Detección de isquemia silente en pacientes hipotiroideos con estudios de perfusión miocárdica nuclear

En la evaluación de los procesos subclínicos de ateroesclerosis se realizan estudios para la detección de isquemia silente (IS) en pacientes hipotiroideos asintomáticos intentando identificar grupos de riesgo. El objetivo de este trabajo es detectar con estudios de perfusión miocárdica spect gatillado (GS) la presencia de isquemia silente, cuantificando la severidad y extensión en población hipotiroidea.Facultad de Ciencias Médica

Detección de isquemia silente en pacientes hipotiroideos con estudios de perfusión miocárdica nuclear

En la evaluación de los procesos subclínicos de ateroesclerosis se realizan estudios para la detección de isquemia silente (IS) en pacientes hipotiroideos asintomáticos intentando identificar grupos de riesgo. El objetivo de este trabajo es detectar con estudios de perfusión miocárdica spect gatillado (GS) la presencia de isquemia silente, cuantificando la severidad y extensión en población hipotiroidea.Facultad de Ciencias Médica

Detección de isquemia silente en pacientes hipotiroideos con estudios de perfusión miocárdica nuclear

En la evaluación de los procesos subclínicos de ateroesclerosis se realizan estudios para la detección de isquemia silente (IS) en pacientes hipotiroideos asintomáticos intentando identificar grupos de riesgo. El objetivo de este trabajo es detectar con estudios de perfusión miocárdica spect gatillado (GS) la presencia de isquemia silente, cuantificando la severidad y extensión en población hipotiroidea.Facultad de Ciencias Médica

Mortality prediction in chronic obstructive pulmonary disease comparing the GOLD 2015 and GOLD 2019 staging: a pooled analysis of individual patient data

In 2019, The Global Initiative for Chronic Obstructive Lung Disease (GOLD) modified the grading system for patients with COPD, creating 16 subgroups (1A–4D). As part of the COPD Cohorts Collaborative International Assessment (3CIA) initiative, we aim to compare the mortality prediction of the 2015 and 2019 COPD GOLD staging systems. We studied 17 139 COPD patients from the 3CIA study, selecting those with complete data. Patients were classified by the 2015 and 2019 GOLD ABCD systems, and we compared the predictive ability for 5-year mortality of both classifications. In total, 17 139 patients with COPD were enrolled in 22 cohorts from 11 countries between 2003 and 2017; 8823 of them had complete data and were analysed. Mean±sd age was 63.9±9.8 years and 62.9% were male. GOLD 2019 classified the patients in milder degrees of COPD. For both classifications, group D had higher mortality. 5-year mortality did not differ between groups B and C in GOLD 2015; in GOLD 2019, mortality was greater for group B than C. Patients classified as group A and B had better sensitivity and positive predictive value with the GOLD 2019 classification than GOLD 2015. GOLD 2015 had better sensitivity for group C and D than GOLD 2019. The area under the curve values for 5-year mortality were only 0.67 (95% CI 0.66–0.68) for GOLD 2015 and 0.65 (95% CI 0.63–0.66) for GOLD 2019

Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease

Background: External validations and comparisons of prognostic models or scores are a prerequisite for their use in routine clinical care but are lacking in most medical fields including chronic obstructive pulmonary disease (COPD). Our aim was to externally validate and concurrently compare prognostic scores for 3-year all-cause mortality in mostly multimorbid patients with COPD. Methods: We relied on 24 cohort studies of the COPD Cohorts Collaborative International Assessment consortium, corresponding to primary, secondary, and tertiary care in Europe, the Americas, and Japan. These studies include globally 15,762 patients with COPD (1871 deaths and 42,203 person years of follow-up). We used network meta-analysis adapted to multiple score comparison (MSC), following a frequentist two-stage approach; thus, we were able to compare all scores in a single analytical framework accounting for correlations among scores within cohorts. We assessed transitivity, heterogeneity, and inconsistency and provided a performance ranking of the prognostic scores. Results: Depending on data availability, between two and nine prognostic scores could be calculated for each cohort. The BODE score (body mass index, airflow obstruction, dyspnea, and exercise capacity) had a median area under the curve (AUC) of 0.679 [1st quartile-3rd quartile = 0.655-0.733] across cohorts. The ADO score (age, dyspnea, and airflow obstruction) showed the best performance for predicting mortality (difference AUC(ADO) - AUC(BODE) = 0.015 [95% confidence interval (CI) = - 0.002 to 0.032]; p = 0.08) followed by the updated BODE (AUCBODE updated - AUCBODE = 0.008 [95% CI = -0.005 to +0.022]; p = 0.23). The assumption of transitivity was not violated. Heterogeneity across direct comparisons was small, and we did not identify any local or global inconsistency. Conclusions: Our analyses showed best discriminatory performance for the ADO and updated BODE scores in patients with COPD. A limitation to be addressed in future studies is the extension of MSC network meta-analysis to measures of calibration. MSC network meta-analysis can be applied to prognostic scores in any medical field to identify the best scores, possibly paving the way for stratified medicine, public health, and research

A simple algorithm for the identification of clinical COPD phenotypes

This study aimed to identify simple rules for allocating chronic obstructive pulmonary disease (COPD) patients to clinical phenotypes identified by cluster analyses.Data from 2409 COPD patients of French/Belgian COPD cohorts were analysed using cluster analysis resulting in the identification of subgroups, for which clinical relevance was determined by comparing 3-year all-cause mortality. Classification and regression trees (CARTs) were used to develop an algorithm for allocating patients to these subgroups. This algorithm was tested in 3651 patients from the COPD Cohorts Collaborative International Assessment (3CIA) initiative.Cluster analysis identified five subgroups of COPD patients with different clinical characteristics (especially regarding severity of respiratory disease and the presence of cardiovascular comorbidities and diabetes). The CART-based algorithm indicated that the variables relevant for patient grouping differed markedly between patients with isolated respiratory disease (FEV1, dyspnoea grade) and those with multi-morbidity (dyspnoea grade, age, FEV1 and body mass index). Application of this algorithm to the 3CIA cohorts confirmed that it identified subgroups of patients with different clinical characteristics, mortality rates (median, from 4% to 27%) and age at death (median, from 68 to 76 years).A simple algorithm, integrating respiratory characteristics and comorbidities, allowed the identification of clinically relevant COPD phenotypes

Patients with Crohn's disease have longer post-operative in-hospital stay than patients with colon cancer but no difference in complications' rate

BACKGROUNDRight hemicolectomy or ileocecal resection are used to treat benign conditions like Crohn's disease (CD) and malignant ones like colon cancer (CC).AIMTo investigate differences in pre- and peri-operative factors and their impact on post-operative outcome in patients with CC and CD.METHODSThis is a sub-group analysis of the European Society of Coloproctology's prospective, multi-centre snapshot audit. Adult patients with CC and CD undergoing right hemicolectomy or ileocecal resection were included. Primary outcome measure was 30-d post-operative complications. Secondary outcome measures were post-operative length of stay (LOS) at and readmission.RESULTSThree hundred and seventy-five patients with CD and 2,515 patients with CC were included. Patients with CD were younger (median = 37 years for CD and 71 years for CC (P < 0.01), had lower American Society of Anesthesiology score (ASA) grade (P < 0.01) and less comorbidity (P < 0.01), but were more likely to be current smokers (P < 0.01). Patients with CD were more frequently operated on by colorectal surgeons (P < 0.01) and frequently underwent ileocecal resection (P < 0.01) with higher rate of de-functioning/primary stoma construction (P < 0.01). Thirty-day post-operative mortality occurred exclusively in the CC group (66/2515, 2.3%). In multivariate analyses, the risk of post-operative complications was similar in the two groups (OR 0.80, 95%CI: 0.54-1.17; P = 0.25). Patients with CD had a significantly longer LOS (Geometric mean 0.87, 95%CI: 0.79-0.95; P < 0.01). There was no difference in re-admission rates. The audit did not collect data on post-operative enhanced recovery protocols that are implemented in the different participating centers.CONCLUSIONPatients with CD were younger, with lower ASA grade, less comorbidity, operated on by experienced surgeons and underwent less radical resection but had a longer LOS than patients with CC although complication's rate was not different between the two groups

A simple algorithm for the identification of clinical COPD phenotypes

This study aimed to identify simple rules for allocating chronic obstructive pulmonary disease (COPD) patients to clinical phenotypes identified by cluster analyses.Data from 2409 COPD patients of French/Belgian COPD cohorts were analysed using cluster analysis resulting in the identification of subgroups, for which clinical relevance was determined by comparing 3-year all-cause mortality. Classification and regression trees (CARTs) were used to develop an algorithm for allocating patients to these subgroups. This algorithm was tested in 3651 patients from the COPD Cohorts Collaborative International Assessment (3CIA) initiative.Cluster analysis identified five subgroups of COPD patients with different clinical characteristics (especially regarding severity of respiratory disease and the presence of cardiovascular comorbidities and diabetes). The CART-based algorithm indicated that the variables relevant for patient grouping differed markedly between patients with isolated respiratory disease (FEV1, dyspnoea grade) and those with multi-morbidity (dyspnoea grade, age, FEV1 and body mass index). Application of this algorithm to the 3CIA cohorts confirmed that it identified subgroups of patients with different clinical characteristics, mortality rates (median, from 4% to 27%) and age at death (median, from 68 to 76 years).A simple algorithm, integrating respiratory characteristics and comorbidities, allowed the identification of clinically relevant COPD phenotypes.status: publishe

A simple algorithm for the identification of clinical COPD phenotypes

This study aimed to identify simple rules for allocating chronic obstructive pulmonary disease (COPD) patients to clinical phenotypes identified by cluster analyses. Data from 2409 COPD patients of French/Belgian COPD cohorts were analysed using cluster analysis resulting in the identification of subgroups, for which clinical relevance was determined by comparing 3-year all-cause mortality. Classification and regression trees (CARTs) were used to develop an algorithm for allocating patients to these subgroups. This algorithm was tested in 3651 patients from the COPD Cohorts Collaborative International Assessment (3CIA) initiative. Cluster analysis identified five subgroups of COPD patients with different clinical characteristics (especially regarding severity of respiratory disease and the presence of cardiovascular comorbidities and diabetes). The CART-based algorithm indicated that the variables relevant for patient grouping differed markedly between patients with isolated respiratory disease (FEV1, dyspnoea grade) and those with multi-morbidity (dyspnoea grade, age, FEV1 and body mass index). Application of this algorithm to the 3CIA cohorts confirmed that it identified subgroups of patients with different clinical characteristics, mortality rates (median, from 4% to 27%) and age at death (median, from 68 to 76 years). A simple algorithm, integrating respiratory characteristics and comorbidities, allowed the identification of clinically relevant COPD phenotype

Yellow fever reemergence in Venezuela – Implications for international travelers and Latin American countries during the COVID-19 pandemic

Fundación Universitaria Autónoma de las Américas. Faculty of Medicine. Grupo de Investigacíon Biomedicina. Pereira, Risaralda, Colombia / Colombian Association of Infectious Diseases. Committe on Tropical Medicine, Zoonoses and Travel Medicine. Bogota, Colombia / Fundación Universitaria Autónoma de las Américas. Grupo de Investigacíon GISCA. Semillero de Zoonosis. Sede Pereira, Pereira, Risaralda, Colombia / Instituto para la Investigación en Ciencias Biomédicas. Emerging Infectious Diseases and Tropical Medicine Research Group. Pereira, Risaralda, Colombia / Pan-American Association of Infectious Diseases. Committe on Travel Medicine. Panama City, Panama / Universidad Cientifica del Sur. Master of Clinical Epidemiology and Biostatistics. Lima, Peru / Institución Universitaria Visión de las Américas. Faculty of Medicine. Grupo de Investigacón Biomedicina. Pereira, Risaralda, Colombia / Institución Universitaria Visión de las Américas. Grupo de Investigación GISCA. Semillero de Zoonosis. Sede Pereira, Pereira, Risaralda, Colombia.Colombian Association of Infectious Diseases. Committe on Tropical Medicine, Zoonoses and Travel Medicine. Bogota, Colombia / Fundación Universitaria Autónoma de las Américas. Grupo de Investigacíon GISCA. Semillero de Zoonosis. Sede Pereira, Pereira, Risaralda, Colombia / Instituto para la Investigación en Ciencias Biomédicas. Emerging Infectious Diseases and Tropical Medicine Research Group. Pereira, Risaralda, Colombia / Institución Universitaria Visión de las Américas. Grupo de Investigación GISCA. Semillero de Zoonosis. Sede Pereira, Pereira, Risaralda, Colombia.Pan-American Association of Infectious Diseases. Committe on Travel Medicine. Panama City, Panama / Instituto Conmemorativo Gorgas de Estudios de la Salud. Clinical Research Deparment. Investigador SNI Senacyt Panama. Panama City, Panama.Pan-American Association of Infectious Diseases. Committe on Travel Medicine. Panama City, Panama / University of Colorado Anschutz Medical Center. Department of Medicine. Division of Infectious Diseases. Aurora, CO, USA / Hospital Infantil de México. Federico Góomez, Méexico City, Mexico.Biomedical Research and Therapeutic Vaccines Institute. Ciudad Bolivar, Venezuela.Colombian Association of Infectious Diseases. Committe on Tropical Medicine, Zoonoses and Travel Medicine. Bogota, Colombia / Universidad de Cordoba. Instituto de Investigaciones Biológicas del Trópico. Colombia.Colombian Association of Infectious Diseases. Committe on Tropical Medicine, Zoonoses and Travel Medicine. Bogota, Colombia / Pan-American Association of Infectious Diseases. Committe on Travel Medicine. Panama City, Panama / Hospital Universitario de Sincelejo. Infectious Diseases and Infection Control Research Group. Sincelejo, Sucre, Colombia / Universidad del Atlático. SUE Caribe. Programa del Doctorado de Medicina Tropical. Barranquilla, Colombia.Colombian Association of Infectious Diseases. Committe on Tropical Medicine, Zoonoses and Travel Medicine. Bogota, Colombia / Universidad Cooperativa de Colombia. Grupo de Investigación en Ciencias Animales. Bucaramanga, Colombia.Fundación Universitaria Autónoma de las Américas. Faculty of Medicine. Grupo de Investigacíon Biomedicina. Pereira, Risaralda, Colombia / Colombian Association of Infectious Diseases. Committe on Tropical Medicine, Zoonoses and Travel Medicine. Bogota, Colombia / Instituto para la Investigación en Ciencias Biomédicas. Emerging Infectious Diseases and Tropical Medicine Research Group. Pereira, Risaralda, Colombia / Pan-American Association of Infectious Diseases. Committe on Travel Medicine. Panama City, Panama / Fundación Universitaria Autónoma de las Américas. Faculty of Medicine. Semillero de Investigación en Infecciones Emergentes y Medicina Tropical. Pereira, Risaralda, Colombia / Institución Universitaria Visión de las Américas. Faculty of Medicine. Grupo de Investigacón Biomedicina. Pereira, Risaralda, Colombia.Instituto Médico La Floresta. Caracas, Venezuela.Universidad del Norte and Hospital Universidad del Norte. Department of Medicine, Health Sciences Division. Barranquilla, Colombia.Pan-American Association of Infectious Diseases. Committe on Travel Medicine. Panama City, Panama / Universidad Central de Venezuela. Faculty of Medicine. Caracas, Venezuela.Institute of Infectious Diseases Emilio Ribas, São Paulo, SP, Brazil.Universidad Estatal del Sur de Manabí. Carrera de Laboratorio Clínico. Cantón Jipijapa, Ecuador.Universidad Católica del Maule. Vicerrectoría de Investigación y Postgrado. Chile.Universidad Central de Venezuela. Faculty of Medicine. Hospital Universitario de Caracas. Department of Internal Medicine. Cardiology Division. Caracas, Venezuela.Hospital José María Vargas. La Guaira, Vargas, Venezuela.Universidad Castilla La Mancha. Facultad de Medicina. Complejo Hospitalario Universitario de Albacete. Servicio de Anatomía Patológica. Albacete, Spain.International Airport Camilo Daza. Health Care Service. Cúcuta, Norte de Santander, Colombia.Universidade de São Paulo. Faculdade de Saúde Pública. Departamento de Epidemiologia. São Paulo, SP, Brazil.Universidad Técnica de Ambato. Ambato, Ecuador.Hospital Transfrontalier Cerdayna. Catalonia, Spain.University of Illinois. Department of Internal Medicine. Division of Infectious Diseases. Chicago, IL, USA.Hospital Evangélico de Montevideo. Montevideo, Uruguay.Universidad Cooperativa de Colombia. Grupo de Investigación en Ciencias Animales. Bucaramanga, Colombia.Instituto Nacional de Salud del Niño San Borja. Infectious Diseases Division. Lima, Peru / Universidad Privada de Tacna. Facultad de Ciencias de la Salud. Tacna, Peru.Hospital Universitario Departamental de Nariño. Pasto, Nariño, Colombia.Universidad de Manizales. School of Medicine. Coordination of Microbiology. Manizales, Caldas, Colombia / Grupo de Resistencia Antibiótica de Manizales. Manizales, Caldas, Colombia.Clínica San Josée. Cúcuta, Norte de Santander, Colombia / Hospital Universitario Erasmo Meoz. Cúcuta, Norte de Santander, Colombia.Hospital de Niños J. M. de Los Ríos. Division of Infectious Diseases. Caracas, Venezuela / Venezuelan Society of Infectious Diseases. Executive Board. Caracas, Venezuela.University of Colorado Anschutz Medical Center. Department of Medicine. Division of Infectious Diseases. Aurora, CO, USA.Universidad Industrial de Santander. Department of Internal Medicine. Bucaramanga, Santander, Colombia.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil / aq Universidade Federal do Para. Faculdade de Medicina. Belém, PA, Brasil.Hospital de Infecciosas F. Muñíz. Buenos Aires, Argentina.GSK Vaccines. Clinical Research & Development and Medical Affairs. Rio de Janeiro, RJ, Brazil.Hospital de Trauma y Emergencias Federico Abete. Buenos Aires, Argentina.Hospital Británico de Buenos Aires. Buenos Aires, Argentina.Pontificia Universidad Católica de Chile. School of Medicine. Department of Pediatric Infectious Diseases and Immunology. Santiago de Chile, Chile.Hospital de Infecciosas F. Muñíz. Buenos Aires, Argentina.Hospital de Infecciosas F. Muñíz. Buenos Aires, Argentina / Universidad de Buenos Aires. Buenos Aires, Argentina.Latin American Society for Travel Medicine. Panel of Sports and Travel. Buenos Aires, Argentina.Universidad Internacional SEK. Health Sciences Faculty. Research Group of Emerging Diseases, Ecoepidemiology and Biodiversity. Quito, Ecuador / Universidad Central de Venezuela. Facultad de Ciencias. Instituto de Zoología y Ecología Tropical. Caracas, Venezuela.Pan-American Association of Infectious Diseases. Committe on Travel Medicine. Panama City, Panama / Instituto de Investigaciones Biomédicas. Clínica IDB Cabudare. Department of Infectious Diseases and Tropical Medicine. Lara, Venezuela / Venezuelan Science Incubator and the Zoonosis and Emerging Pathogens Regional Collaborative Network. Infectious Diseases Research Branch. Lara, Venezuela / Instituto de Estudios Avanzados. Laboratorio de Señalización Celular y Bioquímica de Parásitos. Caracas, Caracas, Venezuela / Academia Nacional de Medicina. Caracas, Venezuela / The Mount Sinai Hospital-Icahn School of Medicine at Mount Sinai. Department of Pathology, Molecular and Cellbased Medicine. Direction of Microbiology. New York, USA