Epidemiological Algorithm for Early Detection of COVID-19 Cases in a Mexican Oncologic Center

An early detection tool for latent COVID-19 infections in oncology staff and patients is essential to prevent outbreaks in a cancer center. (1) Background: In this study, we developed and implemented two early detection tools for the radiotherapy area to identify COVID-19 cases opportunely. (2) Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data were collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). Cut-off values were established according to the algorithm models. SARS-CoV-2 qRT-PCR tests confirmed the positive algorithms individuals. (3) Results: Oncology staff members (n = 142) were tested, and 14% (n = 20) were positives for the R-Track algorithm; 75% (n = 15) were qRT-PCR positive. The S-Facts Algorithm identified 7.75% (n = 11) positive oncology staff members, and 81.82% (n = 9) were qRT-PCR positive. Oncology patients (n = 369) were evaluated, and 1.36% (n = 5) were positive for the Algorithm used. The five patients (100%) were confirmed by qRT-PCR. (4) Conclusions: The proposed early detection tools have proved to be a low-cost and efficient tool in a country where qRT-PCR tests and vaccines are insufficient for the population. View Full-Tex

Epidemiological Algorithm and Early Molecular Testing to Prevent COVID-19 Outbreaks in a Mexican Oncologic Center

Introduction: Prevention strategies and detection of latent COVID-19 infections in oncology staff and oncologic patients are essential to prevent outbreaks in a cancer center. In this study, we used two statistical predictive models in oncology staff and patients from the radiotherapy area to prevent outbreaks and detect COVID-19 cases. Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data was collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). According to the algorithm\u27s models, cut-off values were established. SARS-CoV-2 qRT-PCR tests confirmed the algorithm\u27s positive individuals. Results: Oncology staff members (n=142) were tested, and 14% (n=20) were positives for the R-Track algorithm; 75% (n=15) were qRT-PCR positive. The S-Facts algorithm identified 7.75% (n=11) positive oncology staff members, and 81.82% (n=9) were qRT-PCR positive. Oncology patients (n=369) were evaluated, and 1.36% (n=5) were positive for the algorithms. The 5 patients (100%) were confirmed by qRT-PCR at a very early stage. Conclusions: The proposed algorithms could prove to become an essential prevention tool in countries where qRT-PCR tests and vaccines are insufficient for the population

Mexican radiation dermatitis management consensus

Abstract Background: Radiotherapy (RT) is an essential element in cancer treatment: 50–70% of cancer patients receive RT at some time of the course of their disease. Of these, almost 95% experience some grade of radiation dermatitis (RD). RD can affect patient’s quality of life during and after treatment. Consequently, the management of RD is important. There are few randomized controlled clinical trials on interventions used to prevent and treat RD and no standardized consensus on RD management. A panel of opinion leaders of the Mexican Society of Radiotherapy (SOMERA) took part in a study of oncologic practice in Mexico. The following clinical guide is referenced both by the national practice reality and international evidence. Materials and methods: This RD management guide is based on input provided by 25 Mexican radiation oncologists, whose criteria were gathered using the Delphi Method and article review. Results: Twenty-one questions about experience in RD treatment were voted. More than 80% of the panel agreed with: the use of dermocosmetics/medical device in prevention and in treatment of RD grades 1–2. As for grade 3, they recommend individualizing each case and dermatologist evaluation. Topical steroids should be used when there is skin itching or pain. Consider the use of natural soaking elements. Skin care must be continued to avoid or reduce severity of late radiation skin lesions. Conclusion: This consensus was developed as a supportive educational tool that can be adapted to individual clinical needs, useful for professionals involved in the treatment of RT patients.  

Memorias de la Brigada de respuesta inmediata del Hospital Universitario de la UANL en Tabasco

El 3 de noviembre de 2007 una brigada de médicos voluntarios partió al estado de Tabasco, en respuesta al desastre que azotó dicho estado, con lo que la Facultad de Medicina y Hospital Universitario Dr. José Eleuterio González refrendó el compromiso que tiene la Universidad Autónoma de Nuevo León con la sociedad, y dejó de manifiesto la importancia que le da a la formación de médicos con alto valor humanitario y espíritu de entrega. En las siguientes líneas se plasman las experiencias vividas y el trabajo realizado por los integrantes de la Brigada de respuesta inmediata Hospital Universitario en Tabasco

Epidemiological Algorithm for Early Detection of COVID-19 Cases in a Mexican Oncologic Center

An early detection tool for latent COVID-19 infections in oncology staff and patients is essential to prevent outbreaks in a cancer center. (1) Background: In this study, we developed and implemented two early detection tools for the radiotherapy area to identify COVID-19 cases opportunely. (2) Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data were collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). Cut-off values were established according to the algorithm models. SARS-CoV-2 qRT-PCR tests confirmed the positive algorithms individuals. (3) Results: Oncology staff members (n = 142) were tested, and 14% (n = 20) were positives for the R-Track algorithm; 75% (n = 15) were qRT-PCR positive. The S-Facts Algorithm identified 7.75% (n = 11) positive oncology staff members, and 81.82% (n = 9) were qRT-PCR positive. Oncology patients (n = 369) were evaluated, and 1.36% (n = 5) were positive for the Algorithm used. The five patients (100%) were confirmed by qRT-PCR. (4) Conclusions: The proposed early detection tools have proved to be a low-cost and efficient tool in a country where qRT-PCR tests and vaccines are insufficient for the population