Diseño y desarrollo de un sistema de alerta temprana para pacientes hospitalizados por COVID-19

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.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.Fil: Huespe, Ivan. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; ArgentinaFil: Carboni Bisso, Indalecio. Hospital Italiano; ArgentinaFil: Gemelli, Nicolas A.. Hospital Italiano; ArgentinaFil: Terrasa, Sergio Adrian. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Di Stefano, Sabrina. Hospital Italiano; ArgentinaFil: Burgos, Valeria Laura. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; ArgentinaFil: Sinner, Jorge. Hospital Italiano; ArgentinaFil: Oubiña, Mailen. Hospital Italiano; ArgentinaFil: Bezzati, Marina. Hospital Italiano; ArgentinaFil: Delgado, Pablo. Hospital Italiano; ArgentinaFil: Las Heras, Marcos. Hospital Italiano; ArgentinaFil: Risk, Marcelo. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; Argentin

Diseño y desarrollo de un sistema de alerta temprana para pacientes hospitalizados por COVID-19

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.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.Fil: Huespe, Ivan. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; ArgentinaFil: Carboni Bisso, Indalecio. Hospital Italiano; ArgentinaFil: Gemelli, Nicolas A.. Hospital Italiano; ArgentinaFil: Terrasa, Sergio Adrian. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Di Stefano, Sabrina. Hospital Italiano; ArgentinaFil: Burgos, Valeria Laura. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; ArgentinaFil: Sinner, Jorge. Hospital Italiano; ArgentinaFil: Oubiña, Mailen. Hospital Italiano; ArgentinaFil: Bezzati, Marina. Hospital Italiano; ArgentinaFil: Delgado, Pablo. Hospital Italiano; ArgentinaFil: Las Heras, Marcos. Hospital Italiano; ArgentinaFil: Risk, Marcelo. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; Argentin

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Autistic-like behaviours and hyperactivity in mice lacking ProSAP1/Shank2.

International audienceAutism spectrum disorders comprise a range of neurodevelopmental disorders characterized by deficits in social interaction and communication, and by repetitive behaviour. Mutations in synaptic proteins such as neuroligins, neurexins, GKAPs/SAPAPs and ProSAPs/Shanks were identified in patients with autism spectrum disorder, but the causative mechanisms remain largely unknown. ProSAPs/Shanks build large homo- and heteromeric protein complexes at excitatory synapses and organize the complex protein machinery of the postsynaptic density in a laminar fashion. Here we demonstrate that genetic deletion of ProSAP1/Shank2 results in an early, brain-region-specific upregulation of ionotropic glutamate receptors at the synapse and increased levels of ProSAP2/Shank3. Moreover, ProSAP1/Shank2(-/-) mutants exhibit fewer dendritic spines and show reduced basal synaptic transmission, a reduced frequency of miniature excitatory postsynaptic currents and enhanced N-methyl-d-aspartate receptor-mediated excitatory currents at the physiological level. Mutants are extremely hyperactive and display profound autistic-like behavioural alterations including repetitive grooming as well as abnormalities in vocal and social behaviours. By comparing the data on ProSAP1/Shank2(-/-) mutants with ProSAP2/Shank3αβ(-/-) mice, we show that different abnormalities in synaptic glutamate receptor expression can cause alterations in social interactions and communication. Accordingly, we propose that appropriate therapies for autism spectrum disorders are to be carefully matched to the underlying synaptopathic phenotype

Next-generation ARIA care pathways for rhinitis and asthma: a model for multimorbid chronic diseases

Abstract Background In all societies, the burden and cost of allergic and chronic respiratory diseases are increasing rapidly. Most economies are struggling to deliver modern health care effectively. There is a need to support the transformation of the health care system into integrated care with organizational health literacy. Main body As an example for chronic disease care, MASK (Mobile Airways Sentinel NetworK), a new project of the ARIA (Allergic Rhinitis and its Impact on Asthma) initiative, and POLLAR (Impact of Air POLLution on Asthma and Rhinitis, EIT Health), in collaboration with professional and patient organizations in the field of allergy and airway diseases, are proposing real-life ICPs centred around the patient with rhinitis, and using mHealth to monitor environmental exposure. Three aspects of care pathways are being developed: (i) Patient participation, health literacy and self-care through technology-assisted “patient activation”, (ii) Implementation of care pathways by pharmacists and (iii) Next-generation guidelines assessing the recommendations of GRADE guidelines in rhinitis and asthma using real-world evidence (RWE) obtained through mobile technology. The EU and global political agendas are of great importance in supporting the digital transformation of health and care, and MASK has been recognized by DG Santé as a Good Practice in the field of digitally-enabled, integrated, person-centred care. Conclusion In 20 years, ARIA has considerably evolved from the first multimorbidity guideline in respiratory diseases to the digital transformation of health and care with a strong political involvement

Next-generation ARIA care pathways for rhinitis and asthma: a model for multimorbid chronic diseases

Abstract Background In all societies, the burden and cost of allergic and chronic respiratory diseases are increasing rapidly. Most economies are struggling to deliver modern health care effectively. There is a need to support the transformation of the health care system into integrated care with organizational health literacy. Main body As an example for chronic disease care, MASK (Mobile Airways Sentinel NetworK), a new project of the ARIA (Allergic Rhinitis and its Impact on Asthma) initiative, and POLLAR (Impact of Air POLLution on Asthma and Rhinitis, EIT Health), in collaboration with professional and patient organizations in the field of allergy and airway diseases, are proposing real-life ICPs centred around the patient with rhinitis, and using mHealth to monitor environmental exposure. Three aspects of care pathways are being developed: (i) Patient participation, health literacy and self-care through technology-assisted “patient activation”, (ii) Implementation of care pathways by pharmacists and (iii) Next-generation guidelines assessing the recommendations of GRADE guidelines in rhinitis and asthma using real-world evidence (RWE) obtained through mobile technology. The EU and global political agendas are of great importance in supporting the digital transformation of health and care, and MASK has been recognized by DG Santé as a Good Practice in the field of digitally-enabled, integrated, person-centred care. Conclusion In 20 years, ARIA has considerably evolved from the first multimorbidity guideline in respiratory diseases to the digital transformation of health and care with a strong political involvement