Modelo matemático optimizado para la predicción y planificación de la asistencia sanitaria por la COVID-19 / Mathematical model optimized for prediction and health care planning for COVID-19

Objetivo: La pandemia de la COVID-19 ha supuesto una amenaza de colapso de los servicios hospitalarios y de unidades de cuidado intensivo (UCI), así como una reducción de la dinámica asistencial de pacientes afectados por otras patologías. El objetivo fue desarrollar un modelo matemático diseñado para optimizar las predicciones relacionadas con las necesidades de hospitalización e ingresos en UCI por la COVID-19. Diseño: Estudio prospectivo. Ámbito: Provincia de Granada (España). Pacientes: Pacientes de COVID-19 hospitalizados, ingresados en UCI, recuperados y fallecidos desde el 15 de marzo hasta el 22 de septiembre del 2020. Intervenciones: Desarrollo de un modelo matemático tipo susceptible, expuesto, infectado y recuperado (SEIR) capaz de predecir la evolución de la pandemia, considerando las medidas de salud pública establecidas. Variables de interés: Número de pacientes infectados por SARS-CoV-2, hospitalizados e ingresados en UCI por la COVID-19.Resultados: A partir de los datos registrados, hemos podido desarrollar un modelo matemático que refleja el flujo de la población entre los diferentes grupos de interés en relación con la COVID-19. Esta herramienta permite analizar diferentes escenarios basados en medidas de restricción socio-sanitarias y pronosticar el número de infectados, hospitalizados e ingresados en UCI. Conclusiones: El modelo matemático es capaz de proporcionar predicciones sobre la evolución de la COVID-19 con suficiente antelación como para poder conjugar los picos de prevalencia y de necesidades de asistencia hospitalaria y de UCI, con la aparición de ventanas temporales que posibiliten la atención de enfermos no-COVID (AU)

Objective: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. Design: Prospective study. Setting: Province of Granada (Spain). Population: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. Study variables: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. Results: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. Conclusions:The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified (AU)

Mathematical model optimized for prediction and health care planning for COVID-19.

OBJECTIVE: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. DESIGN: Prospective study. SETTING: Province of Granada (Spain). POPULATION: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. STUDY VARIABLES: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. RESULTS: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. CONCLUSIONS: The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified.

Mathematical model optimized for prediction and health care planning for COVID-19. / Modelo matemático optimizado para la predicción y planificación de la asistencia sanitaria por la COVID-19.

OBJECTIVE: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. DESIGN: Prospective study. SETTING: Province of Granada (Spain). POPULATION: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. STUDY VARIABLES: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. RESULTS: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. CONCLUSIONS: The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified.

[Respiratory infections caused by Aspergillus spp. in critically ill patients admitted to the intensive care units]. / Infecciones respiratorias por Aspergillus spp. en pacientes críticos ingresados en unidades de cuidados intensivos.

UNLABELLED: The presence of respiratory fungal infection in the critically ill patient is associated with high morbidity and mortality. OBJECTIVES: To assess the incidence of respiratory infection caused by Aspergillus spp. independently of the origin of infection in patients admitted to Spanish ICUs, as well as to describe the rates, characteristics, outcomes and prognostic factors in patients with this type of infection. MATERIAL AND METHODS: An observational, retrospective, open-label and multicenter study was carried out in a cohort of patients with respiratory infection caused by Aspergillus spp. admitted to Spanish ICUs between 2006 and 2012 (months of April, May and June), and included in the ENVIN-HELICS registry (108,244 patients and 825,797 days of ICU stay). Variables independently related to in-hospital mortality were identified by multiple logistic regression analysis. RESULTS: A total of 267 patients from 79 of the 198 participating ICUs were included (2.46 cases per 1000 ICU patients and 3.23 episodes per 10,000 days of ICU stay). From a clinical point of view, infections were classified as ventilator-associated pneumonia in 93 cases (34.8%), pneumonia unrelated to mechanical ventilation in 120 cases (44.9%), and tracheobronchitis in 54 cases (20.2%). The study population included older patients (mean 64.8±17.1 years), with a high severity level (APACHE II score 22.03±7.7), clinical diseases (64.8%) and prolonged hospital stay before the identification of Aspergillus spp. (median 11 days), transferred to the ICU mainly from hospital wards (58.1%) and with high ICU (57.3%) and hospital (59.6%) mortality rates, exhibiting important differences depending on the type of infection involved. Independent mortality risk factors were previous admission to a hospital ward (OR=7.08, 95%CI: 3.18-15.76), a history of immunosuppression (OR=2.52, 95%CI: 1.24-5.13) and severe sepsis or septic shock (OR=8.91, 95%CI: 4.24-18.76). CONCLUSIONS: Respiratory infections caused by Aspergillus spp. in critically ill patients admitted to the ICU in Spain are infrequent, and affect a very selected group of patients, characterized by high mortality and conditioned by non-modifiable risk factors.

Variabilidad en el manejo del infarto agudo de miocardio en el registro ARIAM en el año 2002 / Variability in the management of acute myocardial infarction in the ARIAM registry in the year 2002

Objetivo. Analizar las diferencias en el manejo del infarto agudo de miocardio (IAM) entre las distintas comunidades autónomas de los hospitales participantes en el registro ARIAM durante el año 2002. Diseño. Registro multicéntrico nacional de base hospitalaria de pacientes ingresados en Unidades de Cuidados Intensivos Cardiológicos (UCIC) por sospecha de síndrome coronario agudo. Ámbito. UCIC de 80 hospitales españoles de 14 comunidades autónomas. Pacientes o participantes. Se incluyen todos los pacientes del registro ARIAM del año 2002 cuyo motivo de ingreso hospitalario es IAM de menos de 24 horas de evolución. Intervenciones. Ninguna Variables de interés principales. Se registraron variables demográficas, antecedentes, clínicas, retrasos y lugar de actuación inicial, uso de técnicas diagnósticas y terapéuticas, tiempos de estancia y morbimortalidad. Se agruparon los casos por áreas geográficas que corresponden a las diferentes comunidades autónomas de España. Se analizaron las diferencias mediante el uso del coeficiente de variación (CV). Resultados. Se incluyeron 6.820 pacientes. Las características basales fueron similares, salvo para la presencia de diabetes como factor de riesgo (CV: 21%). Los CV excesivos correspondieron al modo de acceso al sistema sanitario (061: 29%), lugar de realización de la fibrinolisis (extrahospitalaria: 155%, urgencias: 120%), tipo de fibrinolítico (rTPA: 78%), retrasos intrahospitalarios (puerta-aguja: 24% y puerta-balón: 39%), uso de angioplastia coronaria transluminal percutánea (ACTP) primaria (122%), realización de coronariografía (75%) y tratamiento con anti-IIb/IIIa (34%). La mortalidad en UCIC global fue del 8,0%, con un CV de 16%. Conclusiones. Existen diferencias en el manejo del IAM entre las distintas comunidades autónomas estudiadas, especialmente en lo concerniente a la atención prehospitalaria y el uso de las técnicas de revascularización. Sin embargo, en la población estudiada, no se traducen en diferencias significativas respecto al resultado a corto plazo

Objective. Analyze the differences in the management of acute myocardial infarction (AMI) between the different regional communities of the hospitals participating in the ARIAM registry during the year 2002. Design. Hospital based multicenter, national registry of patients admitted to cardiology intensive care units (CICU) due to suspicion of acute coronary syndrome. Scope. CICU of 80 Spanish hospitals in 14 regional communities. Patients or participants. All the patients from the ARIAM registry in the 2002 whose cause of hospitalization is AMI of less than 24 hours evolution are included. Interventions. None. Variables of principal interest. Demographic variable, background, symptoms, delays and site of initial action, use of diagnostic and therapeutic techniques, stay time and morbidity-mortality were recorded. The cases were grouped by geographic areas that correspond to the different regional communities of Spain. Differences were analyzed with the variation coefficient (VC).Results. A total of 6,820 patients were included. Basal characteristics were similar, except for the presence of diabetes as risk factor (VC: 21%). Excessive variation coefficients corresponded to way of access to health care system (061: 29%), site fibrinolysis was done (community: 155%, emergency service: 120%), fibrinolytic type (rTPA: 78%), interhospital delays (door-to-needle: 24% and door-to-balloon: 39%), use of primary percutaneous transluminal coronary angioplasty (PTCA) (122%), conduction of coronariography (75%) and treatment with anti-IIb/IIIa (34%). Global mortality in the CICU was 8.0%, with a 16% VC. Conclusions. There are differences in the management of AMI between the different regional communities studied, especially in that regarding prehospital care and the use of revascularization techniques. However, no significant differences are found in the study population regarding short term result

Abdomen release in prone position does not improve oxygenation in an experimental model of acute lung injury.

OBJECTIVE: To analyze the effect of abdomen release in the prone position on oxygenation in an experimental model of acute lung injury. DESIGN: Experimental randomized controlled study. SETTING: Experimental laboratory of a tertiary university hospital. PARTICIPANTS: Mixed-breed adolescent pigs weighing between 25-31 kg. INTERVENTIONS: Thirty minutes after pulmonary edema was produced with oleic acid, the animals were turned prone and randomized into two groups: group I or control (n = 9), lying directly on the operating table; and group II (n = 11) with abdomen release, with positioning rolls under the upper part of the chest wall and the pelvis to allow free movement of the abdomen. MEASUREMENTS AND RESULTS: The gas exchange, respiratory mechanics, hemodynamics, intra-abdominal pressure (IAP) and the extravascular lung water (EVLW), determined by double indicator dilution method (DI), were recorded at baseline (time 0) and at 30, 60, 90, 120 and 150 min. The PaO2/FIO2 increased in both groups at 30 min after the pigs were placed in the prone position (time 60) and then decreased progressively until the end of the experimental period, with no statistical differences between the groups at any time (73.1 +/- 14.5 vs 79.5 +/- 14.9 at 150 min). Abdomen release was not associated with changes in the respiratory mechanics, EVLW or intra-abdominal pressure. CONCLUSIONS: Abdomen release in prone position does not improve oxygenation in an experimental model of acute lung injury.