目的:探讨老年髋部骨折手术延迟的影响因素,构建老年髋部骨折手术延迟风险预测模型。方法:选取2019年11月至2022年11月采用手术治疗的老年髋部骨折患者的病例资料进行研究,将纳入研究的患者按照2∶1的比例随机分为训练集(用于模型构建)...目的:探讨老年髋部骨折手术延迟的影响因素,构建老年髋部骨折手术延迟风险预测模型。方法:选取2019年11月至2022年11月采用手术治疗的老年髋部骨折患者的病例资料进行研究,将纳入研究的患者按照2∶1的比例随机分为训练集(用于模型构建)和验证集(用于模型验证)。从病历系统中提取纳入患者的信息,包括年龄、性别、体质量指数、骨折类型、美国麻醉医师协会(American Society of Anesthesiologists, ASA)分级、伤前日常活动能力(activities of daily living, ADL)、是否服用影响凝血功能的药物、入院至手术时间、手术方式,是否合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、肝功能不全、肾功能不全、电解质紊乱、尿酮体异常、下肢静脉血栓、凝血功能异常,以及入院后血清肿瘤坏死因子-α、C反应蛋白水平等。将训练集中的患者根据入院至手术时间分为早期手术组(入院至手术时间<48 h)和延迟手术组(入院至手术时间≥48 h)。先对2组患者的相关信息进行单因素对比分析,再对单因素分析中组间差异有统计学意义的因素进行多因素Logistic回归分析及多重共线性诊断;采用R软件基于贝叶斯网络模型构建老年髋部骨折手术延迟风险预测模型,并采用Netica软件进行贝叶斯网络模型推理。采用受试者操作特征(receiver operating characteristic, ROC)曲线评价老年髋部骨折手术延迟风险预测模型的区分度,采用校准曲线评价老年髋部骨折手术延迟风险预测模型的校准度。结果:(1)分组结果。共纳入老年髋部骨折患者318例,训练集212例、验证集106例。根据入院至手术时间,训练集中早期手术组78例、延迟手术组134例。(2)老年髋部骨折手术延迟影响因素的单因素分析结果。2组患者ASA分级、是否服用影响凝血功能的药物及是否合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常的比较,组间差异均有统计学意义(χ~2=3.862,P=0.049;χ~2=26.806,P=0.000;χ~2=29.852,P=0.000;χ~2=21.743,P=0.000;χ~2=25.226,P=0.000;χ~2=5.415,P=0.020;χ~2=11.683,P=0.001;χ~2=14.686,P=0.000;χ~2=6.057,P=0.014)。(3)老年髋部骨折手术延迟影响因素的多因素分析及多重共线性诊断结果。多因素Logistic回归分析结果显示,服用影响凝血功能的药物及合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常均是老年髋部骨折手术延迟的影响因素[β=0.328,P=0.000,OR=5.112,95%CI(2.686,9.728);β=0.322,P=0.000,OR=5.425,95%CI(2.884,10.203);β=0.302,P=0.000,OR=3.956,95%CI(2.189,7.148);β=0.312,P=0.000,OR=4.560,95%CI(2.476,8.398);β=0.291,P=0.021,OR=1.962,95%CI(1.108,3.474);β=0.296,P=0.001,OR=2.713,95%CI(1.520,4.844);β=0.303,P=0.000,OR=3.133,95%CI(1.729,5.679);β=0.296,P=0.015,OR=2.061,95%CI(1.154,3.680)];多重共线性诊断结果显示,上述影响因素均不存在共线性(VIF=1.134,VIF=1.266,VIF=1.465,VIF=1.389,VIF=1.342,VIF=1.183,VIF=1.346,VIF=1.259)。(4)基于贝叶斯网络模型的老年髋部骨折手术延迟风险预测模型的构建与推理结果。基于贝叶斯网络模型构建的老年髋部骨折手术延迟风险预测模型包括8个节点、8条有向边。模型显示,服用影响凝血功能的药物及合并精神障碍、呼吸系统疾病、电解质紊乱、凝血功能异常直接影响手术延迟的发生,合并心功能不全、高血压、糖尿病间接影响手术延迟的发生;推理结果显示,患者合并心功能不全、凝血功能异常及精神障碍时,手术延迟发生率为64.1%。(5)老年髋部骨折手术延迟风险预测模型的评价结果。采用训练集数据进行老年髋部骨折手术延迟风险预测模型评价,ROC曲线下面积为0.861[P=0.000,95%CI(0.810,0.912)],灵敏度为91.29%,特异度为93.35%;校准曲线显示其一致性指数为0.866[P=0.000,95%CI(0.702,0.943)];采用验证集数据进行老年髋部骨折手术延迟风险预测模型评价,ROC曲线下面积为0.848[P=0.000,95%CI(0.795,0.901)],灵敏度为91.62%,特异度为92.46%;校准曲线显示其一致性指数为0.879[P=0.000,95%CI(0.723,0.981)]。结论:服用影响凝血功能的药物以及合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常均为老年髋部骨折手术延迟的影响因素,基于上述因素构建的老年髋部骨折手术延迟风险预测模型具有较高的应用价值。展开更多
Objective The purpose of this study was to investigate the bacterial communities of biting midges and ticks collected from three sites in the Poyang Lake area,namely,Qunlu Practice Base,Peach Blossom Garden,and Huangt...Objective The purpose of this study was to investigate the bacterial communities of biting midges and ticks collected from three sites in the Poyang Lake area,namely,Qunlu Practice Base,Peach Blossom Garden,and Huangtong Animal Husbandry,and whether vectors carry any bacterial pathogens that may cause diseases to humans,to provide scientific basis for prospective pathogen discovery and disease prevention and control.Methods Using a metataxonomics approach in concert with full-length 16S rRNA gene sequencing and operational phylogenetic unit(OPU)analysis,we characterized the species-level microbial community structure of two important vector species,biting midges and ticks,including 33 arthropod samples comprising 3,885 individuals,collected around Poyang Lake.Results A total of 662 OPUs were classified in biting midges,including 195 known species and 373 potentially new species,and 618 OPUs were classified in ticks,including 217 known species and 326 potentially new species.Surprisingly,OPUs with potentially pathogenicity were detected in both arthropod vectors,with 66 known species of biting midges reported to carry potential pathogens,including Asaia lannensis and Rickettsia bellii,compared to 50 in ticks,such as Acinetobacter lwoffii and Staphylococcus sciuri.We found that Proteobacteria was the most dominant group in both midges and ticks.Furthermore,the outcomes demonstrated that the microbiota of midges and ticks tend to be governed by a few highly abundant bacteria.Pantoea sp7 was predominant in biting midges,while Coxiella sp1 was enriched in ticks.Meanwhile,Coxiella spp.,which may be essential for the survival of Haemaphysalis longicornis Neumann,were detected in all tick samples.The identification of dominant species and pathogens of biting midges and ticks in this study serves to broaden our knowledge associated to microbes of arthropod vectors.Conclusion Biting midges and ticks carry large numbers of known and potentially novel bacteria,and carry a wide range of potentially pathogenic bacteria,which may pose a risk of infection to humans and animals.The microbial communities of midges and ticks tend to be dominated by a few highly abundant bacteria.展开更多
文摘目的:探讨老年髋部骨折手术延迟的影响因素,构建老年髋部骨折手术延迟风险预测模型。方法:选取2019年11月至2022年11月采用手术治疗的老年髋部骨折患者的病例资料进行研究,将纳入研究的患者按照2∶1的比例随机分为训练集(用于模型构建)和验证集(用于模型验证)。从病历系统中提取纳入患者的信息,包括年龄、性别、体质量指数、骨折类型、美国麻醉医师协会(American Society of Anesthesiologists, ASA)分级、伤前日常活动能力(activities of daily living, ADL)、是否服用影响凝血功能的药物、入院至手术时间、手术方式,是否合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、肝功能不全、肾功能不全、电解质紊乱、尿酮体异常、下肢静脉血栓、凝血功能异常,以及入院后血清肿瘤坏死因子-α、C反应蛋白水平等。将训练集中的患者根据入院至手术时间分为早期手术组(入院至手术时间<48 h)和延迟手术组(入院至手术时间≥48 h)。先对2组患者的相关信息进行单因素对比分析,再对单因素分析中组间差异有统计学意义的因素进行多因素Logistic回归分析及多重共线性诊断;采用R软件基于贝叶斯网络模型构建老年髋部骨折手术延迟风险预测模型,并采用Netica软件进行贝叶斯网络模型推理。采用受试者操作特征(receiver operating characteristic, ROC)曲线评价老年髋部骨折手术延迟风险预测模型的区分度,采用校准曲线评价老年髋部骨折手术延迟风险预测模型的校准度。结果:(1)分组结果。共纳入老年髋部骨折患者318例,训练集212例、验证集106例。根据入院至手术时间,训练集中早期手术组78例、延迟手术组134例。(2)老年髋部骨折手术延迟影响因素的单因素分析结果。2组患者ASA分级、是否服用影响凝血功能的药物及是否合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常的比较,组间差异均有统计学意义(χ~2=3.862,P=0.049;χ~2=26.806,P=0.000;χ~2=29.852,P=0.000;χ~2=21.743,P=0.000;χ~2=25.226,P=0.000;χ~2=5.415,P=0.020;χ~2=11.683,P=0.001;χ~2=14.686,P=0.000;χ~2=6.057,P=0.014)。(3)老年髋部骨折手术延迟影响因素的多因素分析及多重共线性诊断结果。多因素Logistic回归分析结果显示,服用影响凝血功能的药物及合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常均是老年髋部骨折手术延迟的影响因素[β=0.328,P=0.000,OR=5.112,95%CI(2.686,9.728);β=0.322,P=0.000,OR=5.425,95%CI(2.884,10.203);β=0.302,P=0.000,OR=3.956,95%CI(2.189,7.148);β=0.312,P=0.000,OR=4.560,95%CI(2.476,8.398);β=0.291,P=0.021,OR=1.962,95%CI(1.108,3.474);β=0.296,P=0.001,OR=2.713,95%CI(1.520,4.844);β=0.303,P=0.000,OR=3.133,95%CI(1.729,5.679);β=0.296,P=0.015,OR=2.061,95%CI(1.154,3.680)];多重共线性诊断结果显示,上述影响因素均不存在共线性(VIF=1.134,VIF=1.266,VIF=1.465,VIF=1.389,VIF=1.342,VIF=1.183,VIF=1.346,VIF=1.259)。(4)基于贝叶斯网络模型的老年髋部骨折手术延迟风险预测模型的构建与推理结果。基于贝叶斯网络模型构建的老年髋部骨折手术延迟风险预测模型包括8个节点、8条有向边。模型显示,服用影响凝血功能的药物及合并精神障碍、呼吸系统疾病、电解质紊乱、凝血功能异常直接影响手术延迟的发生,合并心功能不全、高血压、糖尿病间接影响手术延迟的发生;推理结果显示,患者合并心功能不全、凝血功能异常及精神障碍时,手术延迟发生率为64.1%。(5)老年髋部骨折手术延迟风险预测模型的评价结果。采用训练集数据进行老年髋部骨折手术延迟风险预测模型评价,ROC曲线下面积为0.861[P=0.000,95%CI(0.810,0.912)],灵敏度为91.29%,特异度为93.35%;校准曲线显示其一致性指数为0.866[P=0.000,95%CI(0.702,0.943)];采用验证集数据进行老年髋部骨折手术延迟风险预测模型评价,ROC曲线下面积为0.848[P=0.000,95%CI(0.795,0.901)],灵敏度为91.62%,特异度为92.46%;校准曲线显示其一致性指数为0.879[P=0.000,95%CI(0.723,0.981)]。结论:服用影响凝血功能的药物以及合并精神障碍、高血压、糖尿病、呼吸系统疾病、心功能不全、电解质紊乱、凝血功能异常均为老年髋部骨折手术延迟的影响因素,基于上述因素构建的老年髋部骨折手术延迟风险预测模型具有较高的应用价值。
基金supported by grants from National Key R&D Program of China(2019YFC1200501)Research Units of Discovery of Unknown Bacteria and Function(2018RU010)Capacity Enhancement Project supported by National Institute for Communicable Disease Control and Prevention(China CDC).
文摘Objective The purpose of this study was to investigate the bacterial communities of biting midges and ticks collected from three sites in the Poyang Lake area,namely,Qunlu Practice Base,Peach Blossom Garden,and Huangtong Animal Husbandry,and whether vectors carry any bacterial pathogens that may cause diseases to humans,to provide scientific basis for prospective pathogen discovery and disease prevention and control.Methods Using a metataxonomics approach in concert with full-length 16S rRNA gene sequencing and operational phylogenetic unit(OPU)analysis,we characterized the species-level microbial community structure of two important vector species,biting midges and ticks,including 33 arthropod samples comprising 3,885 individuals,collected around Poyang Lake.Results A total of 662 OPUs were classified in biting midges,including 195 known species and 373 potentially new species,and 618 OPUs were classified in ticks,including 217 known species and 326 potentially new species.Surprisingly,OPUs with potentially pathogenicity were detected in both arthropod vectors,with 66 known species of biting midges reported to carry potential pathogens,including Asaia lannensis and Rickettsia bellii,compared to 50 in ticks,such as Acinetobacter lwoffii and Staphylococcus sciuri.We found that Proteobacteria was the most dominant group in both midges and ticks.Furthermore,the outcomes demonstrated that the microbiota of midges and ticks tend to be governed by a few highly abundant bacteria.Pantoea sp7 was predominant in biting midges,while Coxiella sp1 was enriched in ticks.Meanwhile,Coxiella spp.,which may be essential for the survival of Haemaphysalis longicornis Neumann,were detected in all tick samples.The identification of dominant species and pathogens of biting midges and ticks in this study serves to broaden our knowledge associated to microbes of arthropod vectors.Conclusion Biting midges and ticks carry large numbers of known and potentially novel bacteria,and carry a wide range of potentially pathogenic bacteria,which may pose a risk of infection to humans and animals.The microbial communities of midges and ticks tend to be dominated by a few highly abundant bacteria.
