Artificial intelligence for heart rate variability analyzing with arrhythmias

Introduction. Existing standards of Heart Rate Variability (HRV) technology limit its use to sinus rhythm. A small number of extrasystoles is allowed, if the device used has special procedures for the detection and replacement of ectopic complexes. However, it is important to expand the indicated limits of the applicability of the HRV technology. This specially regards the cases when the HRV technology looks promising in the diagnostics, as, for example, in atrial fibrillation and atrial flutter. Materials and Methods. All ECG measurements were performed on XAI-MEDICA® equipment and software. Processing of the obtained RR Series was carried out using the software Kubios® HRV Standard. All recommended HRV characteristics for Time-Domain, Frequency-Domain and Nonlinear were calculated. The purpose of the work. The article presents an artificial intelligence (AI) procedure for detecting episodes of arrhythmias and reconstruction of core patient’s rhythm, and demonstrates the efficacy of its use for the HRV analysis in patients with varying degrees of arrhythmias. The results of the study. It was shown efficiency of developed artificial intelligence procedure for HRV analyzing of patients with different level of arrhythmias. These were demonstrated for Time-Domain, Frequency-Domain and Nonlinear methods. The direct inclusion into review of Arrhythmia Episodes and the use of the initial RR Series leads to a significant distortion of the results of the HRV analysis for the whole set of methods and for all considered options for arrhythmia. Conclusion. High efficacy of operation of the procedure AI core rhythm extraction from initial RR Series for patients with arrhythmia was reported in all cases

Гендерне питання в охороні здоров’я (Gender question in health care)

У статті розглянуто гендерне питання і його особливості у сфері охорони здоров’я України. Проаналізовані проблеми диспропорції в якості життя, здоров’я і надання медичної допомоги, причини їх виникнення. На основі проведеного аналізу запропоновані методи їх вирішення. (We have investigated gender question in the health-care sector of Ukraine. The imbalance in the quality of life, health and medical care has been analyzed. Based on the analysis of these problems ways of solution were offered

Phenotype Algorithms for the Identification and Characterization of Vaccine-Induced Thrombotic Thrombocytopenia in Real World Data: A Multinational Network Cohort Study

INTRODUCTION: Vaccine-induced thrombotic thrombocytopenia (VITT) has been identified as a rare but serious adverse event associated with coronavirus disease 2019 (COVID-19) vaccines. OBJECTIVES: In this study, we explored the pre-pandemic co-occurrence of thrombosis with thrombocytopenia (TWT) using 17 observational health data sources across the world. We applied multiple TWT definitions, estimated the background rate of TWT, characterized TWT patients, and explored the makeup of thrombosis types among TWT patients. METHODS: We conducted an international network retrospective cohort study using electronic health records and insurance claims data, estimating background rates of TWT amongst persons observed from 2017 to 2019. Following the principles of existing VITT clinical definitions, TWT was defined as patients with a diagnosis of embolic or thrombotic arterial or venous events and a diagnosis or measurement of thrombocytopenia within 7 days. Six TWT phenotypes were considered, which varied in the approach taken in defining thrombosis and thrombocytopenia in real world data. RESULTS: Overall TWT incidence rates ranged from 1.62 to 150.65 per 100,000 person-years. Substantial heterogeneity exists across data sources and by age, sex, and alternative TWT phenotypes. TWT patients were likely to be men of older age with various comorbidities. Among the thrombosis types, arterial thrombotic events were the most common. CONCLUSION: Our findings suggest that identifying VITT in observational data presents a substantial challenge, as implementing VITT case definitions based on the co-occurrence of TWT results in large and heterogeneous incidence rate and in a cohort of patints with baseline characteristics that are inconsistent with the VITT cases reported to date

Phenotype Algorithms for the Identification and Characterization of Vaccine-Induced Thrombotic Thrombocytopenia in Real World Data:A Multinational Network Cohort Study

INTRODUCTION: Vaccine-induced thrombotic thrombocytopenia (VITT) has been identified as a rare but serious adverse event associated with coronavirus disease 2019 (COVID-19) vaccines. OBJECTIVES: In this study, we explored the pre-pandemic co-occurrence of thrombosis with thrombocytopenia (TWT) using 17 observational health data sources across the world. We applied multiple TWT definitions, estimated the background rate of TWT, characterized TWT patients, and explored the makeup of thrombosis types among TWT patients. METHODS: We conducted an international network retrospective cohort study using electronic health records and insurance claims data, estimating background rates of TWT amongst persons observed from 2017 to 2019. Following the principles of existing VITT clinical definitions, TWT was defined as patients with a diagnosis of embolic or thrombotic arterial or venous events and a diagnosis or measurement of thrombocytopenia within 7 days. Six TWT phenotypes were considered, which varied in the approach taken in defining thrombosis and thrombocytopenia in real world data. RESULTS: Overall TWT incidence rates ranged from 1.62 to 150.65 per 100,000 person-years. Substantial heterogeneity exists across data sources and by age, sex, and alternative TWT phenotypes. TWT patients were likely to be men of older age with various comorbidities. Among the thrombosis types, arterial thrombotic events were the most common. CONCLUSION: Our findings suggest that identifying VITT in observational data presents a substantial challenge, as implementing VITT case definitions based on the co-occurrence of TWT results in large and heterogeneous incidence rate and in a cohort of patints with baseline characteristics that are inconsistent with the VITT cases reported to date. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40264-022-01187-y

Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study

BACKGROUND: Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of AESIs following SARS-CoV-2 infection in patients and compare these to historical rates in the general population. METHODS: A multi-national cohort study with data from primary care, electronic health records, and insurance claims mapped to a common data model. This study's evidence was collected between Jan 1, 2017 and the conclusion of each database (which ranged from Jul 2020 to May 2022). The 16 pre-specified prevalent AESIs were: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain- Barré syndrome, haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, transverse myelitis, and thrombosis with thrombocytopenia. Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases. FINDINGS: Substantial heterogeneity by age was seen for AESI rates, with some clearly increasing with age but others following the opposite trend. Similarly, differences were also observed across databases for same health outcome and age-sex strata. All studied AESIs appeared consistently more common in the post-COVID-19 compared to the historical cohorts, with related meta-analytic SIRs ranging from 1.32 (1.05 to 1.66) for narcolepsy to 11.70 (10.10 to 13.70) for pulmonary embolism. INTERPRETATION: Our findings suggest all AESIs are more common after COVID-19 than in the general population. Thromboembolic events were particularly common, and over 10-fold more so. More research is needed to contextualise post-COVID-19 complications in the longer term. FUNDING: None

ДИСТАНЦІЙНЕ НАВЧАННЯ ЯК ІНФОРМАЦІЙНО-КОМУНІКАТИВНА ТЕХНОЛОГІЯ ДОДИПЛОМНОЇ ПІДГОТОВКИ ЛІКАРІВ З ПЕДІАТРІЇ В УМОВАХ СУЧАСНИХ ВИКЛИКІВ

For the implementation and adequate virtual provision of distance education at O. Bohomolets National Medical University, the NMU Neuron distance educational platform is used, which in the current situation can become an effective component of the educational process, while having a number of advantages. It not only provides the student with the most convenient place and time, access to electronic knowledge bases, but also significantly enhances his/her active role in the self-study process, including during the quarantined process, and requires the teacher to improve the teaching in terms of content, and in its form. The main types of distance learning courses are traditional forms of educational training: lectures, practical classes, seminars, labs, consultations and other relevant legislation. In general, distance learning technologies as a complex of modern educational technologies in the higher medical education system should be constantly improved, in particular through the use of modern educational platforms, qualitative methodological filling of web resources, introduction of the system of quality control of distance learning in order to meet the challenges of time.Для реалізації та адекватного віртуального забезпечення дистанційної освіти у Національному медичному університеті імені О. О. Богомольця використовується  дистанційна освітня платформа NMU Neuron, що  в нинішній ситуації може стати дієвою складовою освітнього процесу, маючи при цьому низку переваг. Воно не лише забезпечує навчання студента у найбільш зручному місці та часі, доступ до електронних баз знань, але й суттєво підвищує його активну роль у самому процесі самостійного навчання, зокрема й під час вимушеного внаслідок карантину, а від викладача вимагає вдосконалення викладання як за змістом, так і за його формою. Основними видами занять за дистанційною формою навчання є традиційні форми освітньої підготовки: лекція, практичні заняття, семінар, лабораторні заняття, консультації та інші, що відповідають чинному законодавству. У цілому, технології дистанційного навчання як комплекс сучасних освітніх технологій у системі вищої медичної освіти має постійно вдосконалюватися, зокрема шляхом використання сучасних освітніх платформ, якісного методичного наповнення веб-ресурсів, запровадження системи контролю якості дистанційного навчання, аби відповідати викликам часу

Risk of depression, suicide and psychosis with hydroxychloroquine treatment for rheumatoid arthritis:a multinational network cohort study

Objectives: Concern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA.Methods: We performed a new-user cohort study using claims and electronic medical records from 10 sources and 3 countries (Germany, UK and USA). RA patients ≥18 years of age and initiating HCQ were compared with those initiating SSZ (active comparator) and followed up in the short (30 days) and long term (on treatment). Study outcomes included depression, suicide/suicidal ideation and hospitalization for psychosis. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate database-specific calibrated hazard ratios (HRs), with estimates pooled where I2 &lt;40%.Results: A total of 918 144 and 290 383 users of HCQ and SSZ, respectively, were included. No consistent risk of psychiatric events was observed with short-term HCQ (compared with SSZ) use, with meta-analytic HRs of 0.96 (95% CI 0.79, 1.16) for depression, 0.94 (95% CI 0.49, 1.77) for suicide/suicidal ideation and 1.03 (95% CI 0.66, 1.60) for psychosis. No consistent long-term risk was seen, with meta-analytic HRs of 0.94 (95% CI 0.71, 1.26) for depression, 0.77 (95% CI 0.56, 1.07) for suicide/suicidal ideation and 0.99 (95% CI 0.72, 1.35) for psychosis.Conclusion: HCQ as used to treat RA does not appear to increase the risk of depression, suicide/suicidal ideation or psychosis compared with SSZ. No effects were seen in the short or long term. Use at a higher dose or for different indications needs further investigation.Trial registration: Registered with EU PAS (reference no. EUPAS34497; http://www.encepp.eu/encepp/viewResource.htm? id=34498). The full study protocol and analysis source code can be found at https://github.com/ohdsi-studies/Covid19EstimationHydroxychloroquine2.</p

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups:federated pharmacoepidemiological evaluation in LEGEND-T2DM

Objective: To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin.Design: Federated pharmacoepidemiological evaluation in LEGEND-T2DM.Setting: 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021.Participants: 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments.Exposure: The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort.Main outcomes measures: The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated.Results: 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease.Conclusions: Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.</p

Implementation of the COVID-19 vulnerability index across an international network of health care data sets:Collaborative external validation study

Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated.Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases.Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia.Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68.Conclusions: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.</p

Characteristics and outcomes of over 300,000 patients with COVID-19 and history of cancer in the United States and Spain

Background: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza. Methods: We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes. Results: We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%–18% and 1%–14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin’s lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n ¼ 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events. Conclusions: Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent. Impact: This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.</p