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您的检索式：作者名="Mateus Grellert"
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| 1 | Shock index and its variants as predictors of mortality in severe traumatic brain injury显示文摘BACKGROUND The increase in severe traumatic brain injury(sTBI)incidence is a worldwide phenomenon,resulting in a heavy disease burden in the public health systems,specifically in emerging countries.The shock index(SI)is a physiological parameter that indicates cardiovascular status and has been used as a tool to assess the presence and severity of shock,which is increased in sTBI.Considering the high mortality of sTBI,scrutinizing the predictive potential of SI and its variants is vital.AIM To describe the predictive potential of SI and its variants in sTBI.METHODS This study included 71 patients(61 men and 10 women)divided into two groups:Survival(S;n=49)and Non-survival(NS;n=22).The responses of blood pressure and heart rate(HR)were collected at admission and 48 h after admission.The SI,reverse SI(rSI),rSI multiplied by the Glasgow Coma Score(rSIG),and Age multiplied SI(AgeSI)were calculated.Group comparisons included Shapiro-Wilk tests,and independent samples t-tests.For predictive analysis,logistic regression,receiver operator curves(ROC)curves,and area under the curve(AUC)measurements were performed.RESULTS No significant differences between groups were identified for SI,rSI,or rSIG.The AgeSI was significantly higher in NS patients at 48 h following admission(S:26.32±14.2,and NS:37.27±17.8;P=0.016).Both the logistic regression and the AUC following ROC curve analysis showed that only AgeSI at 48 h was capable of predicting sTBI outcomes.CONCLUSION Although an altered balance between HR and blood pressure can provide insights into the adequacy of oxygen delivery to tissues and the overall cardiac function,only the AgeSI was a viable outcome-predictive tool in sTBI,warranting future research in different cohorts. | Randhall B Carteri Mateus Padilha Silvaine Sasso de Quadros Eder Kroeff Cardoso Mateus Grellert | 2024 | World Journal of Critical Care Medicine2024，13，1： | 0 |
| 2 | Machine learning approaches using blood biomarkers in nonalcoholic fatty liver diseases显示文摘The prevalence of nonalcoholic fatty liver disease(NAFLD)is an important public health concern.Early diagnosis of NAFLD and potential progression to nonalcoholic steatohepatitis(NASH),could reduce the further advance of the disease,and improve patient outcomes.Aiming to support patient diagnostic and predict specific outcomes,the interest in artificial intelligence(AI)methods in hepatology has dramatically increased,especially with the application of lessinvasive biomarkers.In this review,our objective was twofold:Firstly,we presented the most frequent blood biomarkers in NAFLD and NASH and secondly,we reviewed recent literature regarding the use of machine learning(ML)methods to predict NAFLD and NASH in large cohorts.Strikingly,these studies provide insights into ML application in NAFLD patients'prognostics and ranked blood biomarkers are able to provide a recognizable signature allowing cost-effective NAFLD prediction and also differentiating NASH patients.Future studies should consider the limitations in the current literature and expand the application of these algorithms in different populations,fortifying an already promising tool in medical science. | Randhall B Carteri Mateus Grellert Daniela Luisa Borba Claudio Augusto Marroni Sabrina Alves Fernandes | 2022 | Artificial Intelligence in Gastroenterology2022，3，3： | 0 |
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