胃癌术后肺部感染列线图预测模型的构建

目的：研究胃癌术后发生肺部感染的相关危险因素,并构建列线图预测模型。方法 ：回顾性分析2010～2020年我院行手术治疗的330例胃癌患者的相关临床资料,筛选可能引起术后肺部感染的因素,采用Logistic回归进行危险因素分析,并构建列线图预测模型。采用校正曲线、一致性指数（c指数）和决策曲线分析（DCA）评价预测准确性、鉴别能力和临床有用性。结果：多因素Logistic回归分析显示年龄、吸烟史、慢性呼吸系统疾病史、肺功能锻炼依从性、腹腔镜手术、术中失血量是胃癌术后发生肺部感染的独立预测因素。依据独立危险因素所构建的列线图,校准曲线提示有较好的一致性,C-index为0.878(95%CI:0.801～0.956),ROC曲线下面积（AUC）为0.878,提示该列线图预测模型具有良好的区分度和校准度。DCA曲线表明该列线图有较好的临床获益。结论：根据胃癌术后发生肺部感染的相关独立危险因素构建的列线图预测模型,有助于对高危人群进行围手术期干预,减少术后肺部感染发生率。     

机器学习算法和COX列线图在肝细胞癌术后生存预测中的应用价值

目的探讨机器学习算法和COX列线图在肝细胞癌术后生存预测中的应用价值。方法采用回顾性描述性研究方法。收集2012年1月至2017年1月中国医学科学院北京协和医学院肿瘤医院收治的375例肝细胞癌行根治性肝切除术患者的临床病理资料;男304例,女71例;中位年龄为57岁,年龄范围为21~79岁。375例患者通过计算机产生随机数方法以8∶2比例分为训练集300例和验证集75例,应用逻辑回归、支持向量机、决策树、随机森林、人工神经网络机器学习算法构建肝细胞癌患者术后生存的预测模型,筛选性能最优的机器学习算法预测模型;构建肝细胞癌患者术后生存预测的COX列线图预测模型;比较最优机器学习算法预测模型和COX列线图预测模型预测肝细胞癌患者术后生存的性能。观察指标:(1)训练集与验证集患者临床病理资料分析。(2)训练集与验证集患者随访及生存情况。(3)机器学习算法预测模型构建及验证。(4)COX列线图预测模型构建及验证。(5)随机森林机器学习算法预测模型与COX列线图预测模型预测性能评价。采用门诊或电话方式进行随访,了解患者生存情况。随访时间截至2019年12月或患者死亡。正态分布的计量资料以±s表示,组间比较采用配对t检验。偏态分布的计量资料以M(P25,P75)或M(范围)表示,组间比较采用Mann-Whitney U检验。计数资料以绝对数表示,当Tmin≥5,N≥40时,组间比较采用χ2检验;当1≤Tmin≤5,N≥40时,采用校正χ2检验;当Tmin<1或N<40时,采用Fisher确切概率法。采用Kaplan-Meier法计算生存率和绘制生存曲线。采用COX比例风险模型进行单因素分析,将P<0.2的变量纳入Lasso回归分析,根据λ值筛选影响预后的变量,最后将变量纳入COX比例风险模型进行多因素分析。结果(1)训练集与验证集患者临床病理资料分析:训练集和验证集患者微血管侵犯(无、有),肝硬化(无、有)分别为292、8例,105、195例和69、6例,37、38例,两组患者比较,差异均有统计学意义(χ2=4.749,5.239,P<0.05)。(2)训练集与验证集患者随访及生存情况:训练集与验证集患者均获得随访。训练集300例患者随访时间为1.1~85.5个月,中位随访时间为50.3个月。验证集75例患者随访时间为1.0~85.7个月,中位随访时间为46.7个月。375例肝细胞癌患者术后1、3年总体生存率分别为91.7%、79.5%。训练集和验证集患者术后1、3年总体生存率分别为92.0%、79.7%和90.7%、81.9%。两组患者术后生存情况比较,差异无统计学意义(χ2=0.113,P>0.05)。(3)机器学习算法预测模型构建及验证。①筛选最优机器学习算法预测模型:根据变量对预测肝细胞癌术后3年生存的信息增益度,应用逻辑回归、支持向量机、决策树、随机森林和人工神经网络5种机器学习算法对肝细胞癌临床病理因素进行变量综合排名。筛选主要预测因素为乙型肝炎e抗原(HBeAg)、手术方式、肿瘤最大直径、围术期输血、肝被膜侵犯、肝脏Ⅳ段侵犯。将预测因素前3、6、9、12、15、18、21、24、27、29个变量依次引入5种机器学习算法。其结果显示:当引入9个变量时,逻辑回归、支持向量机、决策树、随机森林机器学习算法预测模型受试者工作特征曲线的曲线下面积(AUC)趋于稳定。当引入变量>12个时,人工神经网络机器学习算法预测模型AUC波动明显,逻辑回归、支持向量机机器学习算法预测模型AUC稳定性可继续改善,而随机森林机器学习算法预测模型AUC接近0.990,说明随机森林机器学习算法预测模型为最优机器学习算法预测模型。②随机森林机器学习算法预测模型优化和验证:将预测因素29个变量依次引入随机森林机器学习算法预测模型中,构建训练集最佳随机森林机器学习算法预测模型。其结果显示:当引入变量=10个时,网格搜索法示最佳决策树结点个数=4,最佳决策树数目=1000;当引入变量≥10个时,随机森林机器学习算法预测模型AUC稳定在0.990左右。其中当引入变量=10个时,随机森林机器学习算法预测模型预测训练集术后3年总体生存AUC为0.992,灵敏度为0.629,特异度为0.996,预测验证集术后3年总体生存AUC为0.723,灵敏度为0.177,特异度为0.948。(4)COX列线图预测模型构建及验证。①训练集患者术后生存因素分析。单因素分析结果显示:HBeAg、甲胎蛋白、围术期输血、肿瘤最大直径、肝被膜侵犯、肿瘤分化程度是影响肝细胞癌患者术后生存的相关因素(风险比=1.958,1.878,2.170,1.188,2.052,0.222,95%可信区间为1.185~3.235,1.147~3.076,1.389~3.393,1.092~1.291,1.240~3.395,0.070~0.703,P<0.05)。将P<0.2的临床病理因素纳入Lasso回归分析,其结果显示:性别,HBeAg,甲胎蛋白,手术方式,围术期输血,肿瘤最大直径,肿瘤位置在肝脏Ⅴ段和肝脏Ⅷ段,肝被膜侵犯,肿瘤分化程度(高分化、中高分化、中分化、中低分化)是影响肝细胞癌患者术后生存的相关因素。进一步将上述临床病理因素纳入多因素COX回归分析,其结果显示:HBeAg、手术方式、肿瘤最大直径是肝细胞癌患者术后生存的独立影响因素(风险比=1.770,8.799,1.142,95%可信区间为1.049~2.987,1.203~64.342,1.051~1.242,P<0.05)。②COX列线图预测模型的构建和验证:将训练集COX多因素分析结果中P≤0.1的临床病理因素引入Rstudio软件及其rms软件包,构建训练集COX列线图预测模型。COX列线图预测模型预测术后总体生存的C-index为0.723(se=0.028),预测训练集术后3年总体生存AUC为0.760,预测验证集术后3年总体生存AUC为0.795。训练集校准图验证显示COX列线图预测模型对术后生存有较好预测效果。COX列线图回归函数=0.62706×HBeAg(正常=0,异常=1)+0.13434×肿瘤最大直径(cm)+2.10758×手术方式(腹腔镜=0,开腹手术=1)+0.54558×围术期输血(无输血=0,输血=1)-1.42133×高分化(非高分化=0,高分化=1)。计算所有患者COX列线图风险评分,应用Xtile软件寻找COX列线图风险评分最佳阈值,风险评分≥2.9分为高危组,风险评分<2.9分为低危组。Kaplan-Meier总体生存曲线结果显示:训练集低危组和高危组患者术后总体生存比较,差异有统计学意义(χ2=33.065,P<0.05)。验证集低危组和高危组患者术后总体生存比较,差异有统计学意义(χ2=6.585,P<0.05)。进一步采用决策曲线分析结果显示:联合HBeAg、手术方式、围术期输血、肿瘤最大直径和肿瘤分化程度因素的COX列线图预测模型预测性能优于单一因素的预测性能。(5)随机森林机器学习算法预测模型和COX列线图预测模型预测性能评价:通过对2种模型中共同含有的重要变量(肿瘤最大直径)进行分析,并将2种模型通过预测误差曲线进行比较,观察2种模型的预测差异。其结果显示:肿瘤最大直径为2.2 cm时,随机森林机器学习算法和COX列线图预测模型预测患者术后3年生存率分别为77.17%和74.77%(χ2=0.182,P>0.05);肿瘤最大直径为6.3 cm时,随机森林机器学习算法和COX列线图预测模型预测患者术后3年生存率分别为57.51%和61.65%(χ2=0.394,P>0.05);肿瘤最大直径为14.2 cm时,随机森林机器学习算法和COX列线图预测模型预测患者术后3年生存率分别为51.03%和27.52%(χ2=12.762,P<0.05)。随着肿瘤最大直径增加,2种模型预测患者生存率差异增大。验证集中,随机森林机器学习算法预测模型预测患者术后3年总体生存AUC为0.723,COX列线图预测模型预测患者术后3年总体生存AUC为0.795,两者比较,差异有统计学意义(t=3.353,P<0.05)。采用Bootstrap交叉验证结果显示:随机森林机器学习算法预测模型和COX列线图预测模型预测3年生存的整合Brier得分分别为0.139、0.134,COX列线图预测模型预测误差低于随机森林机器学习算法预测模型。结论与机器学习算法预测模型比较,COX列线图预测模型预测肝细胞癌术后3年生存性能更佳,且其变量少,易于临床使用。     

输尿管软镜碎石术后尿路感染的危险因素分析及预测模型构建

目的:分析影响输尿管软镜碎石术后尿路感染的危险因素,建立列线图预测模型。方法:采用便利抽样法,选取2019年4月—2021年7月阜南县人民医院收治的327例行输尿管软镜碎石术患者为研究对象,根据术后30 d内是否发生尿路感染分为感染组与未感染组,采用多因素logistics回归模型筛选术后尿路感染的独立影响因素,基于独立影响因素建立列线图预测模型,并评估模型的区分度与准确度。结果:输尿管软镜碎石术患者术后尿路感染发生率为14.37%（47/327）;多因素logistic回归分析显示,术前尿路感染、合并糖尿病、留置尿管时间>7 d、肾盂内压>30 mmHg为影响术后尿路感染的独立危险因素（P<0.05）,预防性应用抗菌药物为保护性因素（P<0.05）;基于输尿管软镜碎石术后尿路感染的独立影响因素建立列线图预测模型,内部验证结果显示列线图模型的校准曲线预测值与实际值基本一致,Hosmer-Lemeshow拟合优度检验χ²=8.199,P=0.315,ROC曲线下面积为0.805（95%CI:0.729～0.877）,DCA曲线分析阈值范围为0...     

颈动脉支架植入术后谵妄风险列线图模型构建

目的 分析与提取颈动脉支架植入术后患者发生谵妄的危险因素,为针对性干预提供参考.方法 统计350例颈动脉狭窄支架植入术后患者谵妄发生率,行单因素和多因素分析获得术后患者谵妄相关危险因素,基此构建列线图预测模型,采用校正曲线和ROC曲线评估其准确度和区分度.结果 60例术后发生谵妄,发生率17.14％;高龄、术前NIHSS评分和术前焦虑是术后发生谵妄的独立危险因素(均P＜0.05);由3项独立危险因素构建的谵妄风险列线图预测模型,预测曲线和观察曲线基本吻合,AUC=0.888.结论 颈动脉支架植入术后患者谵妄发生率较高;高龄、术前焦虑及脑卒中倾向是术后患者发生谵妄的危险因素;构建的列线图预测模型具有较好的准确度和区分度,可提高筛选效能.     

预测心脏术后引流时间延长风险列线图的建立

【摘要】目的建立可预测心脏术后患者引流时间延长的列线图,便于进行更好的临床管理。方法对中山大学附属孙逸仙纪念医院2014年1月至2016年1月期间152例行开胸心脏手术的病人进行回顾性分析,收集患者的一般资料、既往病史、围手术期相关情况和术后引流时间等资料。通过Logistic回归法分析并筛选术后引流时间的显著影响因素,建立预测术后引流时间延长的列线图。结果单因素分析显示性别、体外循环转机时间、升主动脉阻断时间、吸烟、疾病类型、凝血酶原国际标准比值（PT-INR）、白细胞计数、谷草转氨酶（AST）、谷丙转氨酶（ALT）、术前肌酐、N端脑那肽前体（N-proBNP）、左房内径、左室收缩功能与心脏术后引流时间具有相关性。多因素Logistic回归分析中吸烟史、术前肌酐、白细胞计数、体外循环转机时间、凝血酶原国际比值是独立预后因素,并用于绘制了便于临床使用的列线图。列线图初始的一致性指数（C-idex）为0.78。经过1000次的模型内部验证,并进行矫正,C-idex为0.76。列线图模型的敏感度为80.0%（95%置信区间69.2%~88.4%）,其ROC分析的曲线下面积为0.78（95%置信区间0.74~0.82）。阳性比值比（PLR）为2.43,阴性比值比（NLR）为0.30。结论心脏术后引流时间与多种因素相关,基于相关影响因素建立的预测模型能较为准确预测术后引流时间延长的风险。     

Ⅱ-Ⅲ期结肠癌根治术后复发危险因素分析及其列线图预测模型的应用价值

目的探讨Ⅱ~Ⅲ期结肠癌根治术后复发危险因素及其列线图预测模型的应用价值。方法采用回顾性病例对照研究方法。收集2013年1月至2016年6月西安交通大学第一附属医院收治的228例行根治性切除术治疗Ⅱ~Ⅲ期结肠癌病人的临床病理资料;男118例,女110例;中位年龄为62岁,年龄范围为25~87岁。所有病人行开腹或腹腔镜辅助结肠癌根治性切除术。观察指标:(1)术后复发情况。(2)影响Ⅱ~Ⅲ期结肠癌根治术后复发的危险因素分析。(3)Ⅱ~Ⅲ期结肠癌根治术后复发列线图预测模型的构建及评价。采用门诊或电话方式进行随访,了解病人术后3年复发情况。随访时间截至2019年6月。偏态分布的计量资料以M(范围)表示。计数资料以绝对数表示,组间比较采用Pearsonχ2检验或Fisher确切概率法。多因素分析采用Logistic逐步回归分析。将独立危险因素引入R 3.6.1软件,构建列线图预测模型。绘制受试者工作特征曲线(ROC),以曲线下面积(AUC)评价列线图预测模型的区分度。使用R软件绘制校准度曲线图评价列线图预测模型的一致性。结果(1)术后复发情况:228例病人中,53例术后复发,其中局部复发19例,远处转移34例。34例远处转移病人中,肝转移14例、肺转移7例、脑转移4例、多发转移及其他部位单发转移9例。53例病人术后复发时间为12个月(6~19个月)。(2)影响Ⅱ~Ⅲ期结肠癌根治术后复发的危险因素分析:单因素分析结果为肠梗阻、术前癌胚抗原(CEA)、腹腔积液、血管侵犯是影响Ⅱ~Ⅲ期结肠癌根治术后复发的相关因素(χ²=4.463、13.622、10.914、5.911,P<0.05)。病理学N分期是影响Ⅱ~Ⅲ期结肠癌根治术后复发的相关因素(P<0.05)。多因素分析结果显示:术前CEA>5μg/L、腹腔积液、血管侵犯、病理学N分期为N1期或N2期是影响Ⅱ~Ⅲ期结肠癌根治术后复发的独立危险因素(优势比=3.129,3.071,7.634,3.439,15.467,95%可信区间为1.328~7.373,1.047~9.007,1.103~52.824,1.422~8.319,3.498~68.397,P<0.05)。(3)Ⅱ~Ⅲ期结肠癌根治术后复发列线图预测模型的构建及评价:根据多因素分析结果,将术前CEA、腹腔积液、血管侵犯及病理学N分期引入R 3.6.1软件,构建Ⅱ~Ⅲ期结肠癌根治术后复发的列线图预测模型。术前CEA>5μg/L的列线图评分为41.7分,腹腔积液为41.0分,血管侵犯为74.2分,病理学N分期N1期为45.1分、N2期为100.0分,各项危险因素不同取值得分总和对应术后复发概率。绘制ROC评价列线图预测Ⅱ~Ⅲ期结肠癌根治术后复发的能力,其AUC为0.805(95%可信区间为0.737~0.873,P<0.05)。校准曲线图显示Ⅱ~Ⅲ期结肠癌根治术后列线图模型预测复发概率与实际复发概率具有较好一致性。结论术前CEA>5μg/L、腹腔积液、血管侵犯、病理学N分期为N1或N2期是Ⅱ~Ⅲ期结肠癌根治术后复发的独立危险因素;以此构建列线图预测模型有助于预测Ⅱ~Ⅲ期结肠癌根治术后复发风险。     

结直肠癌术后肠梗阻的危险因素分析及预测模型建立

目的构建结直肠癌(CRC)术后肠梗阻(POI)的列线图风险预测模型并进行验证。方法回顾性收集2018年6月至2019年8月接受CRC手术患者413例的围术期临床资料,年龄≥18岁,ASAⅠ—Ⅲ级。通过LASSO回归和多因素Logistic回归分析筛选独立危险因素,以此建立列线图模型。通过C-index验证模型的区分度;通过Calibration校正曲线验证模型的一致性;并通过决策曲线分析(DCA)以确定模型的临床有效性。结果共有404例CRC患者纳入分析,其中POI患者74例(18.3%)。列线图风险预测模型中包括开腹手术、术中未用非甾体类抗炎药(NSAIDs)、术前白蛋白(Alb)37.55 g/L和术前球蛋白(Glb)≥28.35 g/L。经内部验证,该模型的C-index为0.799(95%CI 0.746～0.852);Calibration校正曲线显示较好的一致性。DCA曲线表明当POI发生的风险阈值超过4%时,此列线图具有临床使用价值。结论基于开腹手术、术中未用NSAIDs、术前Alb37.55 g/L和术前Glb≥28.35 g/L这4个预测因素构建的列线图预测模型对CRC患者发生POI风险有良好的预测性能。     

社区老年高血压患者衰弱的动态列线图风险预测模型构建

目的 构建动态列线图预测模型,分析社区老年高血压患者衰弱的影响因素,为制定针对性的干预措施提供参考。方法 从中国健康与养老追踪调查随访数据库中提取高血压患者信息,以7∶3比例随机分为训练集(n=1 160)与验证集(n=494)。采用Lasso法筛选最佳预测变量,使用logistic回归模型分析高血压患者衰弱影响因素,并构建动态列线图。使用ROC曲线的曲线下面积、Hosmer-Lemeshow检验、校准曲线和决策曲线分析评估列线图的预测性能。结果 共筛选出1 654例老年高血压患者,其中560例(33.86%)并发衰弱。受教育程度、握力、BMI、抑郁、认知障碍、自评健康、代谢性疾病、心脑血管疾病、呼吸系统疾病、胃肠道疾病10个变量纳入预测模型。预测模型在训练集和验证集的ROC曲线下面积分别为0.883(95%CI为0.863～0.903)和0.887(95%CI为0.857～0.916);Hosmer-Lemeshow检验值分别为P=0.825和P=0.410;校准曲线显示预测值和实际值之间存在显著一致性。决策曲线分析显示该模型具有良好的净效益和预测准确性。结论 动态列线图具有良好预测...     

基于Cox回归的Stanford B型主动脉夹层术后谵妄预测模型的构建

目的 开发适用于Stanford B型主动脉夹层患者术后的谵妄预测模型，为早期识别该人群的术后谵妄提供依据。 方法 回顾性纳入2019年1月至2021年3月559例Stanford B型主动脉夹层术后患者，应用Lasso回归选出与术后谵妄相关的预测变量，随后采用多变量Cox回归分析进一步探索术后谵妄的预测因素并构建列线图预测模型。采用自助法重抽样1 000次进行内部验证。 结果 术后谵妄的发生率为14.49%，基于5个预测因素［年龄≥60岁、晕厥、入住ICU、入院时中性粒细胞计数＞6.3×109/L、术后估计肾小球滤过率＜90 mL/（min·1.73 m2）］构建的列线图预测模型，C指数为0.774，在内部验证中为0.762。术后1 d、3 d和7 d的AUC分别为0.776、0.771和0.778，相应的校准图也显示了预测结果和实际观察之间的较好一致性。 结论 基于Cox回归分析以5个预测因素构建的列线图预测模型具有较好的风险预测价值，可帮助医护人员识别Stanford B型主动脉夹层患者术后谵妄风险，为临床工作中针对性地预防和干预术后谵妄提供借鉴。     

肝移植术后早期感染危险因素分析及预测

目的探讨肝移植术后早期(≤1个月)感染发生的危险因素并建立列线图预测模型。 方法回顾性分析2016年1月至2020年12月宁波大学附属李惠利医院肝移植中心200例同种异体肝移植受者临床资料。根据纳入和排除标准共收集181例受者的人口学数据、临床资料和病原菌检测结果,根据术后早期是否发生感染分为感染组(n＝96)和非感染组(n＝85)。分析受者术后早期感染菌群分布特点和相关危险因素,构建列线图并评价其拟合度、区分度和临床实用性。正态分布计量资料采用独立样本t检验比较,不符合正态分布计量资料采用Mann-Whitney U检验比较。分类变量采用χ²检验或Fisher确切概率法。采用Logistic回归分析进行多因素分析。采用R语言(4.1.2)软件rms包构建列线图模型,并通过Bootstrap自抽样法对模型进行内部验证;采用Hosmer-Lemeshow检验、校准曲线、受试者工作特征(ROC)曲线下面积、一致性指数(C指数)及临床决策曲线分析来评价列线图的校准度、区分度及临床实用性。P<0.05为差异有统计学意义。 结果纳入研究的181例受者中,肝移植术后早期感染发生率为53.0%(96/181),96例感染组受者共检出病原菌132株,以革兰阴性菌最为常见(42.4%)。受者术后2周内感染发生率最高(70.8%,68/96),感染常见部位为肺部和血行感染。多因素Logistic回归分析结果显示,受者女性(OR＝4.235,95%CI：1.577～11.370)、MELD评分≥20(OR＝3.742,95%CI：1.296～10.805)、Chlid-Pugh分级C级(OR＝3.346,95%CI：1.263～8.862)、术后呼吸机使用时间(OR＝1.036,95%CI：1.009～1.063)是肝移植术后早期感染的独立危险因素。根据上述独立危险因素建立列线图预测模型,经Bootstrap法进行内部验证,Hosmer-Lemeshow检验无统计学意义(χ²＝7.236,P>0.05),校正曲线贴近于理想曲线,预测模型与观测值具有较好的拟合度。C指数和ROC曲线下面积均为0.800(95%CI：0.735～0.865),模型具有良好的区分度。模型临床决策曲线在较广的阈值概率范围内(0.2～1.0),高于采用单一危险因素预测,显示该模型具有临床实用性。 结论肝移植受者性别、MELD评分、Child-Pugh分级和术后呼吸机使用时间是移植术后早期感染的独立危险因素,列线图对移植术后早期感染的预测效果良好。     

A nomogram for accurately predicting the surgical site infection following transforaminal lumbar interbody fusion in type 2 diabetes patients,based on glycemic variability

Surgical site infection (SSI) is a common and serious complication of transforaminal lumbar interbody fusion (TLIF), and the occurrence of SSI usually leads to prolonged hospitalisation, increased medical costs, poor prognosis, and even death. The objectives of this study were to compare the incidence of SSI in patients with type 2 diabetes, investigate the correlation between perioperative glycemic variability and postoperative SSI, and develop a nomogram model to predict the risk of SSI. This study retrospectively analysed 339 patients with type 2 diabetes who underwent TLIF in the spinal surgery department of the Affiliated Zhongda Hospital of Southeast University from January 2018 to September 2021. The medical records of all patients were collected, and postoperative infection cases were determined according to the diagnostic criteria of surgical site infection. The risk factors for postoperative SSI were analysed by univariate and multivariate logistic regression. And Nomogram prediction model was established and validated. The nomogram incorporated seven independent predictors. Preoperative FPG-CV was the most important independent risk predictor of SSI, followed by preoperative MFBG, duration of drain placement, postoperative FPG-CV, preoperative blood glucose control scheme, duration of diabetes >5 years, and the number of fused vertebrae ≥2. The nomogram showed good diagnostic accuracy for the SS of both the training cohort and the validation cohort (AUC = 0.915 and AUC = 0.890). The calibration curves for the two cohorts both showed optimal agreement between nomogram prediction and actual observation. In conclusion, preoperative and postoperative glycemic variability is closely related to the occurrence of SSI. We developed and validated a nomogram to accurately predict the risk of SSI after TLIF surgery. It's helpful for spinal surgeons to formulate reasonable treatment plans and prevention strategies for type 2 diabetes patients.     

Preoperative risk factors of intraoperative hypothermia in major surgery under general anesthesia

Preoperative factors, such as age and body habitus, affect intraoperative hypothermia during general anesthesia. In a preliminary study, we developed a logistic model to retrospectively evaluate predictors of intraoperative hypothermia in patients who received major surgery. The following factors were selected to develop the model: Z = -15.014 + 0.097 x (Age) + 0.263 x (Height) - 0.323 x (Weight) - 0.055 x (Preoperative systolic blood pressure) - 0.121 x (Preoperative heart rate). By using this model, the probability of hypothermia can be estimated by applying the following formula: Probability = 1/(1 + e(-)(Z)). If an estimated probability of hypothermia was >0.5, the sensibility of prediction was 81.5% and the specificity was 83%. In the second study, the model was applied prospectively to other patients, and the validity of the logistic model was evaluated. The core temperature showed a significant decrease in patients with a probability >0.7, who were predicted to be hypothermic, and their thermoregulatory vasoconstriction threshold also showed a significant decrease, compared with the patients with a probability <==0.3, who were predicted to be normothermic. We concluded that intraoperative hypothermia could be predicted from preoperative characteristics such as age, height, weight, systolic blood pressure, and heart rate. IMPLICATIONS: Increases in age and height and decreases in weight systolic blood pressure and heart rate are major preoperative risk factors of intraoperative hypothermia during major surgery.     

Construction and validation of a model to predict intraoperative hypothermia

OBJECTIVES: Perioperative hypothermia is linked to adverse effects that increase morbidity and mortality. The objectives of this study were to identify the risk factors for intraoperative hypothermia and construct an instrument for identifying patients at high risk. MATERIALS AND METHODS: We studied patients of all ages who had undergone surgery. Patients were assigned to a design group or a validation group by means of a list of randomly generated numbers. Intraoperative hypothermia was defined by an tympanic temperature of 35.9 degrees C or less. A bivariate analysis of the design group identified the predictive factors and a multivariate analysis (logistic regression with backward elimination of nonsignificant variables) provided a predictive model. Risk scores were obtained for each variable by converting them to a 4-degree risk scale (abbreviated model). Predictive power was determined by calculating the area under the receiver-operator characteristic curve (AUC). RESULTS: We enrolled 264 consecutive patients; 200 were assigned to the design group and 64 to the validation group. In the design group, the AUC was 0.85 for the complete model and 0.83 for the abbreviated model. In the validation group, the AUC was 0.85 for the complete model and 0.82 for the abbreviated model. The P value was <.01 for all curves. CONCLUSION: Age, weight, approximate duration of surgery, and body and ambient temperature during induction were the included factors that predicted intraoperative hypothermia in a heterogeneous sample of surgical patients.     

建立预测法洛四联症根治术后出血风险的列线图

目的明确儿童法洛四联症(tetralogy of Fallot,TOF)根治术后出血的危险因素并构建具有预测术后出血风险性能的列线图。方法回顾性分析2018年11月至2019年6月在我院行TOF根治手术儿童的临床数据。术后出血定义为术后24 h内的胸腔积液量≥16 mL/kg,相当于本研究人群的第75个百分位数。主要结果采用最小绝对收缩与选择算子(the least absolute shrinkage and selection operator,LASSO)回归、单因素以及多因素logistic回归分析确定术后出血的独立预测因素,在此基础上构建列线图,并分析其一致性、区分度。结果入选儿童105例。出血组术后24 h胸腔积液量明显高于非出血组(P<0.0001)。多因素logistic回归分析显示,患儿低体重[odds ratio(OR)=0.538,95%confidence interval(CI)0.369~0.787,P=0.001]、术前高血红蛋白浓度(OR=1.036,95%CI 1.008~1.066,P=0.013)、术中主动脉阻断时间长(OR=1.022,95%CI 1.000~1.044,P=0.048)是术后出血风险独立预测因素。在内部验证中,列线图C-指数为0.835(95%CI 0.745~0.926),并且其校准曲线质量高。结论本研究建立的列线图在评估TOF根治术后出血风险方面显示出良好的一致性与区分度。     

Risk factors and the nomogram model for intraoperatively acquired pressure injuries in children with brain tumours: A retrospective study

This study aimed to investigate the clinical features and incidence of Intraoperatively Acquired Pressure Injuries (IAPIs) of brain tumours in children, to screen the risk factors and to establish a nomogram model for making prevention strategies against the development of IAPIs. Clinical data of 628 children undergoing brain tumour surgery from August 2019 to August 2021 were extracted from the adverse events and the electronic medical systems. They were randomly divided into a training cohort(n = 471) and a validation cohort(n = 157). The univariate and multivariate analysis was performed to identify the risk factors in training cohort; R software was used to construct a nomogram model; the area under the receiver operator characteristic curve (AUC) and calibration plots were used to judge the predictive performance of the nomogram model; decision curve analysis (DCA) was used to assess the clinical usefulness of the nomogram model. Age, haemorrhage, use of vasopressor, temperature, operation time and operation position were considered as significant risk factors, and enrolled to construct a nomogram model. The results of AUC showed satisfactory discrimination of the nomogram; the calibration plots indicated favourable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts; DCA showed better net benefit and threshold probability of the nomogram model. The nomogram model illustrates significant predictive ability, which can provide scientific and individual guidance for preventing development of IAPIs.     

Intraoperative and postoperative hyponatremia with craniosynostosis surgery

Background: An alarming incidence of significant intraoperative hyponatremia during major pediatric craniofacial surgery has recently been reported, the mechanism of which is unclear. Aims: To establish the incidence and severity of hyponatremia occurring during and after major craniofacial surgery for craniosynostosis in our institution and identify any associated risk factors. Methods: Retrospective review of case notes and blood test results for all cases of major craniofacial surgery for craniosynostosis in children under 10 years of age from January 2007 to May 2011. Hyponatremia was classified as: mild 131–134 mmol·l^?1; moderate 126–130 mmol·l^?1; and, severe ≤125 mmol·l^?1. Analyses were performed to look for factors associated with hyponatremia including gender, weight, syndromic or not, duration of procedure, and volumes of crystalloid, colloid and blood administered. Results: One hundred and two consecutive cases were identified. Mild intraoperative hyponatremia occurred in five (5%) of children. There were no cases of moderate or severe intraoperative hyponatremia. All five had normal sodium values within two hours of their single low readings of 134 mmol.l^?1 and none had any subsequent episodes of hyponatremia in the postoperative period. Intraoperative hyponatremia was associated with lower body weight (P = 0.002). Mild postoperative hyponatremia on the day of surgery (POD0) occurred in three other children (3%) with no identifiable associations. There were no cases of moderate or severe postoperative hyponatremia on POD0. Hyponatremia on the first postoperative day (POD1) was mild in 23 children (24%) and moderate in one child (1%). There were no cases of severe postoperative hyponatremia on POD1. Hyponatraemia on POD1 was associated with male gender (P = 0.042). Conclusions: Clinically significant intraoperative hyponatremia was not a feature of major craniofacial surgery in our institution. Mild postoperative hyponatremia was relatively common on POD1.     

PI-RADS≤3分患者预测前列腺穿刺阳性列线图模型的构建

目的探讨前列腺影像报告及数据系统（PI-RADS.v2.1）联合前列腺特异性抗原及其他参数构建的列线图模型对PI-RADS≤3分患者活检阳性的预测价值。 方法回顾性分析2018年1月至2021年12月198例在中山市人民医院接受经直肠超声穿刺前列腺首次活检患者的临床血清学和影像学资料,应用Logistic多因素回归分析前列腺癌相关独立风险因素,并构建对前列腺PI-RADS≤3分病变的列线图模型,利用受试者工作曲线、校准曲线和决策曲线对模型进行评估。 结果多因素Logistic回归分析显示年龄（P<0.001）、PI-RADS（P=0.017）、游离PSA/总PSA（FPSA/TPSA） （P=0.049）及移行带体积（TZV） （P<0.001）是前列腺癌的独立危险因素。基于多变量构建的融合模型效能最优（AUC=0.823,95%CI=0.762~0.885）,敏感性81.3%,特异性78.8%,准确性79.8%。校准曲线显示其预测概率与病理结果有良好的一致性。决策曲线显示模型具有良好的临床应用价值。 结论基于多变量构建的列线图及预测模型能较好地术前预测患前列腺癌的风险。     

Risk factors and the potential of nomogram for predicting hospital‐acquired pressure injuries

This 1:5 case‐control study aimed to identify the risk factors of hospital‐acquired pressure injuries (HAPIs) and to develop a mathematical model of nomogram for the risk prediction of HAPIs. Data for 370 patients with HAPIs and 1971 patients without HAPIs were extracted from the adverse events and the electronic medical systems. They were randomly divided into two sets: training (n = 1951) and validation (n = 390). Significant risk factors were identified by univariate and multivariate analyses in the training set, followed by a nomogram constructed. Age, independent movement, sensory perception and response, moisture, perfusion, use of medical devices, compulsive position, hypoalbuminaemia, an existing pressure injury or scarring from a previous pressure injury, and surgery sufferings were considered significant risk factors and were included to construct a nomogram. In both of the training and validation sets, the areas of 0.90 under the receiver operating characteristic curves showed excellent discrimination of the nomogram; calibration plots demonstrated a good consistency between the observed probability and the nomogram's prediction; decision curve analyses exhibited preferable net benefit along with the threshold probability in the nomogram. The excellent performance of the nomogram makes it a convenient and reliable tool for the risk prediction of HAPIs.     

Temperature monitoring and management during neuraxial anesthesia: an observational study

Temperature monitoring and thermal management are rare during spinal or epidural anesthesia because clinicians apparently restrict monitoring to patients with an expected risk of hypothermia. This implies that anesthesiologists can predict patient thermal status without monitoring core temperature. We therefore, tested the hypotheses that during neuraxial anesthesia: 1) amount of core hypothermia depends on the magnitude and duration of surgery; 2) temperature monitoring and thermal management are used selectively in patients at high risk of hypothermia; and 3) anesthesiologists can estimate patient thermal status. We evaluated thermal status on arrival in the recovery room along with intraoperative thermal management and monitoring in 120 patients. Anesthesiologists were asked if their patients were hypothermic (<36 degrees C). There was no correlation between the magnitude or duration of surgery and initial postoperative core temperature in unwarmed patients. Temperature monitoring and thermal management were not used selectively in high-risk patients. Initial postoperative tympanic membrane temperatures were <36 degrees C in 77% of patients and <35 degrees C in 22%. Body temperature was monitored intraoperatively in 27% of the patients and forced-air warming was used in 31%. Anesthesiologists failed to accurately estimate whether their patients were hypothermic. Our results suggest that temperature monitoring and management during neuraxial anesthesia is currently inadequate. IMPLICATIONS: In this observational study, we evaluated core temperatures and intraoperative thermal management in patients undergoing spinal or epidural anesthesia. Hypothermia was common, however, rarely detected either by temperature monitoring or estimates by anesthesiologists. In addition, it was not treated with active warming. Consequently, temperature monitoring and management have to be done during neuraxial anesthesia.     

Mild hypothermia, blood loss and complications in elective spinal surgery.

BACKGROUND CONTEXT: Spinal surgery carries risks of incidental spinal cord and nerve root injury. Neuroprotection, to minimize the extent of such injuries, is desirable. However, no neuroprotective strategies have been conclusively validated in nonvascular spinal surgery. Mild hypothermia resulting from general anesthesia is a readily achievable potential neuroprotective strategy. Mild hypothermia, however, has been associated with wound infection, increased operative blood loss and other complications. No previous studies have specifically evaluated whether mild hypothermia is associated with an increased risk of these complications in elective spinal surgery. PURPOSE: We investigated the association between incidental mild hypothermia, perioperative complications and operative blood loss. STUDY DESIGN/SETTING: This is a retrospective study employing cohort analysis, rank analysis and single and multivariate linear regression. The setting was the Veterans Administration Medical Center, a teaching hospital of the University of Miami. PATIENT SAMPLE: Data on a total of 70 adult veterans aged 23 to 81 years undergoing complex spinal procedures in which passive cooling was employed during surgical decompression. OUTCOME MEASURES: The variables measured were temperature, blood loss, mean arterial pressure (MAP) and duration of anesthesia. The outcome measured was the presence or absence of complications. METHODS: After 70 patients had been acquired, regression and rank analyses were performed to test for a link between mild hypothermia and blood loss. In addition, two cohorts, patients who experienced complications, and those who did not experience complications in the perioperative period, were compared for several variables including three measures of exposure to hypothermia. Surgical procedures included 60 cervical, 1 occipitocervical, 1 cervicothoracic, 7 thoracic and 1 thoracolumbar procedure. Hypothermia followed induction of anesthesia; esophageal or bladder temperature was monitored. Cooling was passive; warming utilized a forced air blanket. Temperature data from anesthetic records was used to derive mean intraoperative temperature, nadir intraoperative temperature and the rates of cooling and rewarming. The time course of hypothermia, the overall fluctuation in core temperature and the quantity of subbaseline temperature were determined. Medical and surgical complications were included. Two patients with complications considered irrelevant to hypothermia were removed from further analysis. Patients with and without complications were compared as cohorts for differences in mean values of age, comorbid risk factors, intraoperative MAP, intraoperative blood loss, anesthetic duration and temperature-related measures. Relationships between blood loss, anesthesia duration and temperature parameters were assessed in rank and regression analyses. RESULTS: Patients with complications (n=12) had longer mean anesthetic durations (p=.0001) and larger mean surgical blood losses (p=.001) than patients without complications (n=56). Neither mean nor nadir intraoperative hypothermic temperatures were statistically associated with complications. However, large hypothermic integrals (p=.04) and the total quantity of recorded temperature fluctuation (p=.01) were both associated with complications. Comorbid risk factors, MAP and age were not statistically linked to complications. Finally, no relationship between any of the temperature measures and increased blood loss was found. CONCLUSION: Operative blood loss was not linked to any index of the patient's temperature. Longer anesthesia durations were linked to complications and increased blood loss. Regarding mild hypothermia, neither mean nor nadir hypothermic temperatures were linked to complications, but the estimated total quantity of subbaseline temperature was linked, as was total fluctuation in temperature. Lengthy exposure to mild hypothermia appeared to be associated with wound infections. The use of mild hypothermia as a potential neuroprotective strategy during spinal surgery appears to be reasonably safe, but to avoid complications, the duration of hypothermic exposure should be minimized.