COVID-19 Data Prediction and Visualization in Mainland China

Contemporarily, COVID-19 shows a sign of recurrence in Mainland China. To better understand the situation, this paper investigates the growth pattern of COVID-19 based on the research of past data through regression models. The proposed work collects the data on COVID-19 in Mainland China from January 21st, 2020, to April 30th, 2020, including confirmed, recovered, and death cases. Based on polynomial regression and support vector machine regressor, it predicts the further trend of COVID-19. The paper uses root mean squared error to evaluate the performance of both models and concludes that there is no best model due to the high frequency of daily changes. According to the analysis, support vector machine regressors fit the growth of COVID-19 confirmed case better than polynomial regression does. The best solution is to utilize different types of models to generate a range of prediction result. These results shed light on guiding further exploration of the growth of COVID-19. © 2023 SPIE.

DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.

Prescribing Practices at a Residential Treatment Center – from Paper to Plastic (Computers)

Medication prescription procedures were reviewed at psychiatric residential treatment centers (RTC), and a quality improvement project was conducted in order to implement integrated electronic medical records (EMR) and computerized provider order entry (CPOE). To assess the impact of preexisting prescribing practices on RTCs, surveys were sent anonymously to current and past providers of regional and local RTCs. The surveys assessed the impact of the prescribing protocol on workflow, the frequency of medication errors, the impact such errors had on patient care, the overall provider satisfaction, and the discrepancies between electronic and paper charts. Current providers were then re-surveyed after implementing the integrated CPOE system in May 2020, during the COVID-19 pandemic. The first round of provider survey results revealed that the majority of staff (n = 10, 70%) observed frequent medication errors and were largely dissatisfied with the current medication ordering and administration process (n = 10;70%). Incident Response Improvement System (IRIS) reports revealed the most common medication errors were omissions, incorrect dosing, and incorrect medications administered. After the initiation and implementation of the CPOE, staff satisfaction increased with the process (n = 14;50%) noting that medication errors were less frequent than before (n = 14;50%). [ FROM AUTHOR] Copyright of Residential Treatment for Children & Youth is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Lessons for the Age of Consequences: COVID-19 and the Macroeconomy

Comparative empirical evidence for 22 OECD countries shows that country differences in cumulative mortality impacts of SARS-CoV-2 are caused by weaknesses in public health competences, pre-existing variances in structural socio-economic and public health vulnerabilities, and the presence of fiscal constraints. Remarkably, the (fiscally non-constrained) U.S. and the U.K. stand out, as they experience mortality outcomes similar to those of fiscally-constrained countries. High COVID19 mortality in the U.S. and the U.K. is due to pre-existing socio-economic and public health vulnerabilities, created by the following macroeconomic policy errors: (a) a deadly emphasis on fiscal austerity (which diminished public health capacities, damaged public health and deepened inequalities);(b) an obsessive belief in a trade-off between ‘efficiency' and ‘equity', which is mostly used to justify extreme inequality;(c) a complicit endorsement by mainstream macro of the unchecked power over monetary and fiscal policy-making of global finance and the rentier class;and (d) an unhealthy aversion to raising taxes, which deceives the public about the necessity to raise taxes to counter the excessive liquidity preference of the rentiers and to realign the interests of finance and of the real economy. The paper concludes by outlining a few lessons for a saner macroeconomics.

A New Registry for Genetic Diagnosis of Inborn Errors of Immunity within the Military Health System

Genotypic definition of monogenic inborn errors of immunity (IEIs) continues to accelerate with broader access to next generation sequencing, underscoring this aggregated group of disorders as a major health burden impacting both civilian and military populations. At an estimated prevalence of 1 in 1200 individuals, IEIs affect ~8,000 patients within the Military Health System (MHS). Despite access to targeted gene/exome panels at military treatment facilities, most affected patients never receive a definitive genetic diagnosis that would significantly improve clinical care. To address this gap, we established the first registry of IEI patients within the MHS with the goal of identifying known and novel pathogenic genetic defects to increase diagnosis rates and enhance clinical care. Using the registry, a research protocol was opened in July 2022. Since July we have enrolled 75 IEI patients encompassing a breadth of phenotypes including severe and recurrent infections, bone marrow failure, autoimmunity/autoinflammation, atopic disease, and malignancy. Enrolled patients provide blood and bone marrow samples for whole genome, ultra-deep targeted panel and comprehensive transcriptome sequencing, plus cryopreservation of peripheral blood mononuclear cells for future functional studies. We are also implementing and developing analytical methods for identifying and interrogating non-coding and structural variants. Suspected pathogenic variants are adjudicated by a clinical molecular geneticist using state-of-the-art analysis pipelines. These analyses subsequently inform in vitro experiments to validate causative mutations using cell reporter systems and primary patient cells. Clinical variant validation and return of genetic results are planned with genetic counseling provided. As a proof of principle, this integrated genetic evaluation pipeline revealed a novel, candidate TLR7 nonsense variant in two adolescent brothers who both endured critical COVID-19 pneumonia, requiring mechanical ventilation and extracorporeal membrane oxygenation. Our protocol is therefore poised to greatly enrich clinical genetics resources available in the MHS for IEI patients, contributing to better diagnosis rates, informed family counseling, and targeted treatments that collectively improve the health and readiness of the military community. Moreover, our efforts should yield new mechanistic insights on immune pathogenesis for a broad variety of known and novel IEIs.Copyright © 2023 Elsevier Inc.

Impact of a Mobile Meditation Application Among Hospital-Based Acute Care Nurses

Compassion fatigue has also resulted in increased absenteeism, errors, and other disruptive behaviors and can have negative effects on patient care. Examples may include medication errors and failure to rescue patients with unrecognized declining health status. [...]in a separate study of college students who were randomly assigned to use one of three apps, for ten minutes per day for ten days, Headspace users had positive outcomes. Methods The Professional Quality of Life 5 (ProQOL5) and Mindfulness Attentive Awareness Scale (MAAS) surveys were used to collect pre-and post-intervention data through Survey Monkey online.

COVID-19-Related Encephalitis in a Child with PIK3CD Defect

Introduction: COVID-19 related encephalitis has been reported in pediatric patients;however, there are no reports in patients with inborn errors of immunity (IEI). Activated PI3K Delta Syndrome (APDS) is a disease of immune dysregulation with immunodeficiency, autoimmunity, and abnormal lymphoproliferation resulting from autosomal dominant gain-offunction variants in PIK3CD or PIK3R1 genes. We investigate a family with APDS, one mother and three children, one of whom developed COVID-19 related encephalitis. Method(s): Patients were consented to an IRB-approved protocol at our institution. Medical records and detailed immunophenotyping were reviewed. Family members were sequenced for IEI with a targeted gene panel. Result(s): The index case is a 10-year-old female with a known pathogenic variant in PIK3CD (c.3061 G > A, p.Glu1021Lys), who contracted SARS-COV-2 despite one COVID-19 vaccination in the series. Her disease course included COVID-related encephalitis with cerebellitis and compression of the pons, resulting in lasting truncal ataxia and cerebellar mutism. At that time, the patient was not on immunoglobulin replacement therapy (IgRT), but was receiving Sirolimus. Besides the index case, 3 family members (2 brothers, 1 mother) also share the same PIK3CD variant with variable clinical and immunological phenotypes. All children exhibited high transitional B-cells, consistent with developmental block to follicular B cell stage. Increased non-class switched IgM+ memory B cells and skewing towards CD21lo B cell subset, which is considered autoreactive-like, was observed in all patients. Of note, the patient had low plasmablasts, but normal immunoglobulins. Of her family members, only one was receiving both sirolimus and IgRT. Conclusion(s): We describe a rare case of COVID-19-related encephalitis in a patient with inborn error of immunity while not on IgRT. This may indicate infection susceptibility because of a lack of sufficient immunity to SARS-CoV-2, unlike the rest of her family with the same PIK3CD variant.Copyright © 2023 Elsevier Inc.

Comparative Analysis of Different Systematic Odometry Error Correction Methods on a Differential Drive Indoor Service Robot

The COVID-19 pandemic has led to a surge of interest in research work involving the development of robotic systems that reduce human-to-human interaction, as such a technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. An indoor service robot is built and equipped with wheel odometry and a 2D LiDAR. However, the presence of the systematic odometry errors is evident during field testing. Hence, the possibility of minimizing systematic odometry errors is inspected using various methods of calculation, namely: UMBmark, Lee's and Jung's. The methods all use the Bidirectional Square Path test, performed together with ROS. It is found that Jung's method is the most appropriate method showing a 20.4% improvement compared to the uncalibrated dead reckoning accuracy. Moreover, it is found that the presence of slippage, a nonsystematic error, greatly affects the return position errors of the robot. Consequently, it is recommended to improve the design of the wheelbase to minimize the effects of nonsystematic errors. © 2022 IEEE.

Misleading Beyond Visual Tricks: How People Actually Lie with Charts

Data visualizations can empower an audience to make informed decisions. At the same time, deceptive representations of data can lead to inaccurate interpretations while still providing an illusion of data-driven insights. Existing research on misleading visualizations primarily focuses on examples of charts and techniques previously reported to be deceptive. These approaches do not necessarily describe how charts mislead the general population in practice. We instead present an analysis of data visualizations found in a real-world discourse of a significant global event - Twitter posts with visualizations related to the COVID-19 pandemic. Our work shows that, contrary to conventional wisdom, violations of visualization design guidelines are not the dominant way people mislead with charts. Specifically, they do not disproportionately lead to reasoning errors in posters' arguments. Through a series of examples, we present common reasoning errors and discuss how even faithfully plotted data visualizations can be used to support misinformation. © 2023 Owner/Author.

Comparison Analysis of Prediction the Number of Covid-19 Cases Using Support Vector Regression and Long Short-Time Memory

Covid-19 emerged as a pandemic outbreak that spread almost worldwide at the end of December 2019. While this research was carried out, the Covid-19 pandemic was still ongoing. Many countries have made various attempts to overcome Covid-19. In Indonesia, the government and stakeholders, including researchers, have made many activities to reduce the number of positive patients. One of many activities that the government made is the vaccination program. The vaccination program is believed to be the most effective in reducing the number of positive cases of Covid-19. But nobody knows when the Covid-19 pandemic will end. Stakeholder has to know how the trend of Covid-19 cases in Indonesia to make a better decision for facing Covid-19 cases. This study aims to predict the number of positive Covid-19 cases in Indonesia by conducting a comparative analysis performance of Support Vector Regression (SVR) method and Long Short-Term Memory (LSTM) method in machine learning to the prediction of the number of Covid-19 cases. This study was conducted using the dataset Covid-19 in Indonesia from Control Team from 13 January 2021 until 08 November 2021 and with 300 records. The evaluation has been conducted to know the performance of the model prediction number of Covid-19 with Support Vector Regression method and Long Short-Term Memory method based on values of R-Square (R2), the value of Mean Absolute Error (MAE) and Mean Square Error (MSE). The research found that the method Support Vector Regression has better performance than Long Short-Term Memory method for making a prediction of the number Covid-19 using Machine Learning model based on the value of accuracy and error rate based with the value of R-Squared, MAE, and MSE are consecutively 0.902, 0.163, and 0.072. © 2022 IEEE.

An Interpretable Fake News Detection Method Based on Commonsense Knowledge Graph

Existing deep learning-based methods for detecting fake news are uninterpretable, and they do not use external knowledge related to the news. As a result, the authors of the paper propose a graph matching-based approach combined with external knowledge to detect fake news. The approach focuses on extracting commonsense knowledge from news texts through knowledge extraction, extracting background knowledge related to news content from a commonsense knowledge graph through entity extraction and entity disambiguation, using external knowledge as evidence for news identification, and interpreting the final identification results through such evidence. To achieve the identification of fake news containing commonsense errors, the algorithm uses random walks graph matching and compares the commonsense knowledge embedded in the news content with the relevant external knowledge in the commonsense knowledge graph. The news is then discriminated as true or false based on the results of the comparative analysis. From the experimental results, the method can achieve 91.07%, 85.00%, and 89.47% accuracy, precision, and recall rates, respectively, in the task of identifying fake news containing commonsense errors.

Mild COVID-19 outcomes in adults with inborn errors of immunity: a single-center experience

Currently, 640 million cases of coronavirus disease 2019 (COVID-19) and 6.6 million deaths have been reported world-wide. Risk factors for severe COVID-19 are known, including those with compromised immunity. Among patients with inborn errors of immunity (IEI), early reports of severe outcomes lead to strict masking and social distancing measures. While this resulted in relatively low infection rates among those with IEI, real-world data describing the clinical course of COVID-19 in this patient population have remained limited. We performed a retrospective study of adult IEI patients followed by our center in which a positive test (rapid antigen or PCR) for COVID-19 was determined between November 2021-November 2022. Medical charts were reviewed, and patient interviews conducted. All patients provided informed consent. Twenty-nine patients were enrolled (22 females, 7 males), aged between 18-69 years (median: 20-29 years). The cohort included those with antibody deficiencies (41.37%), combined immunodeficiencies (34.48%;HIES, CARD11, STAT1-GOF), immune dysregulation disorders (20.69%;LRBA deficiency, AIRE deficiency) and phagocyte defect (3.45%;CGD). The duration of symptoms ranged between 3 days-4 weeks (median: < 1 week). Upper respiratory symptoms (including sore throat, congestion) were reported in 97% while fever was present in 41% of patients. Prior to infection, 14 (48%) patients had underlying asthma or bronchiectasis - 2 subsequently experienced shortness of breath and were treated with inhalers or Sotrovimab, respectively. No treatment was required in 65.5% of cases. The remaining received Paxlovid (10.3%), Sotrovimab (13.79%), or antibiotics (10.3%). Of the 2 patients with STAT1-GOF, one tested positive during a repeat episode of febrile neutropenia which required hospitalization. No other patients were hospitalized or needed ICU admission. No deaths were recorded. In light of these favourable outcomes, patients with IEI can gradually and safely return to normal activities.Copyright © 2023 Elsevier Inc.

Characterization and Optimization of Trajectory Errors on Remote-Controlled Differential Drive Robot through Speed Calibration

An increase in interest in research projects which involves the design of robotic systems that minimizes interactions between humans has been caused by the COVID-19 outbreak, as such technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. The utilization of remote-controlled robots in many different fields, especially in the medical field is becoming more and more necessary. However, mobile robots are susceptible to both systematic and nonsystematic errors that cause deviations in its trajectory. In view thereof, the researchers explored the possibility of minimizing the trajectory errors through speed calibration. The differential drive robot was navigated to finish a five-meter linear path of forward and backward motion. The test was conducted with a default linear speed of 0.5 m/s in which a high trajectory error was observed. Upon changing the speed of the robot, the same trajectory test was conducted at four additional different speeds, namely;0.25 m/s, 0.35 m/s, 0.65m/s and 0.75 m/s. With the gathered data, the researchers conducted a linear least-squares regression model using MATLAB wherein there is only one predictor variable (speed of the robot) and one response variable (deviation). Based on the results, the researchers concluded that the speed of 0.35 m/s is the optimal speed in which the trajectory error of the robot is minimal. The researchers recommend improving the design of the caster wheels to minimize the effects of nonsystematic errors. © 2022 IEEE.

Anti-interferon-alpha autoantibodies in patients with inborn errors of Immunity and rheumatic diseases before and during the COVID-19 pandemic

Introduction: Type 1 interferon (IFN) autoantibodies, such as anti-IFNalpha, have pathogenic significance in life-threatening COVID-19 pneumonia. Ten to twenty percent of severe COVID cases are associated with type I IFN autoantibodies. These autoantibodies likely pre-exist while others arise de novo relative to SARS-CoV-2 infection. It is unclear to what extent type I anti-IFN autoantibodies are induced by SARS-CoV-2 infection and contribute to COVID-19 severity. We investigated these phenomena in those with inborn errors of immunity (IEI) and rheumatic disease (RHE). Aim(s): We aim to compare the prevalence and neutralization ability of anti-IFNalpha autoantibodies in IEI and RHE patients using archived blood samples before and after the COVID-19 pandemic began. Method(s): We determined the presence of autoantibodies against IFNalpha in plasma samples by enzyme linked immunosorbent assay in 453 patients with IEI or RHE who were testing either before or after the COVID-19 pandemic began in March 2020. Using flow cytometry, we determined the function of IFNalpha autoantibodies in plasma to block CD4T cell activation by inhibiting STAT-1 phosphorylation. Result(s): We found that 25 patients with IEI or RHE were positive for anti-IFNalpha autoantibodies. 10 out of 229 patient samples collected before the pandemic (4.2%) tested positive whereas 15 out of 224 patient samples collected after the pandemic began (7.0%) were positive. Seven of the 25 patients (28%) who tested positive had neutralizing antibodies in plasma, which prevented STAT-1 phosphorylation in CD4T cells;all of these patients had partial recombination activating gene deficiency (pRD) except for one patient with autoimmunity, leukemia and selective IgA deficiency. One pRD patient had anti-IFNalpha autoantibodies with neutralization capacity before the pandemic, which persisted after hematopoietic stem cell transplantation (HSCT) with full immune reconstitution. The patient was immunized for SARS-CoV-2 before and after HSCT and acquired COVID-19 infection a year after HSCT. The patient was symptomatic but never hospitalized and fully recovered despite having anti-IFNalpha autoantibodies. Conclusion(s): Anti-IFNalpha autoantibody levels were comparable before and after the start of the COVID-19 pandemic in IEI and RHE patients but only 28% of cases were neutralizing. The clinical implications of these autoantibodies are yet to be determined.Copyright © 2023 Elsevier Inc.

Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study

This study aims to provide insights into predicting future cases of COVID-19 infection and rates of virus transmission in the UK by critically analyzing and visualizing historical COVID-19 data, so that healthcare providers can prepare ahead of time. In order to achieve this goal, the study invested in the existing studies and selected ARIMA and Fb-Prophet time series models as the methods to predict confirmed and death cases in the following year. In a comparison of both models using values of their evaluation metrics, root-mean-square error, mean absolute error and mean absolute percentage error show that ARIMA performs better than Fb-Prophet. The study also discusses the reasons for the dramatic spike in mortality and the large drop in deaths shown in the results, contributing to the literature on health analytics and COVID-19 by validating the results of related studies. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Obtaining medication histories via telepharmacy: an observational study.

BACKGROUND: Medication reconciliation is an effective strategy to reduce medication errors upon hospital admission. The process involves obtaining a best possible medication history (BPMH), which can be both time-consuming and resource-intensive. During the COVID-19 pandemic, telepharmacy was used to reduce the risk of viral transmission. Telepharmacy is the remote provision of pharmacy-led clinical services, such as obtaining BPMHs, using telecommunications. However, the accuracy of telephone-obtained BPMHs has not yet been evaluated. Therefore, the primary aim of this study was to evaluate the proportion of patients who have an accurate BPMH from the telephone-obtained BPMH compared to an in-person obtained BPMH. METHODS: This prospective, observational study took place in a large tertiary hospital. Recruited patients or carers had their BPMH obtained by a pharmacist over the telephone. The same patients or carers then had their BPMH conducted in-person to identify any deviations between the telephone-obtained and in-person obtained BPMH. All telephone-obtained BPMHs were timed with a stopwatch. Any deviations were categorised according to their potential consequence. An accurate BPMH was defined as having no deviations. Descriptive statistics were used to report all quantitative variables. A multivariable logistic regression was conducted to identify risk factors for patients and medications for having medication deviations. RESULTS: In total, 116 patients were recruited to receive both a telephone-obtained and in-person obtained BPMH. Of these, 91 patients (78%) had an accurate BPMH with no deviations. Of the 1104 medications documented across all the BPMHs, 1064 (96%) had no deviation. Of the 40 (4%) medication deviations, 38 were deemed low-risk (3%) and 2 high-risk (1%). A patient was more likely to have a deviation if they are taking more medications (aOR: 1.11; 95% CI: 1.01-1.22; p < 0.05). A medication was more likely to have a deviation if it was regular non-prescription medication (aOR: 4.82; 95% CI: 2.14-10.82; p < 0.001) or 'when required' non-prescription medication (aOR: 3.12; 95% CI: 1.20-8.11; p = 0.02) or a topical medication (aOR: 12.53; 95% CI: 4.34-42.17; p < 0.001). CONCLUSIONS: Telepharmacy represents a reliable and time-efficient alternative to in-person BPMHs.

Importance of Balanced Attention Toward Coronavirus Disease 2019 and Neglected Tropical Diseases.

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has been spreading since 2019, causing a worldwide pandemic. Amid the COVID-19 pandemic, tuberculosis, AIDS, and malaria have adversely affected the quality of life of patients and killed millions of people. In addition, COVID-19 continues to impede the delivery of health services, including those for the control of neglected tropical diseases (NTDs). Furthermore, NTDs have been reported as possible co-pathogens among patients infected with COVID-19. However, studies regarding parasitic co-infection among these patients have been limited. This review aimed to explore and describe the cases and reports of parasitic infections in the backdrop of COVID-19 to provide comprehensive knowledge regarding this aspect. We reviewed seven cases of patients who had parasitic co-infection and tested positive for COVID-19, and summarized the literature on the importance of controlling parasitic diseases. In addition, we identified recommendations for the control of parasitic diseases under possible difficulties, such as declining funding for parasitic diseases in 2020. This review highlights the growing burden of NTDs under COVID-19 that may be caused by the deficiency of healthcare infrastructure and human resources as the main reasons. Clinicians should remain vigilant for possible co-infections with parasites in COVID-19 patients, while policymakers are urged to reinforce a balanced and long-term health strategy that addresses both NTDs and COVID-19.

Severity Outcomes among Adult Patients with Primary Immunodeficiency and COVID-19 Seen in Emergency Departments, United States, April 2020-August 2021.

Primary immunodeficiencies (PIs) are a group of diseases that increase susceptibility to infectious diseases. Few studies have examined the relationship between PI and COVID-19 outcomes. In this study, we used Premier Healthcare Database, which contains information on inpatient discharges, to analyze COVID-19 outcomes among 853 adult PI and 1,197,430 non-PI patients who visited the emergency department. Hospitalization, intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), and death had higher odds in PI patients than in non-PI patients (hospitalization aOR: 2.36, 95% CI: 1.87-2.98; ICU admission aOR: 1.53, 95% CI: 1.19-1.96; IMV aOR: 1.41, 95% CI: 1.15-1.72; death aOR: 1.37, 95% CI: 1.08-1.74), and PI patients spent on average 1.91 more days in the hospital than non-PI patients when adjusted for age, sex, race/ethnicity, and chronic conditions associated with severe COVID-19. Of the largest four PI groups, selective deficiency of the immunoglobulin G subclass had the highest hospitalization frequency (75.2%). This large study of United States PI patients provides real-world evidence that PI is a risk factor for adverse COVID-19 outcomes.

Evaluation of medical decision errors during the transition period to telemedicine

The context of the Coronavirus pandemic has fundamentally changed the way we approach medical services. Beyond setting up new technological possibilities, it has propelled telemedicine to become a reality, bringing undeniable practical benefits. The questions that arise are both justified and worrying: can digitalization replace a direct interpersonal relationship that involves a physical examination, while preserving the quality of the medical act and the degree of patient satisfaction? Isn't there a risk that the digitization of the medical record will cancel out the deep human character of classical medicine that has evolved since the time of Hippocrates? Should the implementation of telemedicine be "the state-of-art" of modern medicine, in accordance with the co-evolution of digital technology? It is hard to believe that once used in this period, telemedicine will be abandoned. However, telemedicine must be analyzed not only in the short term but also in the long term, in order to be able to evaluate both its usefulness and possible deficiencies.

Immune Responses 6 Months After mRNA-1273 COVID-19 Vaccination and the Effect of a Third Vaccination in Patients with Inborn Errors of Immunity.

PURPOSE: Patients with inborn errors of immunity (IEI) are at increased risk of severe coronavirus disease-2019 (COVID-19). Effective long-term protection against COVID-19 is therefore of great importance in these patients, but little is known about the decay of the immune response after primary vaccination. We studied the immune responses 6 months after two mRNA-1273 COVID-19 vaccines in 473 IEI patients and subsequently the response to a third mRNA COVID-19 vaccine in 50 patients with common variable immunodeficiency (CVID). METHODS: In a prospective multicenter study, 473 IEI patients (including X-linked agammaglobulinemia (XLA) (N = 18), combined immunodeficiency (CID) (N = 22), CVID (N = 203), isolated or undefined antibody deficiencies (N = 204), and phagocyte defects (N = 16)), and 179 controls were included and followed up to 6 months after two doses of the mRNA-1273 COVID-19 vaccine. Additionally, samples were collected from 50 CVID patients who received a third vaccine 6 months after primary vaccination through the national vaccination program. SARS-CoV-2-specific IgG titers, neutralizing antibodies, and T cell responses were assessed. RESULTS: At 6 months after vaccination, the geometric mean antibody titers (GMT) declined in both IEI patients and healthy controls, when compared to GMT 28 days after vaccination. The trajectory of this decline did not differ between controls and most IEI cohorts; however, antibody titers in CID, CVID, and isolated antibody deficiency patients more often dropped to below the responder cut-off compared to controls. Specific T cell responses were still detectable in 77% of controls and 68% of IEI patients at 6 months post vaccination. A third mRNA vaccine resulted in an antibody response in only two out of 30 CVID patients that did not seroconvert after two mRNA vaccines. CONCLUSION: A similar decline in IgG titers and T cell responses was observed in patients with IEI when compared to healthy controls 6 months after mRNA-1273 COVID-19 vaccination. The limited beneficial benefit of a third mRNA COVID-19 vaccine in previous non-responder CVID patients implicates that other protective strategies are needed for these vulnerable patients.