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%

Timing of implant-removal in late acute periprosthetic joint infection:A multicenter observational study

Objectives: We evaluated the treatment outcome in late acute (LA) periprosthetic joint infections (PJI) treated with debridement and implant retention (DAIR) versus implant removal. Methods: In a large multicenter study, LA PJIs of the hip and knee were retrospectively evaluated. Failure was defined as: PJI related death, prosthesis removal or the need for suppressive antibiotic therapy. LA PJI was defined as acute symptoms Results: 445 patients were included, comprising 340 cases treated with DAIR and 105 cases treated with implant removal (19% one-stage revision (n = 20), 74.3% two-stage revision (n = 78) and 6.7% definitive implant removal (n = 7). Overall failure in patients treated with DAIR was 45.0% (153/340) compared to 24.8% (26/105) for implant removal (p = 3 (OR 2.9), PJI caused by S. aureus (OR 1.8) and implant retention (OR 3.1) were independent predictors for failure in the multivariate analysis. Conclusion: DAIR is a viable surgical treatment for most patients with LA PJI, but implant removal should be considered in a subset of patients, especially in those with a CRIME80-score >= 3. (C) 2019 The British Infection Association. Published by Elsevier Ltd. All rights reserved

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