RESUMEN
We aimed to develop a prognostic model for primary pontine hemorrhage (PPH) patients and validate the predictive value of the model for a good prognosis at 90 days. A total of 254 PPH patients were included for screening of the independent predictors of prognosis, and data were analyzed by univariate and multivariable logistic regression tests. The cases were then divided into training cohort (n=219) and validation cohort (n=35) based on the two centers. A nomogram was developed using independent predictors from the training cohort to predict the 90-day good outcome and was validated from the validation cohort. Glasgow Coma Scale score, normalized pixels (used to describe bleeding volume), and mechanical ventilation were significant predictors of a good outcome of PPH at 90 days in the training cohort (all P<0.05). The U test showed no statistical difference (P=0.892) between the training cohort and the validation cohort, suggesting the model fitted well. The new model showed good discrimination (area under the curve=0.833). The decision curve analysis of the nomogram of the training cohort indicated a great net benefit. The PPH nomogram comprising the Glasgow Coma Scale score, normalized pixels, and mechanical ventilation may facilitate predicting a 90-day good outcome.
Asunto(s)
Escala de Coma de Glasgow , Nomogramas , Humanos , Femenino , Masculino , Pronóstico , Persona de Mediana Edad , Adulto , Respiración Artificial , Puente , Valor Predictivo de las Pruebas , Anciano , Reproducibilidad de los Resultados , Hemorragias Intracraneales/diagnóstico , Estudios RetrospectivosRESUMEN
Abstract We aimed to develop a prognostic model for primary pontine hemorrhage (PPH) patients and validate the predictive value of the model for a good prognosis at 90 days. A total of 254 PPH patients were included for screening of the independent predictors of prognosis, and data were analyzed by univariate and multivariable logistic regression tests. The cases were then divided into training cohort (n=219) and validation cohort (n=35) based on the two centers. A nomogram was developed using independent predictors from the training cohort to predict the 90-day good outcome and was validated from the validation cohort. Glasgow Coma Scale score, normalized pixels (used to describe bleeding volume), and mechanical ventilation were significant predictors of a good outcome of PPH at 90 days in the training cohort (all P<0.05). The U test showed no statistical difference (P=0.892) between the training cohort and the validation cohort, suggesting the model fitted well. The new model showed good discrimination (area under the curve=0.833). The decision curve analysis of the nomogram of the training cohort indicated a great net benefit. The PPH nomogram comprising the Glasgow Coma Scale score, normalized pixels, and mechanical ventilation may facilitate predicting a 90-day good outcome.
RESUMEN
Antibiotic pollution causes serious environmental and social issues. China is the largest antibiotic producer and user in the world, with a large share of antibiotics used in agriculture. This study quantified agricultural antibiotic emissions of mainland China in 2014 as well as critical drivers in global supply chains. Results show that China's agriculture discharged 4131 tons of antibiotics. Critical domestic supply chain drivers are mainly located in Central China, North China, and East China. Foreign final demand contributes 9% of agricultural antibiotic emissions in mainland China and leads to 5-40% of emissions in each province. Foreign primary inputs (e.g., labor and capital) contribute 5% of agricultural antibiotic emissions in mainland China and lead to 2-63% of emissions in each province. Critical international drivers include the final demand of the United States and Japan for foods and textile products, as well as the primary inputs of the oil seeds sector in Brazil. The results indicate the uniqueness of supply chain drivers for antibiotic emissions compared with other emissions. Our findings reveal supply chain hotspots for multiple-perspective policy decisions to control China's agricultural antibiotic emissions as well as for international cooperation.