COVID-19 vaccines reduce mortality in hospitalized patients with oxygen requirements: Differences between vaccine subtypes. A multicontinental cohort study.

The aim of this study was to analyze whether the coronavirus disease 2019 (COVID-19) vaccine reduces mortality in patients with moderate or severe COVID-19 disease requiring oxygen therapy. A retrospective cohort study, with data from 148 hospitals in both Spain (111 hospitals) and Argentina (37 hospitals), was conducted. We evaluated hospitalized patients for COVID-19 older than 18 years with oxygen requirements. Vaccine protection against death was assessed through a multivariable logistic regression and propensity score matching. We also performed a subgroup analysis according to vaccine type. The adjusted model was used to determine the population attributable risk. Between January 2020 and May 2022, we evaluated 21,479 COVID-19 hospitalized patients with oxygen requirements. Of these, 338 (1.5%) patients received a single dose of the COVID-19 vaccine and 379 (1.8%) were fully vaccinated. In vaccinated patients, mortality was 20.9% (95% confidence interval [CI]: 17.9-24), compared to 19.5% (95% CI: 19-20) in unvaccinated patients, resulting in a crude odds ratio (OR) of 1.07 (95% CI: 0.89-1.29; p = 0.41). However, after considering the multiple comorbidities in the vaccinated group, the adjusted OR was 0.73 (95% CI: 0.56-0.95; p = 0.02) with a population attributable risk reduction of 4.3% (95% CI: 1-5). The higher risk reduction for mortality was with messenger RNA (mRNA) BNT162b2 (Pfizer) (OR 0.37; 95% CI: 0.23-0.59; p < 0.01), ChAdOx1 nCoV-19 (AstraZeneca) (OR 0.42; 95% CI: 0.20-0.86; p = 0.02), and mRNA-1273 (Moderna) (OR 0.68; 95% CI: 0.41-1.12; p = 0.13), and lower with Gam-COVID-Vac (Sputnik) (OR 0.93; 95% CI: 0.6-1.45; p = 0.76). COVID-19 vaccines significantly reduce the probability of death in patients suffering from a moderate or severe disease (oxygen therapy).

Plasma and stool metabolomics to identify microbiota derived-biomarkers of metabolic dysfunction-associated fatty liver disease: effect of PNPLA3 genotype.

INTRODUCTION: Non-invasive biomarkers are needed for metabolic dysfunction-associated fatty liver disease (MAFLD), especially for patients at risk of disease progression in high-prevalence areas. The microbiota and its metabolites represent a niche for MAFLD biomarker discovery. However, studies are not reproducible as the microbiota is variable. OBJECTIVES: We aimed to identify microbiota-derived metabolomic biomarkers that may contribute to the higher MAFLD prevalence and different disease severity in Latin America, where data is scarce. METHODS: We compared the plasma and stool metabolomes, gene patatin-like phospholipase domain-containing 3 (PNPLA3) rs738409 single nucleotide polymorphism (SNP), diet, demographic and clinical data of 33 patients (12 simple steatosis and 21 steatohepatitis) and 19 healthy volunteers (HV). The potential predictive utility of the identified biomarkers for MAFLD diagnosis and progression was evaluated by logistic regression modelling and ROC curves. RESULTS: Twenty-four (22 in plasma and 2 in stool) out of 424 metabolites differed among groups. Plasma triglyceride (TG) levels were higher among MAFLD patients, whereas plasma phosphatidylcholine (PC) and lysoPC levels were lower among HV. The PNPLA3 risk genotype was related to higher plasma levels of eicosenoic acid or fatty acid 20:1 (FA(20:1)). Body mass index and plasma levels of PCaaC24:0, FA(20:1) and TG (16:1_34:1) showed the best AUROC for MAFLD diagnosis, whereas steatosis and steatohepatitis could be discriminated with plasma levels of PCaaC24:0 and PCaeC40:1. CONCLUSION: This study identified for the first time MAFLD potential non-invasive biomarkers in a Latin American population. The association of PNPLA3 genotype with FA(20:1) suggests a novel metabolic pathway influencing MAFLD pathogenesis.

Electropore Formation in Mechanically Constrained Phospholipid Bilayers.

Molecular dynamics simulations of lipid bilayers in aqueous systems reveal how an applied electric field stabilizes the reorganization of the water-membrane interface into water-filled, membrane-spanning, conductive pores with a symmetric, toroidal geometry. The pore formation process and the resulting symmetric structures are consistent with other mathematical approaches such as continuum models formulated to describe the electroporation process. Some experimental data suggest, however, that the shape of lipid electropores in living cell membranes may be asymmetric. We describe here the axially asymmetric pores that form when mechanical constraints are applied to selected phospholipid atoms. Electropore formation proceeds even with severe constraints in place, but pore shape and pore formation time are affected. Since lateral and transverse movement of phospholipids may be restricted in cell membranes by covalent attachments to or non-covalent associations with other components of the membrane or to membrane-proximate intracellular or extracellular biomolecular assemblies, these lipid-constrained molecular models point the way to more realistic representations of cell membranes in electric fields.

Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach.

In 2002, Bandt and Pompe [Phys. Rev. Lett. 88, 174102 (2002)] introduced a successfully symbolic encoding scheme based on the ordinal relation between the amplitude of neighboring values of a given data sequence, from which the permutation entropy can be evaluated. Equalities in the analyzed sequence, for example, repeated equal values, deserve special attention and treatment as was shown recently by Zunino and co-workers [Phys. Lett. A 381, 1883 (2017)]. A significant number of equal values can give rise to false conclusions regarding the underlying temporal structures in practical contexts. In the present contribution, we review the different existing methodologies for treating time series with tied values by classifying them according to their different strategies. In addition, a novel data-driven imputation is presented that proves to outperform the existing methodologies and avoid the false conclusions pointed by Zunino and co-workers.

User-centered design improves the usability of drug-drug interaction alerts: Experimental comparison of interfaces.

Clinical Decision Support Systems can alert health professionals about drug interactions when they prescribe medications. The Hospital Italiano de Buenos Aires in Argentina developed an electronic health record with drug-drug interaction alerts, using traditional software engineering techniques and requirements. Despite enhancing the drug-drug interaction knowledge database, the alert override rate of this system was very high. We redesigned the alert system using user-centered design (UCD) and participatory design techniques to enhance the drug-drug interaction alert interface. This paper describes the methodology of our UCD. We used crossover method with realistic, clinical vignettes to compare usability of the standard and new software versions in terms of efficiency, effectiveness, and user satisfaction. Our study showed that, compared to the traditional alert system, the UCD alert system was more efficient (alerts faster resolution), more effective (tasks completed with fewer errors), and more satisfying. These results indicate that UCD techniques that follow ISO 9241-210 can generate more usable alerts than traditional design.

Size-controlled nanopores in lipid membranes with stabilizing electric fields.

Molecular dynamics (MD) has been shown to be a useful tool for unveiling many aspects of pore formation in lipid membranes under the influence of an applied electric field. However, the study of the structure and transport properties of electropores by means of MD has been hampered by difficulties in the maintenance of a stable electropore in the typically small simulated membrane patches. We describe a new simulation scheme in which an initially larger porating field is systematically reduced after pore formation to lower stabilizing values to produce stable, size-controlled electropores, which can then be characterized at the molecular level. A new method allows the three-dimensional modeling of the irregular shape of the pores obtained as well as the quantification of its volume. The size of the pore is a function of the value of the stabilizing field. At lower fields the pore disappears and the membrane recovers its normal shape, although in some cases long-lived, fragmented pores containing unusual lipid orientations in the bilayer are observed.

Lack of maintenance of gait pattern as measured by instrumental methods suggests psychogenic gait.

Fluctuation is a common feature of all psychogenic gait disorder (PGD) patterns. Whether this fluctuation involves only the degree of impairment or whether it affects the gait pattern itself remains an interesting question. We hypothesize that, on repeated measurements, both normal and abnormal gait may present quantitative differences while maintaining their basic underlying pattern; conversely, in psychogenic gait, the basic pattern appears not to be preserved. Using an optoelectronic system, data acquired from 19 normal subjects and 66 patients were applied to train a neural network (NN) and subsequently classify gait patterns into four different groups (normal, ataxic, spastic-paraparetic and parkinsonian). Five patients who fulfilled clinical criteria for psychogenic gait and six controls were then prospectively evaluated on two separate occasions, three months apart. Normal controls and ataxic, parkinsonian or spastic patients were correctly identified by the NN, and categorized within the corresponding groups at baseline as well as at a three-month follow-up evaluation. NN analysis showed that after three months, no PGD patient preserved the gait pattern detected at baseline, even though this finding was not clinically apparent. Modification of gait pattern detected by repeated kinematic measurement and NN analysis could suggest the presence of PGD, particularly in difficult-to-diagnose cases.

Automatic Integration of Clinical and Genetic Data Using cBioPortal.

Precision medicine seeks to improve the prevention, diagnosis and treatment of patients based on genetic characteristics unique to each person. In oncology, therapeutic decisions have been established based on the genomic characteristics of each patient's tumor. Data integration is key for the successful implementation of precision medicine since it is necessary for both studying a large volume of data from different sources and working with an interdisciplinary and translational vision. In this work, a bioinformatic process was successfully implemented that allows the integration of patients' genomic data, from two molecular biology laboratories, with their clinical data provided by their electronic medical records. For this, the REDCap data capture software, the cBioPortal visualization and analysis software, and a computer tool developed to automate the processing and annotation of the information in REDCap were used to be included in cBioPortal, for the "Map of Tumor Genomic Actionability of Argentina" project.

Design and development of an early warning score for covid-19 hospitalized patients.

Pandemics pose a major challenge for public health preparedness, requiring a coordinated international response and the development of solid containment plans. Early and accurate identification of high-risk patients in the course of the current COVID-19 pandemic is vital for planning and making proper use of available resources. The purpose of this study was to identify the key variables that account for worse outcomes to create a predictive model that could be used effectively for triage. Through literature review, 44 variables that could be linked to an unfavorable course of COVID-19 disease were obtained, including clinical, laboratory, and X-ray variables. These were used for a 2-round modified Delphi processing with 14 experts to select a final list of variables with the greatest predictive power for the construction of a scoring system, leading to the creation of a new scoring system: the COVID-19 Severity Index. The analysis of the area under the curve for the COVID-19 Severity Index was 0.94 to predict the need for ICU admission in the following 24 hours against 0.80 for NEWS-2. Additionally, the digital medical record of the Hospital Italiano de Buenos Aires was electronically set for an automatic calculation and constant update of the COVID-19 Severity Index. Specifically designed for the current COVID-19 pandemic, COVID-19 Severity Index could be used as a reliable tool for strategic planning, organization, and administration of resources by easily identifying hospitalized patients with a greater need of intensive care.

La pandemia por COVID-19 planteó un desafío para el sistema salud, debido a la gran demanda de pacientes hospitalizados. La identificación temprana de pacientes hospitalizados con riesgo de evolución desfavorable es vital para asistir en forma oportuna y planificar la demanda de recursos. El propósito de este estudio fue identificar las variables predictivas de mala evolución en pacientes hospitalizados por COVID-19 y crear un modelo predictivo que pueda usarse como herramienta de triage. A través de una revisión narrativa, se obtuvieron 44 variables vinculadas a una evolución desfavorable de la enfermedad COVID-19, incluyendo variables clínicas, de laboratorio y radiográficas. Luego se utilizó un procesamiento por método Delphi modificado de 2 rondas para seleccionar una lista final de variables incluidas en el score llamado COVID-19 Severity Index. Luego se calculó el Área Bajo la Curva (AUC) del score para predecir el pase a terapia intensiva en las próximas 24 horas. El score presentó un AUC de 0,94 frente a 0,80 para NEWS-2. Finalmente se agregó el COVID-19 Severity Index a la historia clínica electrónica de un hospital universitario de alta complejidad. Se programó para que el mismo se actualice de manera automática, facilitando la planificación estratégica, organización y administración de recursos a través de la identificación temprana de pacientes hospitalizados con mayor riesgo de transferencia a la Unidad de Cuidados Intensivos.

[Ewing sarcoma of the bone. Multidisciplinary approach and oncological results in 88 patients]. / Sarcoma de Ewing óseo. Enfoque multidisciplinario y resultados oncológicos en 88 pacientes.

Ewing sarcoma of the bone is a rare, highly aggressive tumor that typically affects children and young adults. In Argentina, the lack of Ewing's sarcoma registries reflects in the absence of information regarding prevalence, treatment protocols and patient's outcome. The purpose of this study was to analyze, in a group of patients diagnosed with Ewing sarcoma of the bone, treated with chemotherapy and limb-conserving surgery, their overall survival rate, local recurrence rate, and oncological risk factors. A retrospective research was conducted between 1990 and 2017. Eighty-eight patients with Ewing sarcoma of the bone matched the inclusion criteria. Median age was 14.5 years and median follow-up was 8.8 years. Overall survival rate was 79.5%, 69% and 64% at 2, 5 and 10 years respectively. Negative prognostic factors, associated with less survival rate after univariate analysis, were: bad response to chemotherapy (tumoral necrosis 0-89%), age > 16 years-old, central tumor localization and local recurrence. Gender and tumor size were not significant prognostic factors. After multivariate analysis, response to chemotherapy remained statistical significant. Local recurrence-free survival rate at 2 and 5 years was 87%. Tumor response to chemotherapy (0-89%) was the only significant factor for local recurrence. We consider that limb-salvage surgery, with neoadjuvant and adjuvant chemotherapy, are the mainstays of treatment for Ewing's sarcoma, with an overall survival rate, at 5 years, of 69%. In this population, response to chemotherapy is the most relevant prognostic factor, being associated with both local recurrence and overall survival.

El sarcoma de Ewing óseo es un tumor poco frecuente, agresivo, que afecta principalmente a niños y adultos jóvenes. Existe ausencia de registros en nuestro país respecto de la prevalencia de esta enfermedad, los esquemas de tratamiento utilizados y sus resultados. El objetivo fue analizar, en un grupo de pacientes con sarcoma de Ewing óseo tratados con quimioterapia y cirugía de conservación de miembro, las tasas de supervivencia global, de recurrencia local y los factores de riesgo oncológicos. Se incluyó a 88 pacientes. La edad media de la serie fue de 14.5 años y el seguimiento promedio de 8.8 años. La tasa de supervivencia global fue de 79.5% a los 2 años, de 69% a 5 años y de 64% a 10 años. Los factores pronósticos negativos asociados a menor supervivencia fueron: mala respuesta a la quimioterapia, edad > de 16 años, localización central, y recurrencia local. En el análisis multivariable únicamente la respuesta a la quimioterapia tuvo significancia estadística. La tasa libre de recurrencia local a 2 y 5 años fue del 87%. La mala respuesta a la quimioterapia fue el único factor significativo para la recurrencia local. Consideramos que la cirugía de conservación de miembro asociada a quimioterapia pre y postoperatoria debe ser el tratamiento para el sarcoma de Ewing óseo, alcanzando de esta manera una supervivencia global a 5 años del 69%. En nuestra serie, la respuesta a la quimioterapia ha sido el factor pronóstico más relevante para supervivencia y recurrencia local.

Blunting of circadian rhythms and increased acrophase variability in sleep-time hypertensive subjects.

24 h and ultradian rhythms of blood pressure (BP) have been previously shown to be disorganized in nocturnal hypertensive subjects. The present study was undertaken to further analyze the ultradian and circadian BP rhythm structure in sleep-time hypertensive subjects with normal or elevated awake-time BP levels. Fourier analysis was used to fit 24, 12, 8, and 6 h curves to mean BP as well as heart rate (HR) time series data derived from 24 h ambulatory blood pressure monitoring. Awake and sleep periods were defined according to individual sleep diaries. Awake-time hypertension was defined as diurnal systolic (SBP) and/or diastolic BP (DBP) means > or =135/85 mmHg. Sleep-time hypertension was defined as nocturnal SBP and/or DBP means > or =120/70 mmHg. The sample included 240 awake-time normotensive subjects (180 sleep-time normotensives and 60 sleep-time hypertensives) and 138 untreated awake-time hypertensive subjects (31 sleep-time normotensives and 107 sleep-time hypertensives). The amplitude and integrity (i.e., percent rhythm) of the 24 and 12 h BP rhythms were lower in the sleep-time hypertensive subjects and higher in the awake-time hypertensive subjects. However, no differences were detected when the integrity and amplitude of the 6 and 8 h mean BP rhythms were analyzed. The sleep-time hypertensive group showed significantly higher 24 h BP rhythm acrophase variability. No differences could be found in any of the HR rhythm parameters. Altogether, the findings suggest a disorganization of the BP circadian rhythm in sleep-time hypertensives that results in reduced 24 h rhythm amplitude and integrity that could be related to cardiovascular risk.

Inteligencia artificial en enfermedades infecciosas / Artificial intelligence in infectious diseases

Estamos asistiendo a una verdadera revolución tecnológi-ca en el campo de la salud. Los procesos basados en la aplicación de la inteligencia artificial (IA) y el aprendizaje automático (AA) están llegando progresivamente a todas las áreas disciplinares, y su aplicación en el campo de las enfermedades infecciosas es ya vertiginoso, acelerado por la pandemia de COVID-19.Hoy disponemos de herramientas que no solamente pue-den asistir o llevar adelante el proceso de toma de deci-siones basadas en guías o algoritmos, sino que también pueden modificar su desempeño a partir de los procesos previamente realizados. Desde la optimización en la identificación de microorganis-mos resistentes, la selección de candidatos a participar en ensayos clínicos, la búsqueda de nuevos agentes terapéu-ticos antimicrobianos, el desarrollo de nuevas vacunas, la predicción de futuras epidemias y pandemias, y el segui-miento clínico de pacientes con enfermedades infecciosas hasta la asignación de recursos en el curso de manejo de un brote son actividades que hoy ya pueden valerse de la inteligencia artificial para obtener un mejor resultado. El desarrollo de la IA tiene un potencial de aplicación expo-nencial y sin dudas será uno de los determinantes principa-les que moldearán la actividad médica del futuro cercano.Sin embargo, la maduración de esta tecnología, necesaria para su inserción definitiva en las actividades cotidianas del cuidado de la salud, requiere la definición de paráme-tros de referencia, sistemas de validación y lineamientos regulatorios que todavía no existen o son aún solo inci-pientes

We are in the midst of a true technological revolution in healthcare. Processes based upon artificial intelligence and machine learning are progressively touching all disciplinary areas, and its implementation in the field of infectious diseases is astonishing, accelerated by the COVID-19 pandemic. Today we have tools that can not only assist or carry on decision-making processes based upon guidelines or algorithms, but also modify its performance from the previously completed tasks. From optimization of the identification of resistant pathogens, selection of candidates for participating in clinical trials, the search of new antimicrobial therapeutic agents, the development of new vaccines, the prediction of future epidemics and pandemics, the clinical follow up of patients suffering infectious diseases up to the resource allocation in the management of an outbreak, are all current activities that can apply artificial intelligence in order to improve their final outcomes.This development has an exponential possibility of application, and is undoubtedly one of the main determinants that will shape medical activity in the future.Notwithstanding the maturation of this technology that is required for its definitive insertion in day-to-day healthcare activities, should be accompanied by definition of reference parameters, validation systems and regulatory guidelines that do not exist yet or are still in its initial stages

Big Data in the ICU: Experience in the Hospital Italiano de Buenos Aires.

Intensive care represents the critical care setting of a hospital, where fundamental, precise, and fast decisions have to be made. These decisions will affect the outcome of the patients in a matter of few hours. The knowledge of the therapeutic interventions applied in this setting is evolving, thus the perspective of Big Data may provide a new paradigm in the ICU. The conformation of a multidisciplinary team is essential to develop Big Data in the ICU.

Infrastructure for Big Data in the Intensive Care Unit.

The Big Data paradigm can be applied in intensive care unit, in order to improve the treatment of the patients, with the aim of customized decisions. This poster is about the infrastructure necessary to built a Big Data system for the ICU. Together with the infrastructure, the conformation of a multidisciplinary team is essential to develop Big Data to use in critical care medicine.

Aprendizaje automático aplicado en área de la salud: parte 2 / Machine Learning In Healthcare: part 2

En el artículo anterior se introdujo el tema y se desarrolló cómo es la recolección y análisis de datos, la selección y entrenamiento de modelos de aprendizaje automático supervisados y los métodos de validación interna que permiten corroborar si el modelo arroja resultados similares a los de otros conjuntos de entrenamiento y de prueba. En este artículo continuaremos con la descripción de la evaluación del rendimiento, la selección del modelo más adecuado para identificar la característica que se va a evaluar y la validación externa del modelo. Además, el artículo resume los desafíos existentes en la implementación del Machine Learning desde la investigación al uso clínico. (AU)

In the previous article, we introduced topics such as data collection and analysis, selection and training of supervised machine learning models and methods of internal validation that allow to corroborate whether the model yields similar results to other training and test sets.In this article, we will continue with the description of the performance evaluation, selecting the most appropriate model to identify the characteristic to evaluate and the external validation of the model. In addition, the article summarizes the actual challenges in the implementation of machine learning from research to clinical use. (AU)

User-Centered Design Improves the Usability of Drug-Drug Interaction Alerts: A Validation Study in the Real Scenario.

Decision support systems can alert physicians to the existence of drug interactions. The Hospital Italiano de Buenos Aires, Argentina, has an in-house electronic health record with computerized physician order entry and clinical decision support. It includes a drug-drug interaction alert system, initially developed under traditional engineering techniques. As we detected a high alert override rate, we rebuilt the knowledge database and redesigned the alert interface with User-Centered Design techniques. A laboratory crossover study using clinical vignettes showed that new alerts were more usable than traditional ones.This paper aimed to validate these results through a controlled and randomized experimental study with two branches (old vs. new design) in a real setting. We analyzed, quantitatively, every fired alert between April 2015 and September 2016. Finally, we performed user surveys and qualitative interviews to inquire about their satisfaction and perceptions.In real scenarios, user-centered design alerts were more usable, being more effective and satisfactory, but less efficient than traditional alerts. "Safe omission", as a new concept, emerged from our stratified analyses and interviews.

Impact of Participatory Design for Drug-Drug Interaction Alerts. A Comparison Study Between Two Interfaces.

Decision support systems for alert drug-drug interactions have been shown as valid strategy to reduce medical error. Even so the use of these systems has not been as expected, probably due to the lack of a suitable design. This study compares two interfaces, one of them developed using participatory design techniques (based on user centered design processes). This work showed that the use of these techniques improves satisfaction, effectiveness and efficiency in an alert system for drug-drug interactions, a fact that was evident in specific situations such as the decrease of errors to meet the specified task, the time, the workload optimization and users overall satisfaction with the system.

Aprendizaje automático aplicado en área de la salud: parte 1 / Machine learning in healthcare: part 1

Este será el primero de dos artículos donde se tratarán los pasos necesarios para desarrollar un proyecto de aplicación de técnicas de Machine Learning en Salud, que introduce nociones sobre la recolección y análisis de datos, la selección y entrenamiento de modelos de aprendizaje auto-mático de tipo supervisado y los métodos de validación interna para cada modelo. (AU)

This will be the first of two articles where the steps needed to apply machine learning methods in healthcare will be discussed. It will introduce fundamental notions about data collection, selection and training of supervised ML models as well as the methods of internal validation. In a second article, we will discuss about the performance evaluation to select the most appropriate model and its external validation. (AU)

Design and development of an early warning score for COVID-19 hospitalized patients / Diseño y desarrollo de un sistema de alerta temprana para pacientes hospitalizados por COVID-19

Abstract Pandemics pose a major challenge for public health preparedness, requiring a coordinated international response and the development of solid containment plans. Early and accurate identifica tion of high-risk patients in the course of the current COVID-19 pandemic is vital for planning and making proper use of available resources. The purpose of this study was to identify the key variables that account for worse outcomes to create a predictive model that could be used effectively for triage. Through literature review, 44 variables that could be linked to an unfavorable course of COVID-19 disease were obtained, including clinical, laboratory, and X-ray variables. These were used for a 2-round modified Delphi processing with 14 experts to select a final list of variables with the greatest predictive power for the construction of a scoring system, leading to the creation of a new scoring system: the COVID-19 Severity Index. The analysis of the area under the curve for the COVID-19 Severity Index was 0.94 to predict the need for ICU admission in the following 24 hours against 0.80 for NEWS-2. Additionally, the digital medical record of the Hospital Italiano de Buenos Aires was electronically set for an automatic calculation and constant update of the COVID-19 Severity Index. Specifically designed for the current COVID-19 pandemic, COVID-19 Severity Index could be used as a reliable tool for strategic planning, organization, and administration of resources by easily identifying hospitalized patients with a greater need of intensive care.

Resumen La pandemia por COVID-19 planteó un desafío para el sistema salud, debido a la gran demanda de pacientes hospitalizados. La identificación temprana de pacientes hospitalizados con riesgo de evo lución desfavorable es vital para asistir en forma oportuna y planificar la demanda de recursos. El propósito de este estudio fue identificar las variables predictivas de mala evolución en pacientes hospitalizados por COVID-19 y crear un modelo predictivo que pueda usarse como herramienta de triage. A través de una revisión narrativa, se obtuvieron 44 variables vinculadas a una evolución desfavorable de la enfermedad COVID-19, incluyendo variables clínicas, de laboratorio y radiográficas. Luego se utilizó un procesamiento por método Delphi modificado de 2 rondas para seleccionar una lista final de variables incluidas en el score llamado COVID-19 Severity Index. Luego se calculó el Área Bajo la Curva (AUC) del score para predecir el pase a terapia intensiva en las próximas 24 horas. El score presentó un AUC de 0,94 frente a 0,80 para NEWS-2. Finalmente se agregó el COVID-19 Severity Index a la historia clínica electrónica de un hospital universitario de alta complejidad. Se programó para que el mismo se actualice de manera automática, facilitando la planificación estratégica, organización y administración de recursos a través de la identificación temprana de pacientes hospitalizados con mayor riesgo de transferencia a la Unidad de Cuidados Intensivos.

Participatory design for drug-drug interaction alerts.

The utilization of decision support systems, in the point of care, to alert drug-drug interactions has been shown to improve quality of care. Still, the use of these systems has not been as expected, it is believed, because of the difficulties in their knowledge databases; errors in the generation of the alerts and the lack of a suitable design. This study expands on the development of alerts using participatory design techniques based on user centered design process. This work was undertaken in three stages (inquiry, participatory design and usability testing) it showed that the use of these techniques improves satisfaction, effectiveness and efficiency in an alert system for drug-drug interactions, a fact that was evident in specific situations such as the decrease of errors to meet the specified task, the time, the workload optimization and users overall satisfaction in the system.