Esofagitis Infecciosa En Inmunocompetentes: Serie De Casos En Un Hospital De Tercer Nivel

Introduccion: La prevalencia de esofagitis infecciosa en individuos inmunocompetentes es inusual. El principal agente etiologico es el VHS 1, afectando en la mayoria de los casos a menores de 40 anos con odinofagia intensa y disfagia como sintomas principales. Se desconoce si la infeccion por SARS-CoV-2 o la administracion de su vacuna son factores predisponentes. Objetivos: Determinar las principales caracteristicas clinicas, diagnosticas, endoscopicas y la respuesta al tratamiento de pacientes diagnosticados de esofagitis virica desde el inicio de la pandemia. Metodos: Se incluye una serie de casos de pacientes inmunocompetentes con esofagitis virica en el Complejo Asistencial Universitario de Leon desde marzo 2020 hasta enero 2022. Resultados: Un total de cinco pacientes fueron diagnosticados de esofagitis virica. Los datos personales, clinicos, diagnosticos y terapeuticos de todos los casos se recogen en la tabla. Conclusiones: La esofagitis virica en pacientes inmunocompetentes es infrecuente, lo que conlleva su infradiagnostico. Se deben sospechar ante un cuadro agudo de odinofagia intensa. En nuestra serie el diagnostico histologico fue compatible solo en el 40%. Al tratarse de una infeccion autolimitada en individuos inmunocompetentes, existen discrepancias entre administrar aciclovir o unicamente tratar los sintomas. La infeccion por SARS-CoV-2 o la administracion de sus vacunas podrian actuar como factores predisponentes, aunque se necesitan mas estudios para establecer dicha relacion. [Table presented]Copyright © 2023 Elsevier Espana, S.L.U. Todos los derechos reservados.

Impact of the COVID-19 pandemic on the training of Spanish neurology residents

Introduction: The COVID-19 pandemic has transformed medical practice and severely disrupted the training of medical residents worldwide. The Spanish Society of Neurology conducted a study to assess its impact on the training of neurology residents in Spain. Method(s): We performed a descriptive, cross-sectional study through a survey distributed by e-mail to all neurology residents belonging to the Society. The survey included questions on demographic variables, care activity, and personal and educational impact of the pandemic, as well as respondents' expectations for the future of their work in the post-pandemic era. Result(s): Of 422 surveys sent, we received a total of 152 responses (36%);79 respondents (52%) were women and 73 (48%) were men. By year of residency, 51 respondents (33.6%) were in the fourth year, 45 (29.6%) in the third year, 28 (18.4%) in the second year, and 28 (18.4%) in the first year. A total of 139 respondents (90.8%) reported changes in hospital activity, and 126 (82.8%) considered the situation to have had a negative impact on their training, with 99 (64.7%) having lost non-recoverable rotations. Sixty-six percent of respondents (n=101) expressed a desire to extend their residency period. Conclusion(s): The pandemic has had an extremely severe impact on all areas of the health system, with trainee physicians being one of the most affected groups. Among neurology residents, the crisis has caused significant shortcomings in their training and clinical activities, through the suspension of specific rotations. A high percentage of respondents wished to extend the residency period.Copyright © 2021 Sociedad Espanola de Neurologia

Lessons Learned in the Implementation of a Training Knowledge Management System for the SERVIR Network

The SERVIR program is a unique partnership between NASA, the U.S. Agency for International Development (USAID), focusing on building capacity to use Earth observations for addressing development challenges. In that context, between 2004 and 2020, the program delivered approximately 365 trainings to almost 10,000 professionals. More recently, between November 2020 and August 2021, the SERVIR network executed some 55 training events addressing SERVIR’s 4 priority thematic areas, and roughly a quarter of SERVIR trainings overall have focused on themes related to Ecological Forecasting. Due to the ongoing COVID-19 pandemic, almost four-fifths of recent training events have been virtual, with the remainder being in-person under limited circumstances. The large number of training events delivered represents both an opportunity and a challenge in terms of knowledge management. While the training materials developed can later be reused in other parts of the SERVIR network, prior to recently, the lack of a central repository for those materials has prevented wider dissemination and use. The recently developed Training Knowledge Management System (TKMS) is now becoming an integral part of the SERVIR Capacity Building Framework, supporting the exchange of resources and methods for conducting training activities across the network. This presentation focuses on the structure of this system, as well as on the anticipated benefits for the User Communities for Earth Observations of Terrestrial Systems.

Management and impact of atrial fibrillation in the COVID 19 era;Concomitancy of two procoagulant phenomena

Background: Atrial fibrillation (AF) is a widespread cause of prothrombotic state leading to long-term anticoagulant therapy. Literature describes coagulopathy as a key pathogenic mechanism of COVID-19 disease. Thus, antithrombotic therapy management is still a therapeutic challenge. During hospitalization, changing oral anticoagulant (OAC) therapies into subcutaneous heparin is common in daily clinical practice. Purpose: The primary endpoint of this study is to analyze the impact of AF in mortality within 30 day since admission of COVID-19 patients. The secondary endpoint is to analyze the impact of the anticoagulant therapy strategy (therapeutic dose of subcutaneous heparin vs. OAC) in 30-day mortality of hospitalized COVID-19 patients with AF. Methods: A total of 1001 consecutive patients hospitalized in our centre between 22nd August and 9th January 2021 with a confirmed microbiological diagnosis of COVID-19 by PCR were prospectively included. Of them, 134 had a previous diagnose of AF (13.5%). Cox regression analysis was performed to assess the impact of AF and the choice of anticoagulant therapy in 30-day mortality after adjusting for comorbidity (Charlson Comorbidity Index). Results: After adjusting for comorbidities, AF was not independently associated with a higher 30-day mortality in patients hospitalized due to COVID-19 infection (HR 1.04, CI 0.77-1.43, p=0.760). In the group of patients with AF, changing OAC to heparin therapy was not associated with an improved prognosis (HR 0.85, CI 95% 0.46-1.56, p=0.604). Conclusions: AF is not an independent prognostic factor in COVID-19 hospitalized patients. In hospitalized COVID-19 patients with AF, changing OAC to heparin therapy is not related to an improved prognosis.

Impact of previous cardiac conditions in prognosis and clinical management of patient with COVID-19 infection

Background: Recent studies suggest a higher mortality rate because of COVID-19 in patients with previous cardiac conditions compared to those without. Given the limited resources of intensive care units (ICU) during the pandemic outbreak, this fact has important implications. Purpose: The main purpose of this study was to compare the 30-day mortality of the COVID-19 infection in patients with and without previous cardiac conditions. The secondary end point was to assess the differences in clinical severity of the infection (as development of Acute Respiratory Distress Syndrome - ARDS) and ICU admission amongst these patients. Methods: A total of 1708 consecutive patients were prospectively included. The inclusion criteria were: a confirmed positive diagnosis of COVID-19 infection by PCR and being admitted to our centre between 18th and 23rd March 2020 and 22nd August and 9th January 2021. Patients were classified in two groups according to the presence of previous cardiac conditions (defined as previous history of myocardial infarction, heart failure and atrial fibrillation). Other comorbidities were extensively explored and Charlson Comorbidity Index was calculated. A propensity-score matching was performed and 145 patients with previous cardiac conditions were matched with 145 patients without. Results: The group of patients with a previous cardiac condition included 421 patients (24.6%). The crude analysis showed a higher 30-day mortality rate among patients with previous cardiac affections (35.6% vs. 14.6%, p<0.001). They were also less likely to be admitted to the ICU (9.8% vs. 6.2%, p=0.022) and had a higher prevalence ARDS (48.9% vs. 33.9%, p<0.001). In the matched cohort, there were no significant differences between both groups regarding mortality (24.8% in the group of patients with previous cardiac conditions vs. 31.0%, p=0.272) nor ARDS prevalence (50.3% vs. 53.1%, p=0.655). There was a trend toward patients with previous cardiac conditions to be less likely to be admitted to the ICU (4.8% vs. 9.7%, p=0.090). Conclusions: Patients with a personal history of previous cardiac conditions were less likely to be admitted to the ICU. However, our results show that when comparing cohorts with similar comorbidity burden, a previous cardiopathy per se does not significantly increase the risk of death in patients with a concomitant COVID infection.

Predictive modeling to create a proactive approach to patient blood management in the oncology population

Background: Patient safety concerns that arose during COVID-19, related to blood shortages at a large oncological transfusion center, foregrounded the need for predictive modeling tools to optimize blood product inventory control. A maximum surgical blood ordering schedule (MSBOS), is a tool used to assist clinicians in predicting intraoperative blood usage based on retrospective historical data within an institution. Although MSBOS proves to be valuable, it is rudimentary in nature. Not only is data collection cumbersome but the data generated may not reflect current surgical practices and inter-patient variability may skew procedural averages. Predictive blood modeling is contingent generation of a digital health dashboard (DHB). DHB are electronically embedded in the electronic health record (EHR) to collect perioperative data. Coupling the generated informatics (patient demographics, diagnosis, laboratory results, procedural type, medications/supplements, surgeon) with machine learning allows for creation of patient centered predictive blood modeling algorithms and better inventory control. Methods: To characterize blood use across various procedures at our institution, we engaged information technology specialists to create a Web Intelligence report by integrating data from both an EHR and a lab information system (LIS) into a single repository. Information obtained illuminated a master procedure list, blood product usage patterns, and characterized patient demographics during January-March, 2020. Data is continuously extracted to create a perpetually updated MSBOS while secondarily functioning to cultivate data for future predictive machine learning algorithms. Results: Data analysis demonstrated 5598 procedures were performed during the first quarter of 2020. Procedures not transfused with packed red blood cells (pRBCs) totaled to 4,156 and 1,442 had a greater than or equal to 10% probability of requiring pRBCs. Our current practices reflected our overall cross-match to transfusion ratio ( C:T) was 5.4 to 1. Concerted collaboration, resulting in preparation of pre-surgical blood product orders according laboratory generated MSBOS schedule could decrease the C:T to 1.7 to 1. Additionally, high intraprocedural pRBCs variability was identified in current procedural subtypes. Conclusions: Traditionally generated MSBOS are functionally limited and may not be reflective of current surgical practices. Additionally, inter-patient variability may distort some procedural type guidance. Creating an integrated data report, eliminates some of the inherent limitations of traditional MSBOS. Moving forward, the cultivated data if coupled with machine learning has the potential to create transferable proprietary algorithms that proactively predict individual patient transfusion needs.

Guillain barre syndrome and subsegmentary pulmonary embolism asociated to SARS-CoV-2 infection: Case report and review of current literature

Background and aims: There´s emerging evidence on the association of GBS with SARS-CoV-2 infection. Neurotropism by coronavirus has been documented as well as various neurological manifestations such as encephalitis, stroke, encephalopathy and peripheral nerve disease. Methods: A 67-year-old male, no comorbidities presents three weeks prior to admission with fever, cough, taste and smell disturbances, myalgias, asthenia, clumsy hand movements and progressive lower limbs weakness. 15 days prior to admission: difficulty swallowing, diaphoresis. At admission: breathing difficulty and palpitations. Glasgow 13 E4 V5 M4, bulbar compromise, bradylalia, diminished gag reflex, sternocleidomastoid and trapezius weakness, MRC scale upper and lower limbs: proximal 3/5 distal 2/5, generalized areflexia, distal diffuse hypoesthesia Results: Ferritin 519 LDH 236 D Dimer >10,000 Hgb 19 WBC 11590 L 12% N 80% P 241,000 CK 111 CK MB 17. Chest CT: COVID19 pneumonia, CO-RADS 3. Pulmonary angiography: Posterior right lower lobe segmental PE. Scores: PESI 108, Geneva 10. SARS-CoV-2 PCR negative, SARS-CoV-2 IgG/IgM: Positive. Lumbar puncture not performed due to PE. Met Asbury GBS criteria, HUGHES 4, mEGOS 8 at admission, EGRIS 4. Progression of ascending symmetrical bilateral flaccidity with respiratory failure requiring mechanical ventilation for 10 days, tracheostomy and gastrostomy were performed. Discharged at day 60 with muscle strength recovery, upper limbs 4/5 and lower limbs 3/5, Sensitivity recovery, diminished lower limb reflexes. Therapy: Enoxaparin 60mg every 12h, Immunoglobulin 0.4mg/kg/day/5 doses. Discharge HUGHES 3. [Formula presented] [Formula presented] Conclusions: GBS is caused by an anomalous response of the immune system to an infectious agent. This particular patient presents with a GBS associated with SARS-CoV-2 infection and PE.

Information Generation Rate and its Relationship with the Entropy of Non-Linear Models: Covid-19 Case, Peru 2020

In this paper, entropy was studied in non-linear models including exponential, Gompertz, and logistic, to estimate epidemiological parameters of interest in data from confirmed cases of infection by COVID-19 in Peru. The data related to the spread of COVID-19 in Peru comes from the information available on the INS-Peru institutional portal (2020). The Akaike information criterion (AIC) and the residual standard error (ERR) were considered to evaluate the entropy of the models. The estimation of the parameters of the models was carried out using maximum likelihood and by the Bootstrap method. The results showed that the entropy of the models is related to the information generation rate, associated with the differential in the number of tests applied. Entropy severely affected maximum likelihood estimators. The Bootstrap estimators showed better performance against EMV with the estimated peak of confirmed cases. Bootstrap estimators were significantly affected by sample size, especially when n <= 10. The results of this research suggest considering the entropy and the information generation rate (differential in the application of tests for the diagnosis of COVID-19 in Peru), as well as the use of Bootstrap estimators as an alternative to estimate parameters of epidemiological models.