Tacrolimus(Tac)is currently the most common calcineurin-inhibitor employed in solid organ transplantation.High intra-patient variability(IPV)of Tac(Tac IPV)has been associated with an increased risk of immune-mediated...Tacrolimus(Tac)is currently the most common calcineurin-inhibitor employed in solid organ transplantation.High intra-patient variability(IPV)of Tac(Tac IPV)has been associated with an increased risk of immune-mediated rejection and poor outcomes after kidney transplantation.Few data are available concerning the impact of high Tac IPV in non-kidney transplants.However,even in kidney transplantation,there is still a controversy whether high Tac IPV is indeed detrimental in respect to graft and/or patient survival.This may be due to different methods employed to evaluate IPV and distinct time frames adopted to assess graft and patient survival in those reports published up to now in the literature.Little is also known about the influence of high Tac IPV in the development of other untoward adverse events,update of the current knowledge regarding the impact of Tac IPV in different outcomes following kidney,liver,heart,lung,and pancreas tran-splantation to better evaluate its use in clinical practice.展开更多
Severe cardiovascular diseases can rapidly lead to death.At present,most studies in the deep learning field using electrocardiogram(ECG)are performed on intra-patient experiments for the classification of coronary art...Severe cardiovascular diseases can rapidly lead to death.At present,most studies in the deep learning field using electrocardiogram(ECG)are performed on intra-patient experiments for the classification of coronary artery disease(CAD),myocardial infarction,and congestive heart failure(CHF).By contrast,actual conditions are inter-patient experiments.In this study,we proposed a deep learning network,namely,CResFormer,with dual feature extraction to improve accuracy in classifying such diseases.First,fixed segmentation of dual-lead ECG signals without preprocessing was used as input data.Second,one-dimensional convolutional layers performed moderate dimensionality reduction to accommodate subsequent feature extraction.Then,ResNet residual network block layers and transformer encoder layers sequentially performed feature extraction to obtain key associated abstract features.Finally,the Softmax function was used for classifications.Notably,the focal loss function is used when dealing with unbalanced datasets.The average accuracy,sensitivity,positive predictive value,and specificity of four classifications of severe cardiovascular diseases are 99.84%,99.68%,99.71%,and 99.90%in intra-patient experiments,respectively,and 97.48%,93.54%,96.30%,and 97.89%in inter-patient experiments,respectively.In addition,the model performs well in unbalanced datasets and shows good noise robustness.Therefore,the model has great application potential in diagnosing CAD,MI,and CHF in the actual clinical environment.展开更多
目的分析急诊科危重症患者院内转运不良事件风险因素,构建风险预测模型。方法采用方便抽样法选取2021年10月至2023年2月某院急诊科进行院内转运的870例危重症患者的临床资料,采用单因素和多因素Logistic回归分析建立风险预测模型,以受...目的分析急诊科危重症患者院内转运不良事件风险因素,构建风险预测模型。方法采用方便抽样法选取2021年10月至2023年2月某院急诊科进行院内转运的870例危重症患者的临床资料,采用单因素和多因素Logistic回归分析建立风险预测模型,以受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under curve,AUC)评价模型预测效果。结果英国国家早期预警评分(national early warning score,NEWS)、血氧饱和度、急诊B超、血管活性药物、机械通气是急诊科危重症患者发生病情不良事件的独立风险因素;血氧饱和度、携氧装置、Ⅲ类管路、护工参与转运是技术不良事件的独立风险因素(均P<0.05)。AUC分别为0.813,0.756。结论构建的急诊科危重症患者院内转运不良事件风险预测模型具有一定的参考价值。展开更多
探究基于“-TT”结构经监护仪腹内压监测法降低重症患者喂养不耐受发生率的效果。选取2022年8月—2023年8月四川省自贡市第四人民医院抢救监护室(emergency intensive care unit,EICU)60例重症需行肠内营养(enteral nutrition,EN)支持...探究基于“-TT”结构经监护仪腹内压监测法降低重症患者喂养不耐受发生率的效果。选取2022年8月—2023年8月四川省自贡市第四人民医院抢救监护室(emergency intensive care unit,EICU)60例重症需行肠内营养(enteral nutrition,EN)支持的患者作为研究对象,采用随机数字表法将患者分为参照组和试验组,每组各30例。参照组实施常规EN管理,试验组在参照组基础上实施基于“-TT”结构经监护仪腹内压监测法,对比两组患者的喂养不耐受发生率。结果显示,与参照组相比,试验组喂养不耐受发生率较低(P<0.05);试验组达到目标喂养量时间较短(P<0.05);试验组EICU停留时间较短(P<0.05)。研究发现,于EICU重症需行EN支持患者的管理中,基于“-TT”结构经监护仪腹内压监测法具有一定的临床应用价值,通过对患者腹压变化的实时监测,可以及时调整喂养方案,降低其喂养不耐受发生率,缩短患者达到目标喂养量的时间,改善患者预后,值得借鉴。展开更多
文摘Tacrolimus(Tac)is currently the most common calcineurin-inhibitor employed in solid organ transplantation.High intra-patient variability(IPV)of Tac(Tac IPV)has been associated with an increased risk of immune-mediated rejection and poor outcomes after kidney transplantation.Few data are available concerning the impact of high Tac IPV in non-kidney transplants.However,even in kidney transplantation,there is still a controversy whether high Tac IPV is indeed detrimental in respect to graft and/or patient survival.This may be due to different methods employed to evaluate IPV and distinct time frames adopted to assess graft and patient survival in those reports published up to now in the literature.Little is also known about the influence of high Tac IPV in the development of other untoward adverse events,update of the current knowledge regarding the impact of Tac IPV in different outcomes following kidney,liver,heart,lung,and pancreas tran-splantation to better evaluate its use in clinical practice.
基金This paper was supported by the National Major Scientific Research Instrument Development Project(No.62027819)the General Project of National Natural Science Foundation of China(No.62076177)Shanxi Province Key Technology and Generic Technology R&D Project(No.2020XXX007).
文摘Severe cardiovascular diseases can rapidly lead to death.At present,most studies in the deep learning field using electrocardiogram(ECG)are performed on intra-patient experiments for the classification of coronary artery disease(CAD),myocardial infarction,and congestive heart failure(CHF).By contrast,actual conditions are inter-patient experiments.In this study,we proposed a deep learning network,namely,CResFormer,with dual feature extraction to improve accuracy in classifying such diseases.First,fixed segmentation of dual-lead ECG signals without preprocessing was used as input data.Second,one-dimensional convolutional layers performed moderate dimensionality reduction to accommodate subsequent feature extraction.Then,ResNet residual network block layers and transformer encoder layers sequentially performed feature extraction to obtain key associated abstract features.Finally,the Softmax function was used for classifications.Notably,the focal loss function is used when dealing with unbalanced datasets.The average accuracy,sensitivity,positive predictive value,and specificity of four classifications of severe cardiovascular diseases are 99.84%,99.68%,99.71%,and 99.90%in intra-patient experiments,respectively,and 97.48%,93.54%,96.30%,and 97.89%in inter-patient experiments,respectively.In addition,the model performs well in unbalanced datasets and shows good noise robustness.Therefore,the model has great application potential in diagnosing CAD,MI,and CHF in the actual clinical environment.
文摘目的分析急诊科危重症患者院内转运不良事件风险因素,构建风险预测模型。方法采用方便抽样法选取2021年10月至2023年2月某院急诊科进行院内转运的870例危重症患者的临床资料,采用单因素和多因素Logistic回归分析建立风险预测模型,以受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under curve,AUC)评价模型预测效果。结果英国国家早期预警评分(national early warning score,NEWS)、血氧饱和度、急诊B超、血管活性药物、机械通气是急诊科危重症患者发生病情不良事件的独立风险因素;血氧饱和度、携氧装置、Ⅲ类管路、护工参与转运是技术不良事件的独立风险因素(均P<0.05)。AUC分别为0.813,0.756。结论构建的急诊科危重症患者院内转运不良事件风险预测模型具有一定的参考价值。
文摘探究基于“-TT”结构经监护仪腹内压监测法降低重症患者喂养不耐受发生率的效果。选取2022年8月—2023年8月四川省自贡市第四人民医院抢救监护室(emergency intensive care unit,EICU)60例重症需行肠内营养(enteral nutrition,EN)支持的患者作为研究对象,采用随机数字表法将患者分为参照组和试验组,每组各30例。参照组实施常规EN管理,试验组在参照组基础上实施基于“-TT”结构经监护仪腹内压监测法,对比两组患者的喂养不耐受发生率。结果显示,与参照组相比,试验组喂养不耐受发生率较低(P<0.05);试验组达到目标喂养量时间较短(P<0.05);试验组EICU停留时间较短(P<0.05)。研究发现,于EICU重症需行EN支持患者的管理中,基于“-TT”结构经监护仪腹内压监测法具有一定的临床应用价值,通过对患者腹压变化的实时监测,可以及时调整喂养方案,降低其喂养不耐受发生率,缩短患者达到目标喂养量的时间,改善患者预后,值得借鉴。
