DSpace Repository :: Browsing by Author "Barbé, Mario"

Browsing by Author "Barbé, Mario"

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Campylobacter spp. Prevalence in Santiago, Chile: A Study Based on Molecular Detection in Clinical Stool Samples from 2014 to 2019
(2023) Porte, Lorena; Pérez, Caricia; Barbé, Mario; Varela, Carmen; Vollrath, Valeska; Legarraga, Paulette; Weitzel, Thomas
Campylobacter spp. is an emerging cause of infectious diarrhea worldwide. In South American countries such as Chile, its prevalence is underestimated due to inadequate detection methods. Gastrointestinal multiplex PCR panels (GMP) permit rapid and sensitive detection of bacterial pathogens and provide important epidemiological information. This study aimed to analyze Campylobacter epidemiology using the results of molecular methods and to compare molecular detection results to those of culture methods. We performed a retrospective, descriptive analysis of Campylobacter spp. detected in clinical stool samples between 2014-2019 by GMP and culture. Within 16,582 specimens examined by GMP, Campylobacter was the most prevalent enteropathogenic bacteria (8.5%), followed by Salmonella spp. (3.9%), Shigella spp./enteroinvasive Escherichia coli (EIEC) (1.9%), and Yersinia enterocolitica (0.8%). The highest Campylobacter prevalence occurred in 2014/2015. Campylobacteriosis affected more males (57.2%) and adults from 19-65 years (47.9%) and showed a bimodal seasonality with summer and winter peaks. In 11,251 routine stool cultures, Campylobacter spp. was detected in 4.6%, mostly C. jejuni (89.6%). Among 4533 samples tested by GMP and culture in parallel, GMP showed a superior sensitivity (99.1% versus 50%, respectively). The study suggests that Campylobacter spp. is the most frequent bacterial enteropathogen in Chile.
Covid-19 in South America: clinical and epidemiological characteristics among 381 patients during the early phase of the pandemic in Santiago, Chile
(2020) Vial, Macarena; Peters, Anne; Pérez, Inia; Spencer-Sandino, María; Barbé, Mario; Porte, Lorena; Weitzel, Thomas; Aylwin, Mabel; Vial, Pablo; Araos, Rafael; Munita, José
Background: Understanding the characteristics of the Covid-19 pandemic in different geographical regions, ethnic and socioeconomic settings are of emerging importance. This study presents the demographic and clinical features of SARS-CoV-2 infected patients in a large private healthcare center in Santiago, Chile, during the first month of the pandemic. Methods: We analyzed the demographics, laboratory and clinical characteristics including severity and outcome of all patients diagnosed with Covid-19 during the first month of the pandemic. SARS-2-CoV infection was confirmed by RT-PCR in nosopharyngeal samples. The primary outcome was a composite of ICU admission or all-cause, in-hospital mortality. Clinical and laboratory parameters of hospitalized patients were analyzed regarding their association with the primary outcome. Results: From March 3 to April 4, 2020, 3679 individuals were tested for SARS-CoV-2 in our hospital. Of those, 381 had Covid-19 and were included into this analysis. Most patients (99.2%) were Chileans, 12% returning from recent travel. The median age was 39 years (IQR 31–49) and 52% were female. A total of 88 patients (23.1%) were hospitalized; 18 (3.7%) required ICU and/or died. The overall mortality was 0.7%. Increased body mass index (BMI) and elevated C-reactive protein (CRP) were independently associated with ICU care or death. Conclusion: During the first weeks of the pandemic in Chile, most Covid-19 patients were young, with low rates of hospitalization, ICU requirement, and fatality. BMI and CRP on admission were predictors for severity. Our data provide important information on the clinical course and outcome of Covid-19 in a Latin American setting.
Machine learning‑based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome
(2025) Meza‑Fuentes, Gabriela; Delgado, Iris; Barbé, Mario; Sánchez‑Barraza, Ignacio; Retamal, Mauricio; López, René
Introduction Acute respiratory distress syndrome (ARDS) is a severe condition with high morbidity and mortality, characterized by significant clinical heterogeneity. This heterogeneity complicates treatment selection and patient inclusion in clinical trials. Therefore, the objective of this study is to identify physiological subphenotypes of ARDS using machine learning, and to determine ventilatory variables that can effectively discriminate between these sub‑phenotypes in a bedside setting with high performance, highlighting potential utility for future clinical stratification approaches. Methodology A retrospective cohort study was conducted using data from our ICU, covering admissions from 2017 to 2021. The study included 224 patients over 18 years of age diagnosed with ARDS according to the Berlin criteria and undergoing invasive mechanical ventilation (IMV). Data on physiological and ventilatory variables were collected during the first 24 h IMV. We applied machine learning techniques to categorize subphenotypes in ARDS patients. Initially, we employed the unsupervised Gaussian Mixture Classification Model approach to group patients into sub‑phenotypes. Subsequently, we applied supervised models such as XGBoost to perform root cause analysis, evaluate the classification of patients into these subgroups, and measure their performance. Results Our models identified two ARDS subphenotypes with significant clinical differences and significant outcomes. Subphenotype Efficient (n = 172) was characterized by lower mortality, lower clinical severity and presented a less restrictive pattern with better gas exchange compared to Subphenotype Restrictive (n = 52), which showed the opposite. The models demonstrated high performance with an area under the ROC curve of 0.94, sensitivity of 94.2% and specificity of 87.5%, in addition to an F1 score of 0.85. The most influential variables in the discrimination of subphenotypes were distension pressure, respiratory frequency and exhaled carbon dioxide volume. Conclusion This study presents an approach to improve subphenotype categorization in ARDS. The generation of clustering and prediction models by machine learning involving clinical, ventilatory mechanics, and gas exchange variables allowed for more accurate stratification of patients. These findings have the potential to optimize individualized treatment selection and improve clinical outcomes in patients with ARDS.
Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care:Systematic Literature Assessment and Qualitative Synthesis
(2020) Capurro, Daniel; Barbé, Mario; Daza, Claudio; Santa Maria, Josefa; Trincado, Javier
Background: Inclusion criteria for observational studies frequently contain temporal entities and relations. The use of digital phenotypes to create cohorts in electronic health record–based observational studies requires rich functionality to capture these temporal entities and relations. However, such functionality is not usually available or requires complex database queries and specialized expertise to build them. Objective: The purpose of this study is to systematically assess observational studies reported in critical care literature to capture design requirements and functionalities for a graphical temporal abstraction-based digital phenotyping tool. Methods: We iteratively extracted attributes describing patients, interventions, and clinical outcomes. We qualitatively synthesized studies, identifying all temporal and nontemporal entities and relations. Results: We extracted data from 28 primary studies and 367 temporal and nontemporal entities. We generated a synthesis of entities, relations, and design patterns. Conclusions: We report on the observed types of clinical temporal entities and their relations as well as design requirements for a temporal abstraction-based digital phenotyping system. The results can be used to inform the development of such a system

Browsing by Author "Barbé, Mario"

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