Objective Identification of the risk factors for extraordinary hidden blood loss(HBL) could clarify the underlying causes and provide more appropriate management. This study aims to identify the predictors of HBL in s...Objective Identification of the risk factors for extraordinary hidden blood loss(HBL) could clarify the underlying causes and provide more appropriate management. This study aims to identify the predictors of HBL in spinal surgery.Methods Medical records were retrospectively retrieved to collect the data of patients who undergoing posterior thoracic and lumbar fusion surgery or scoliosis surgery. Demographic information, perioperative visible blood loss volume, as well as laboratory results were recorded. The patients receiving fusion surgery or scoliosis surgery were further divided into the HBL positive subgroup and the HBL negative subgroup. Differences in the variables between the groups were then analyzed. Binary logistic regression analysis was performed to determine independent risk factors associated with HBL.Results For patients undergoing posterior spinal surgery, the independent risk factors associated with HBL were autologous transfusion(for fusion surgery P = 0.011, OR: 2.627, 95%CI: 1.574-2.782; for scoliosis surgery P < 0.001, OR: 2.268, 95%CI: 2.143-2.504) and allogeneic transfusion(for fusion surgery P < 0.001, OR: 6.487, 95%CI: 2.349-17.915; for scoliosis surgery P < 0.001, OR: 3.636, 95%CI: 2.389-5.231).Conclusion Intraoperative blood transfusion might be an early-warning indicator for perioperative HBL.展开更多
We read with interest the recent systematic reviewaArtificial intelligence and machine learning for hemorrhagic trauma careoby Peng et al.[1],which evaluated literature on machine learning(ML)in the management of trau...We read with interest the recent systematic reviewaArtificial intelligence and machine learning for hemorrhagic trauma careoby Peng et al.[1],which evaluated literature on machine learning(ML)in the management of traumatic haemorrhage.We thank the authors for their contribution to the role of ML in trauma.展开更多
目的建立应用血栓弹力图(TEG)及凝血指标综合评估上消化道出血患者凝血功能的输血结局预测模型。方法从输血(科)管理系统和医院信息系统(HIS)系统收集浙江省人民医院及其淳安分院消化内科2018年6月~2021年6月收治的101名符合临床诊断标...目的建立应用血栓弹力图(TEG)及凝血指标综合评估上消化道出血患者凝血功能的输血结局预测模型。方法从输血(科)管理系统和医院信息系统(HIS)系统收集浙江省人民医院及其淳安分院消化内科2018年6月~2021年6月收治的101名符合临床诊断标准的上消化道出血患者,根据结局是否输血分为输血组(n=56)和未输血组(n=45),以及按照肝硬化与否分为肝硬化组(n=74)和非肝硬化组(n=27),同时收集40名非上消化道出血患者的阴性对照组。对比各组的TEG检测R、K、α、MA参数,凝血功能检测PT、INR、APTT、TT、Fib,血常规检测Hb、Plt、WBC、NEUT%,以及生化检测Alb、SCr、ALT、AST、GGT等指标(值);分析TEG指标与传统凝血功能指标的相关性;采用单因素和多因素分析,筛选输血相关因素建立预测模型。结果输血组与未输血组比较:TEG的K(min)为3.86±3.12 vs 2.50±1.47,α(°)为54.00±14.08 vs 61.05±10.88,MA(mm)为51.12±13.37 vs 58.26±11.08(P<0.01);凝血指标检测PT(s)为16.36±7.45 vs 13.44±1.50,Fib(g)为1.59±0.87 vs 2.35±1.09(P<0.01);血常规检测NEUT%为0.75±0.13 vs 0.66±0.15,Hb(g/L)为68.04±14.49 vs 100.73±22.92(P<0.01);生化检测Alb(g/L)为29.73±6.08 vs 33.73±7.19,SCr(nmol/L)为99.50±53.55 vs 76.25±19.28(P<0.01)。相关性分析:APTT与R、K值呈正相关,与α、MA值呈负相关;Fib与K值呈负相关,与α、MA值呈正相关;Plt与K值呈负相关,与α、MA值呈正相关(P<0.01)。单因素分析:将得到8个输血前因素K、MA、PT、Fib、NEUT%、Hb、Alb、SCr做Logistic回归,建立输血预测模型,ROC曲线最佳输血阈值(患者输血预测值)为0.448,灵敏度92.9%,特异度88.9%,AUC0.969。结论综合上消化道出血患者TEG、凝血功能、血常规和生化等检测指标,建立Logistic回归模型对预测患者输血结局有明显的关联性,有较好的临床实用性。展开更多
文摘Objective Identification of the risk factors for extraordinary hidden blood loss(HBL) could clarify the underlying causes and provide more appropriate management. This study aims to identify the predictors of HBL in spinal surgery.Methods Medical records were retrospectively retrieved to collect the data of patients who undergoing posterior thoracic and lumbar fusion surgery or scoliosis surgery. Demographic information, perioperative visible blood loss volume, as well as laboratory results were recorded. The patients receiving fusion surgery or scoliosis surgery were further divided into the HBL positive subgroup and the HBL negative subgroup. Differences in the variables between the groups were then analyzed. Binary logistic regression analysis was performed to determine independent risk factors associated with HBL.Results For patients undergoing posterior spinal surgery, the independent risk factors associated with HBL were autologous transfusion(for fusion surgery P = 0.011, OR: 2.627, 95%CI: 1.574-2.782; for scoliosis surgery P < 0.001, OR: 2.268, 95%CI: 2.143-2.504) and allogeneic transfusion(for fusion surgery P < 0.001, OR: 6.487, 95%CI: 2.349-17.915; for scoliosis surgery P < 0.001, OR: 3.636, 95%CI: 2.389-5.231).Conclusion Intraoperative blood transfusion might be an early-warning indicator for perioperative HBL.
基金JMW,RSS,EP,EK,WM,ZBP,and NRMT have received research funding from a precision trauma care research award from the Combat Casualty Care Research Program of the US Army Medical Research and Materiel Command(DM180044).
文摘We read with interest the recent systematic reviewaArtificial intelligence and machine learning for hemorrhagic trauma careoby Peng et al.[1],which evaluated literature on machine learning(ML)in the management of traumatic haemorrhage.We thank the authors for their contribution to the role of ML in trauma.
文摘目的建立应用血栓弹力图(TEG)及凝血指标综合评估上消化道出血患者凝血功能的输血结局预测模型。方法从输血(科)管理系统和医院信息系统(HIS)系统收集浙江省人民医院及其淳安分院消化内科2018年6月~2021年6月收治的101名符合临床诊断标准的上消化道出血患者,根据结局是否输血分为输血组(n=56)和未输血组(n=45),以及按照肝硬化与否分为肝硬化组(n=74)和非肝硬化组(n=27),同时收集40名非上消化道出血患者的阴性对照组。对比各组的TEG检测R、K、α、MA参数,凝血功能检测PT、INR、APTT、TT、Fib,血常规检测Hb、Plt、WBC、NEUT%,以及生化检测Alb、SCr、ALT、AST、GGT等指标(值);分析TEG指标与传统凝血功能指标的相关性;采用单因素和多因素分析,筛选输血相关因素建立预测模型。结果输血组与未输血组比较:TEG的K(min)为3.86±3.12 vs 2.50±1.47,α(°)为54.00±14.08 vs 61.05±10.88,MA(mm)为51.12±13.37 vs 58.26±11.08(P<0.01);凝血指标检测PT(s)为16.36±7.45 vs 13.44±1.50,Fib(g)为1.59±0.87 vs 2.35±1.09(P<0.01);血常规检测NEUT%为0.75±0.13 vs 0.66±0.15,Hb(g/L)为68.04±14.49 vs 100.73±22.92(P<0.01);生化检测Alb(g/L)为29.73±6.08 vs 33.73±7.19,SCr(nmol/L)为99.50±53.55 vs 76.25±19.28(P<0.01)。相关性分析:APTT与R、K值呈正相关,与α、MA值呈负相关;Fib与K值呈负相关,与α、MA值呈正相关;Plt与K值呈负相关,与α、MA值呈正相关(P<0.01)。单因素分析:将得到8个输血前因素K、MA、PT、Fib、NEUT%、Hb、Alb、SCr做Logistic回归,建立输血预测模型,ROC曲线最佳输血阈值(患者输血预测值)为0.448,灵敏度92.9%,特异度88.9%,AUC0.969。结论综合上消化道出血患者TEG、凝血功能、血常规和生化等检测指标,建立Logistic回归模型对预测患者输血结局有明显的关联性,有较好的临床实用性。
