La hora de inicio de la cirugía como factor de riesgo para la infección de prótesis articular.: resultados de un estudio descriptivo.

La infección de prótesis articular (IPA) es una complicación relacionada con múltiples factores de riesgo. Nos proponemos analizar si la hora a la que se realiza una artroplastia puede ser un factor de riesgo para desarrollar una IPA. Material y método. Estudio observacional retrospectivo de una serie de pacientes que se sometieron a una cirugía de artroplastia de cadera o rodilla en el año 2010 en el Hospital Príncipe de Asturias de Alcalá de Henares (Madrid). Resultados. Durante el período de estudio se analizaron 362 cirugías de artroplastia de rodilla y cadera, 19 de las cuales desarrollaron IPA (incidencia 5,2%). Mediante análisis de regresión logística se observó un incremento estadísticamente significativo de la incidencia de IPA en las cirugías realizadas entre las 12 y las 14 horas (Odds Ratio [OR] 3,4; intervalo de confianza del 95% [IC 95%] 1,1 a 11,3, p=0,04) y menor en las realizadas entre las 8 y las 10 de la mañana (OR 0,2; IC 95% 0,04 a 0,91; p= 0,04). Conclusión. En nuestro estudio, los pacientes intervenidos al final de la mañana tuvieron un riesgo tres veces superior de desarrollar IPA, mientras que operarse a primera hora fue un factor protector.The prosthetic joint infection (PJI) is a complication with multiple risk factors described. We pro - pose to analyze if the hour of start time of surgery may be a risk for developing PJI. Materials and methods. We retrospectively analyze known risk factors in patients who underwent implantation of knee or hip arthroplasty in Principe de Asturias Hospital from January 2010 to December 2010 and the time of performance of the sur - gery. Results. During the study period 362 surgeries were analyzed, of which 19 developed PJI (incidence 5,2%). Logistic regression analysis showed more frequency of PJI incidence in surgeries started between 12 and 14pm (odds ratio [OR] 3,4; confidence interval [CI] 95% 1,1 to 11,3, p=0,04) and less frequent between 8 and 10 am (OR 0,2, CI 95% 0,04 to 0,91, p=0,04). Conclusion. In our study the patients undergoing surgery at the end of the morning had a threefold increased risk of developing PJI, while early surgery was a protective facto

Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence

Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19's effects on patients' lung health.Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU).Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians' diagnosis, and test for improvements on physicians' performance when using the prediction algorithm.Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%

Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence

Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19’s effects on patients’ lung health.Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU).Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians’ diagnosis, and test for improvements on physicians’ performance when using the prediction algorithm.Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%

Resultados asistenciales y económicos en los pacientes “periféricos” de medicina interna

Background and objective: Hospital management system assigns consecutive beds, in one or more inpatient units, to different medical or surgical services. This distribution allows a stable and spatially circumscribed organization, but facilitates the presence of “peripheral” patients. It was studied whether peripheral Internal Medicine inpatients had worse health care and economic outcomes than those admitted to their own beds. Materials and methods: Retrospective observational study involving patients admitted from January 1 to March 15 and discharged before 1 April 2015. Hospital readmissions, length of stay and in-hospital mortality of inpatients of Internal Medicine Service wards were compared against the peripheral ones. Used for statistical adjustement: age, sex, number of diagnoses at discharge and Charlson comorbidity index. Results: The study included 1045 patients, 27.8% (95% CI 25.1 to 30.6) peripherals. Mortality in both groups did not differ significantly (OR 0.8 [95% CI 0.5 to 1.3]), but they did readmissions (OR 1.6 [95% CI 1 to 2.5]; p = 0.037) and length of stay (1 [95% CI 0.1 to 1.8]; p = 0.034). This represented an increase of 51.2% in readmissions and 12.2% in length of stay. Excess of hospitalization days has led to an increase in spending € 157,703.3. Conclusions: Peripheral Internal Medicine patients have worse outcomes than inpatient care in their own beds, with an increase in associated costsFundamento y objetivo: El sistema tradicional de organización hospitalaria asigna un número de camas consecutivas, en una o varias unidades de hospitalización (plantas), a los diferentes servicios. Esta distribución permite una organización estable y circunscrita espacialmente, pero facilita la aparición de pacientes “periféricos”. Se analizó si los pacientes periféricos de Medicina Interna tienen peores resultados asistenciales y económicos que los ingresados en las camas propias del servicio. Material y método: Estudio observacional retrospectivo que incluyó a los pacientes ingresados del 1 de enero al 15 de marzo y dados de alta antes del 1 de abril de 2015. Se compararon la estancia y mortalidad hospitalaria y los reingresos de los pacientes hospitalizados en las camas del servicio frente a los de los periféricos. Para el ajuste estadístico se utilizaron la edad, sexo, número de diagnósticos al alta y el índice de comorbilidad de Charlson. Resultados: Participaron 1045 pacientes, el 27,8% (IC 95% 25,1 a 30,6) periféricos. La mortalidad en ambos grupos no difirió significativamente (OR 0,8 [IC 95% 0,5 a 1,3]), pero sí los reingresos (OR 1,6 [IC 95% 1 a 2,5]; p=0,037) y la estancia media (1 día [IC 95% 0,1 a 1,8]; p=0,034). Esto supuso un incremento del 51,2% en los reingresos y del 12,2% en la estancia. El exceso en la estancia originó un aumento del gasto en 157.703,3€. Conclusiones: Los pacientes periféricos de Medicina Interna tienen unos resultados asistenciales peores que los hospitalizados en las plantas propias del servicio, con un incremento asociado del gasto

Table1_Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence.docx

Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19’s effects on patients’ lung health.Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU).Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians’ diagnosis, and test for improvements on physicians’ performance when using the prediction algorithm.Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%.</p