussi évalué si le nombre de cas de COVID-19 enregistrés dans chaque province était associé au volume d'AC liées aux STEMI. RÉSULTATS: Au total, 41 des 42 laboratoires canadiens de cathétérisme (98 %) ont fourni des données. Lors de la comparaison de la première vague de la pandémie aux périodes témoins, nous avons noté une réduction modeste, mais significative, sur le plan statistique de 16 % (ratio du taux d'incidence [RTI] 0,84; intervalle de confiance à 95 % 0,80-0,87) des AC lors de STEMI. Le RTI n'était pas associé au nombre provincial de cas de COVID-19. Nous avons observé une réduction de 26 % (RTI 0,74; intervalle de confiance à 95 % 0,61-0,89) de l'utilisation de pompes à ballonnet intra-aortique lors de STEMI. L'utilisation d'une pompe Impella et les complications mécaniques après les STEMI étaient extrêmement rares. CONCLUSIONS: Nous avons observé une diminution modeste de 16 % de l'utilisation des AC lors de STEMI durant la première vague de la pandémie au Canada, soit une diminution plus faible que ce que les autres pays ont signalé. Le nombre provincial de cas de COVID-19 n'a pas influencé cette réduction.

Correction: Management of Cardiovascular Disease Patients With Confirmed or Suspected COVID-19 in Limited Resource Settings.

[This corrects the article DOI: 10.5334/gh.823.].

Cardiovascular Care Delivery During the Second Wave of COVID-19 in Canada.

Hospitals and ambulatory facilities significantly reduced cardiac care delivery in response to the first wave of the COVID-19 pandemic. The deferral of elective cardiovascular procedures led to a marked reduction in health care delivery with a significant impact on optimal cardiovascular care. International and Canadian data have reported dramatically increased wait times for diagnostic tests and cardiovascular procedures, as well as associated increased cardiovascular morbidity and mortality. In the wake of the demonstrated ability to rapidly create critical care and hospital ward capacity, we advocate a different approach during the second and possible subsequent COVID-19 pandemic waves. We suggest an approach, informed by local data and experience, that balances the need for an expected rise in demand for health care resources to ensure appropriate COVID-19 surge capacity with continued delivery of essential cardiovascular care. Incorporating cardiovascular care leaders into pandemic planning and operations will help health care systems minimise cardiac care delivery disruptions while maintaining critical care and hospital ward surge capacity and continuing measures to reduce transmission risk in health care settings. Specific recommendations targeting the main pillars of cardiovascular care are presented: ambulatory, inpatient, procedural, diagnostic, surgical, and rehabilitation.

orth merican OVID-19 ST-Segment-Elevation yocardial nfarction (NACMI) registry: Rationale, design, and implications

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that causes coronavirus disease 2019 (COVID-19), has resulted in a global pandemic. Patients with cardiovascular risk factors or established cardiovascular disease are more likely to experience severe or critical COVID-19 illness and myocardial injury is a key extra-pulmonary manifestation. These patients frequently present with ST-elevation on an electrocardiogram (ECG) due to multiple etiologies including obstructive, non-obstructive, and/or angiographically normal coronary arteries. The incidence of ST-elevation myocardial infarction (STEMI) mimics in COVID-19-positive hospitalized patients, and the association with morbidity and mortality is unknown. Understanding the natural history and appropriate management of COVID-19 patients presenting with ST elevation is essential to inform patient management decisions and protect healthcare workers.Methods: The Society for Cardiovascular Angiography and Interventions (SCAI) and The Canadian Association of Interventional Cardiology (CAIC) in conjunction with the American College of Cardiology Interventional Council have collaborated to create a multi-center observational registry, NACMI. This registry will enroll confirmed COVID-19 patients and persons under investigation (PUI) with new ST-segment elevation or new onset left bundle branch block (LBBB) on the ECG with clinical suspicion of myocardial ischemia. We will compare demographics, clinical findings, outcomes and management of these patients with a historical control group of over 15,000 consecutive STEMI activation patients from the Midwest STEMI Consortium using propensity matching. The primary clinical outcome will be in- hospital major adverse cardiovascular events (MACE) defined as composite of all-cause mortality, stroke, recurrent MI, and repeat unplanned revascularization in COVID-19 confirmed or PUI. Secondary outcomes will include the following: reporting of etiologies of ST Elevation;cardiovascular mortality due to myocardial infarction, cardiac arrest and /or shock;individual components of the primary outcome;composite primary outcome at 1 year;as well as ECG and angiographic characteristics.Conclusion: The multicenter NACMI registry will collect data regarding ST elevation on ECG in COVID-19 patients to determine the etiology and associated clinical outcomes. The collaboration and speed with which this registry has been created, refined, and promoted serves as a template for future research endeavors.

Management of Cardiovascular Disease Patients With Confirmed or Suspected COVID-19 in Limited Resource Settings.

In this paper, we provide recommendations on the management of cardiovascular disease (CVD) among patients with confirmed or suspected coronavirus disease (COVID-19) to facilitate the decision making of healthcare professionals in low resource settings. The emergence of novel coronavirus disease, also known as Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2), has presented an unprecedented global challenge for the healthcare community. The ability of SARS-CoV-2 to get transmitted during the asymptomatic phase and its high infectivity have led to the rapid transmission of COVID-19 beyond geographic regions, leading to a pandemic. There is concern that COVID-19 is cardiotropic, and it interacts with the cardiovascular system on multiple levels. Individuals with established CVD are more susceptible to severe COVID-19. Through a consensus approach involving an international group this WHF statement summarizes the links between cardiovascular disease and COVID-19 and present some practical recommendations for the management of hypertension and diabetes, acute coronary syndrome, heart failure, rheumatic heart disease, Chagas disease, and myocardial injury for patients with COVID-19 in low-resource settings. This document is not a clinical guideline and it is not intended to replace national clinical guidelines or recommendations. Given the rapidly growing burden posed by COVID-19 illness and the associated severe prognostic implication of CVD involvement, further research is required to understand the potential mechanisms linking COVID-19 and CVD, clinical presentation, and outcomes of various cardiovascular manifestations in COVID-19 patients.

Guiding Cardiac Care During the COVID-19 Pandemic: How Ethics Shapes Our Health System Response.

The COVID-19 pandemic has raised ethical questions for the cardiovascular leader and practitioner. Attention has been redirected from a system that focuses on individual patient benefit toward one that focuses on protecting society as a whole. Challenging resource allocation questions highlight the need for a clearly articulated ethics framework that integrates principled decision making into how different cardiovascular care services are prioritized. A practical application of the principles of harm minimisation, fairness, proportionality, respect, reciprocity, flexibility, and procedural justice is provided, and a model for prioritisation of the restoration of cardiovascular services is outlined. The prioritisation model may be used to determine how and when cardiovascular services should be continued or restored. There should be a focus on an iterative and responsive approach to broader health care system needs, such as other disease groups and local outbreaks.