Remote Clinical Quality Management of Endovascular Care

To assess the effectiveness of remote clinical quality management of endovascular Aim care. The system of clinical quality management of medical care in myocardial infarction (MI) including the quality of remote control of endovascular care was developed and introduced into the health care system of the Moscow Region as a part of the comprehensive study in 2008-2020. The number of people under the study was 8375. The ground for assessing the effectiveness of remote clinical management in 2019-2020 was the health care system of megapolis. Based on the analysis of 2966 endovascular procedures protocols, the treatment tactics effectiveness of intraoperative decisions was studied after an emergency coronary angiography (ECA) had been performed by interventional cardiologists. The Methods system of remote clinical quality management of endovascular care included a complex of audiovisual communications, computer system processes, mentoring and the algorithm for making an intraoperative decision. The effectiveness of remote clinical quality management of endovascular care was investigated on the number of percutaneous coronary interventions (PCI) in MI, mortality of patients with MI in the Regional vascular center in 2019-2020. The T-criteria was used to assess the reliability. The material statistical processing was carried out in the Statistica 6.0 package calculating adequate statistical indicators and their reliability at p<=0.005. Ratio PCI/ECA in 2019, January-March 2020 counted up to 48.95%. In April-December 2020 it increased up to 71.6% (p<0.001). The frequency of performing Results PCI increased by 1.46 times (p<0.001). Hospital mortality from MI decreased during the following period 2019, April-December 2020 from 9.7% to 8.2% (p = 0.005). Remote clinical management based on telemedicine and mentoring process Conclusion technologies contributes to improving the quality of endovascular care in MI.Copyright © 2021 Angles. All rights reserved.

COVID-19-related ARDS Supported with Extracorporeal Membrane Oxygenation: Using Machine Learning Models to Improve Care

Background: Revised guidelines clarify indications for extracorporeal membrane oxygenation (ECMO) support in patients with COVID-19-related acute respiratory distress syndrome (ARDS). Commercially available ECMO analytics software records granular perfusion data continuously throughout the run. To date, electronic-medical record (EMR) clinical data has not been integrated with ECMO perfusion data and analyzed with machine learning-based algorithms to improve patient care. Methods: Retrospective chart review was performed on all SARS-CoV2 positive patients cannulated to veno-venous ECMO at an urban highvolume regional referral center from March 1st, 2020, through December 31st, 2021. Categorical data including patient demographics, clinical outcomes, and laboratory data (complete blood count, basic metabolic panel, arterial blood gas, lactate, anticoagulation assays) and vital signs (pulse, arterial line blood pressure, oxygen saturation) were collected for the entirety of the ECMO run. Time-series perfusion data (arterial flow normalized to body surface area (BSA), sweep gas, delta pressures normalized to arterial flow) were captured every 60-120 seconds. We constructed a predictive long-short term memory (LSTM) predictive model that integrated clinical and time-series data using an extended machine learning (ML) framework with neural network. Primary outcome was successful ECMO decannulation. Data were truncated to discrete and relative timepoints (7, 14, 21 days, or percent of the run). Receiver operating characteristic (ROC) curves show the model's diagnostic accuracy. Results: 42 patients were included in the analysis (30 male, 12 female). Mean age was 43.9 (SD=11.5) years old, and mean duration of ECMO run was 36.2 (SD=30.1) days. 24 patients were successfully decannulated and 4 are currently supported on ECMO. When provided the complete data, the LSTM model showed an area under the ROC curve >0.95, demonstrating strong diagnostic accuracy in predicting successful ECMO decannulation (Figure 1A). When data were truncated to only the first two weeks of the ECMO run, the area under the ROC curve was 0.93 (Fig. 1B). Patterns of arterial flow normalized to BSA and sweep gas normalized to flow also appear different in patients with divergent clinical outcomes (Fig 2). Conclusion: Characterizing key determinants of ECMO support may offer intensive care unit healthcare teams potentially lifesaving information in real-time. Our machine-learning model successfully integrates clinical and perfusion data from the mind's eye of a clinician managing the care of a patient supported with ECMO. We have identified critical variables with the most meaningful impact on the mechanics of ECMO support. Our model may also help predict patient outcomes into and offer clinicians opportunities for interventions to improve care. (Figure Presented).

Artificial Intelligence Technology as an Economic Accelerator of Business Process

The article covers a range of problems related to the need to use artificial intelligence (AI) technology, on the one hand, as an economic accelerator of business processes in conditions of high competition in the global market, and as conditions for the growth of the economy as a whole. The main purpose of the article is to prove the feasibility of developing a conceptual framework that reveals the value of AI technology in areas such as probabilistic thinking, machine learning and computer vision to help managers better understand how promising achievements can be achieved that can eliminate some limitations in business growth and create a new wave of opportunities in the socio-economic development of the country. The results of the study confirmed that AI technology: firstly, helps to create an innovative product, better serve customers, allocate employees to solve more creative tasks, reduce costs and get high results;secondly, it gives a clear incentive to its developers, companies, politicians and users to solve the socio-economic problems facing them;thirdly, removes restrictions in obtaining massive datasets based on global cooperation in the field of digital transformation. An assessment of the possibility of using the experience of AI technology development in advanced countries has been proposed and a number of measures have been developed to adapt the development of foreign AI technology to meet the requirements of the national program of the digital economy of Russia in response to the COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

REMOTE CLINICAL QUALITY MANAGEMENT OF ENDOVASCULAR CARE

To assess the effectiveness of remote clinical quality management of endovascular Aim care. The system of clinical quality management of medical care in myocardial infarction (MI) including the quality of remote control of endovascular care was developed and introduced into the health care system of the Moscow Region as a part of the comprehensive study in 2008–2020. The number of people under the study was 8375. The ground for assessing the effectiveness of remote clinical management in 2019–2020 was the health care system of megapolis. Based on the analysis of 2966 endovascular procedures protocols, the treatment tactics effectiveness of intraoperative decisions was studied after an emergency coronary angiography (ECA) had been performed by interventional cardiologists. The Methods system of remote clinical quality management of endovascular care included a complex of audiovisual communications, computer system processes, mentoring and the algorithm for making an intraoperative decision. The effectiveness of remote clinical quality management of endovascular care was investigated on the number of percutaneous coronary interventions (PCI) in MI, mortality of patients with MI in the Regional vascular center in 2019–2020. The T-criteria was used to assess the reliability. The material statistical processing was carried out in the Statistica 6.0 package calculating adequate statistical indicators and their reliability at p≤0.005. Ratio PCI/ECA in 2019, January-March 2020 counted up to 48.95%. In April-December 2020 it increased up to 71.6% (p<0.001). The frequency of performing Results PCI increased by 1.46 times (p<0.001). Hospital mortality from MI decreased during the following period 2019, April-December 2020 from 9.7% to 8.2% (p = 0.005). Remote clinical management based on telemedicine and mentoring process Conclusion technologies contributes to improving the quality of endovascular care in MI. © 2021 Angles. All rights reserved.

[The double diseases burdens trends in Ethiopia: COVID-19 aggravate the burdens of diseases and health system].

The relevance of the study included that scarcity of evidence about the burdens of diseases in Ethiopia, in particular to non-communicable diseases along with the impact of COVID-19. MATERIALS AND METHODS: The systematic analysis and content analysis using GBD 2019 database and health metrics datasets of 2020 was used to analyze diseases burdens trends of Ethiopia. The descriptive retrospective analysis has been performed, and reported in text and charts method. RESULTS: The result of the study showed increment of crude death and premature death due to both communicable and non-communicable diseases such as enteric infections, cardiovascular, neoplasm, others. CONCLUSION: COVID-19 would be the headache of Ethiopia health systems due to double burdens of diseases have steadily been shooting at ground. Hence, the nation should incite local based innovation work proactively and integrate all health services to fight with COVID-19 based on the experience of Russia.

[ORGANIZATIONAL AND METHODICAL PROBLEMS OF DISTANCE LEARNING OF MEDICAL STUDENTS IN THE COVID-19 PANDEMIC].

In connection with the COVID-19 pandemic, since March 2020, medical universities in different countries have been forced to switch to distance or blended learning formats. The article discusses the limitations and advantages of distance learning through a meta-analysis of the research results and the authors' own experience. The article describes different options for conducting current classes, intermediate and final online certification, the peculiarities of establishing new methods of communication and changes in student-teacher communication. Practical recommendations are offered for solving organizational, methodical, educational, technical and informational problems of RUDN University and A. I. Yevdokimov MSMSU, associated with distance learning of future doctors.

Gaseous Nitric Oxide and Dinitrosyl Iron Complexes with Thiol-Containing Ligands as Potential Medicines that Can Relieve COVID-19.

It is shown that the inhalation of gaseous nitric oxide (gNO) or sprayed aqueous solutions of binuclear dinitrosyl iron complexes with glutathione or N-acetyl-L-cysteine by animals or humans provokes no perceptible hypotensive effects. Potentially, these procedures may be useful in COVID-19 treatment. The NO level in complexes with hemoglobin in blood decreases as the gNO concentration in the gas flow produced by the Plazon system increases from 100 to 2100 ppm, so that at 2000 ppm more than one-half of the gas can be incorporated into dinitrosyl complexes formed in tissues of the lungs and respiratory tract. Thus, the effect of gNO inhalation may be similar to that observed after administration of solutions of dinitrosyl iron complexes, namely, to the presence of dinitrosyl iron complexes with thiol-containing ligands in lung and airway tissues. With regard to the hypothesis posited earlier that these complexes can suppress coronavirus replication as donors of nitrosonium cations (Biophysics 65, 818, 2020), it is not inconceivable that administration of gNO or chemically synthesized dinitrosyl iron complexes with thiol-containing ligands may help treat COVID-19. In tests on the authors of this paper as volunteers, the tolerance concentration of gNO inhaled within 15 min was approximately 2000 ppm. In tests on rats that inhaled sprayed aqueous solutions of dinitrosyl iron complexes, their tolerance dose was approximately 0.4 mmol/kg body weight.