目的:鉴于脓毒症的高发病率和高病死率,早期识别高风险患者并及时干预至关重要,而现有死亡风险预测模型在操作、适用性和预测长期预后等方面均存在不足。本研究旨在探讨脓毒症患者死亡的危险因素,构建近期和远期死亡风险预测模型。方法...目的:鉴于脓毒症的高发病率和高病死率,早期识别高风险患者并及时干预至关重要,而现有死亡风险预测模型在操作、适用性和预测长期预后等方面均存在不足。本研究旨在探讨脓毒症患者死亡的危险因素,构建近期和远期死亡风险预测模型。方法:从美国重症监护医学信息数据库IV(Medical Information Mart for Intensive Care-IV,MIMIC-IV)中选取符合脓毒症3.0诊断标准的人群,按7?3的比例随机分为建模组和验证组,分析患者的基线资料。采用单因素Cox回归分析和全子集回归确定脓毒症患者死亡的危险因素并筛选出构建预测模型的变量。分别用时间依赖性曲线下面积(area under the curve,AUC)、校准曲线和决策曲线评估模型的区分度、校准度和临床实用性。结果:共纳入14240例脓毒症患者,28 d和1年病死率分别为21.45%(3054例)和36.50%(5198例)。高龄、女性、高感染相关器官衰竭评分(sepsis-related organ failure assessment,SOFA)、高简明急性生理学评分(simplified acute physiology score II,SAPS II)、心率快、呼吸频率快、脓毒症休克、充血性心力衰竭、慢性阻塞性肺疾病、肝脏疾病、肾脏疾病、糖尿病、恶性肿瘤、高白细胞计数(white blood cell count,WBC)、长凝血酶原时间(prothrombin time,PT)、高血肌酐(serum creatinine,SCr)水平均为脓毒症死亡的危险因素(均P<0.05)。由PT、呼吸频率、体温、合并恶性肿瘤、合并肝脏疾病、脓毒症休克、SAPS II及年龄8个变量构建的模型,其28 d和1年生存的AUC分别为0.717(95%CI 0.710~0.724)和0.716(95%CI 0.707~0.725)。校准曲线和决策曲线表明该模型具有良好的校准度及较好的临床应用价值。结论:基于MIMIC-IV建立的脓毒症患者近期和远期死亡风险预测模型有较好的识别能力,对患者预后风险评估及干预治疗具有一定的临床参考意义。展开更多
目的探讨平均动脉压(mean arterial pressure,MAP)变异度与重症患者的重症医学科(intensive care unit,ICU)病死率之间的关系。方法回顾性分析重症监护医学信息数据库(Medical Information Mart for Intensive Care,MIMIC)-Ⅲ中38852例...目的探讨平均动脉压(mean arterial pressure,MAP)变异度与重症患者的重症医学科(intensive care unit,ICU)病死率之间的关系。方法回顾性分析重症监护医学信息数据库(Medical Information Mart for Intensive Care,MIMIC)-Ⅲ中38852例入ICU的重症患者的临床资料,计算入ICU后24 h内记录的MAP的变异系数作为MAP变异度,采用一般线性回归观察入ICU 24 h内MAP变异度与重症患者ICU病死率之间的相关性,并采用受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under curve,AUC)评估MAP变异度预测重症患者ICU病死率的能力。结果入ICU 24 h的MAP变异度与ICU病死率之间有很好的相关性(R2=0.860,P<0.001),MAP变异度越大,ICU病死率越高。24h的MAP变异程度预测ICU病死率的AUC为0.61。结论重症患者入ICU 24 h内的MAP变异度与ICU病死率有很好的相关性,MAP变异度越大,ICU病死率越高;MAP变异度能够为简单快速预测危重患者的ICU病死率提供一定的信息。展开更多
Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical I...Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.展开更多
目的 利用入住重症监护病房(intensive care unit, ICU)时的血压变异度(CV-MAP)及心率变异度(CV-HR)构建预测模型,预测ICU患者院内死亡的风险。方法 回顾性分析在美国重症监护医学信息数据库Ⅲ(medical information mart for intensive ...目的 利用入住重症监护病房(intensive care unit, ICU)时的血压变异度(CV-MAP)及心率变异度(CV-HR)构建预测模型,预测ICU患者院内死亡的风险。方法 回顾性分析在美国重症监护医学信息数据库Ⅲ(medical information mart for intensive care, MIMICⅢ)中年龄≥18岁,且首次入住ICU患者的临床资料。通过多因素Logistic分析筛选危险因素并构建评分系统,采用受试者工作特征(receiver operator characteristic, ROC)曲线和校准曲线评估模型区分度和校准度,采用临床决策曲线评估模型实际应用价值。结果 共筛选符合标准的患者38 824例,院内死亡患者4075例(住院病死率为10.5%)。从危险因素中选择年龄、是否合并肝脏疾病、是否合并血液系统恶性肿瘤、是否合并转移癌、住院类型、24 h心率变异系数、24 h血压变异系数、是否使用血管活性药、是否接受镇痛治疗、是否接受镇静治疗、是否接受有创机械通气构建简化预测模型。模型预测院内死亡的ROC曲线下面积(AUC)为0.743(95%CI 0.735~0.750,P<0.001),Hosmer-Lemeshow检验χ^(2)=4.978,P=0.083。使用Bootstrap法进行1000次重复采样进行内部验证,校正曲线判断预测值与实际值一致性较好。决策曲线分析提示,在高阈值风险0.1~0.6时,预测模型具有较高的实用价值。结论 基于CV-MAP及CV-HR建立ICU患者院内死亡风险预测模型具有较好的临床预测价值,有助于识别高危患者。展开更多
目的:研究血红蛋白-红细胞分布宽度比值(hemoglobin/red blood cell distribution width ratio,HRR)与冠脉搭桥手术(coronary artery bypass grafting,CABG)后急性肾损伤(acute kidney injury,AKI)发生风险的相关性。方法:选择重症监护...目的:研究血红蛋白-红细胞分布宽度比值(hemoglobin/red blood cell distribution width ratio,HRR)与冠脉搭桥手术(coronary artery bypass grafting,CABG)后急性肾损伤(acute kidney injury,AKI)发生风险的相关性。方法:选择重症监护数据集(Medical Information Mart for Intensive Care Database,MIMIC)-Ⅳ中所有CABG术后患者作为研究对象,根据是否发生AKI分为2组。比较分析2组一般资料,并将有统计学差异的变量纳入logistic单因素回归分析,以单因素分析中P<0.05的变量纳入多因素logistic回归分析。Logistic回归模型评估HRR对CABG术后AKI风险的预测价值。亚组分析采用层次回归模型。结果:共5 623例患者纳入研究,AKI组4 342例,非AKI组1 281例。AKI组患者入院时HRR水平明显低于非AKI组(P<0.001)。多因素回归模型显示HRR是预测CABG术后AKI风险的独立危险因子(OR=0.92,95%CI=0.88~0.96,P<0.001)。Logistic回归模型发现,在模型Ⅲ中(调整潜在混杂因素),HRR水平降低仍然是CABG术后发生AKI的独立影响因素。亚组分析发现HRR与AKI发生的相关性在大多数协变量中相似。结论:低HRR水平是CABG术后AKI发生风险的独立危险因素。展开更多
目的评价急诊滞留时间(Emergency Department Length of Stay,ED-LOS)与需要入住重症监护病房(Intensive Care Unit,ICU)的脓毒症患者预后之间的关系。方法此回顾性队列研究从重症监护医学信息数据库(Medical Information Mark for Inte...目的评价急诊滞留时间(Emergency Department Length of Stay,ED-LOS)与需要入住重症监护病房(Intensive Care Unit,ICU)的脓毒症患者预后之间的关系。方法此回顾性队列研究从重症监护医学信息数据库(Medical Information Mark for Intensive Care,MIMIC Ⅲ)提取出需要从急诊科(Emergency Department,ED)直接转入ICU的脓毒症患者的相关信息,以ED-LOS是否大于4 h将研究人群分成两组,比较两组患者结局指标的差异。倾向性评分匹配(Propensity Score Matching,PSM)用于平衡两组间的基线特征。多因素logistic回归分析探讨不同变量对院内死亡率、机械通气及肾脏替代治疗等临床结局的影响;绘制两组患者的28天Kaplan-Meier生存曲线,并进行log-rank检验。结果共有4 918例患者纳入分析,进行1:1 PSM后,两组均有1 895例患者,两组患者的院内死亡率及总住院时间无统计学差异。而与ED-LOS≤4 h组患者相比,>4 h组有更多的患者需要进行机械通气(29.4%vs. 39.9%,P <0.001)以及肾脏替代治疗(8.6%vs.9.8%,P=0.022)。多因素logistic回归分析提示:ED-LOS并未增加或降低院内死亡率(OR=1.016,95%CI:0.988-1.045,P=0.258);而ED-LOS降低了患者需要进行机械通气(机械通气:OR=0.912,95%CI:0.888-0.936,P=0.000)及肾脏替代治疗(OR=0.963,95%CI:0.954-0.972,P=0.021)的风险。结论 ED-LOS与脓毒症患者的院内死亡率及总住院时间并不存在相关性;但ED-LOS的延长,使得更多的脓毒症患者需要进行呼吸支持及肾脏支持治疗。展开更多
目的 构建急性药物中毒性脑病患者重症监护室(intensive care unit, ICU)住院时间延长的预测模型并评价其效能。方法 选择重症监护医疗信息集市(MIMIC)-Ⅳ2.2数据库中148例急性药物中毒性脑病患者作为研究对象,收集患者临床资料,根据IC...目的 构建急性药物中毒性脑病患者重症监护室(intensive care unit, ICU)住院时间延长的预测模型并评价其效能。方法 选择重症监护医疗信息集市(MIMIC)-Ⅳ2.2数据库中148例急性药物中毒性脑病患者作为研究对象,收集患者临床资料,根据ICU住院时间分非延长组(≤48 h)与延长组(>48 h)。采用最小绝对收缩和选择算子(LASSO)回归联合Logistic回归筛选变量,构建和绘制列线图。分别采用受试者工作特征曲线下面积(AUC)、Hosmer-Lemeshow校准曲线和决策曲线分析(DCA)评价模型的区分度、校准度及临床适用度。结果 患者ICU住院时间1~15 d,其中ICU住院时间延长69例,采取LASSO回归与Logistic回归相结合方法筛选预测变量。结果显示SOFA评分、心率、合并心血管疾病、使用机械通气4个变量为独立危险因素,依据以上预测变量构建和绘制列线图,列线图的AUC为0.837,95%CI 0.774~0.900;Bootstrap内部验证AUC 0.873,95%CI 0.817~0.930,说明该列线图预测模型具有较好的预测能力。校准曲线和Hosmer-Lemeshow检验(χ^(2)=6.392,P=0.603)均显示该模型具有较高的一致性和拟合度;DCA结果表明,患者可从模型中净获益(阈值范围0.05~1.00),具有较好的临床适用性。结论 本研究开发的模型性能良好,有助于评估急性药物中毒性脑病患者ICU住院时间的延长风险。展开更多
目的探讨系统免疫炎症指数(systemic immune inflammation index,SII)对危重病患者院内死亡风险的预测价值。方法提取美国重症监护医学信息数据库-Ⅳ(Medical Information Mart for Intensive Care-Ⅳ,MIMIC-Ⅳ)数据库中危重病患者的基...目的探讨系统免疫炎症指数(systemic immune inflammation index,SII)对危重病患者院内死亡风险的预测价值。方法提取美国重症监护医学信息数据库-Ⅳ(Medical Information Mart for Intensive Care-Ⅳ,MIMIC-Ⅳ)数据库中危重病患者的基本信息和临床资料,包括人口统计学资料、生命体征、血常规、Logistic器官功能障碍系统评分(Logistic organ dysfunction score,Lods)、牛津急性疾病严重程度评分(Oxford acute severity of illness score,Oasis)、简化急性生理评分(simplified acute physiology score,Saps-Ⅱ)、急性生理学评分-Ⅲ(acute physiology score-Ⅲ,APS-Ⅲ)、序贯器官衰竭评分(sequential organ failure score,SOFA)及结局指标,主要结局指标为院内死亡,次要结局指标为住院时长、连续性肾脏替代治疗(continuous renal replacement therapy,CRRT)、机械通气率及1年病死率。根据院内死亡事件将患者分为两组,比较组间差异。根据SII三分位进一步将患者分成3组进行组间比较,Logistic回归模型分析患者院内死亡风险。结果共计32450例危重病患者纳入研究,其中3765例发生院内死亡,病死率11.6%。①与生存组相比,死亡组患者SII更高,差异具有统计学意义(P<0.05)。②SII三分位分组(<817、817~2151、>2151)院内病死率分别为8.4%、10.2%、16.3%,差异具有统计学意义。③进一步Logistic回归模型分析显示,随着组别增加,患者的死亡风险逐渐增加(第一组为参考组,第二组OR=1.38,95%CI:1.24~1.54,第三组OR=2.03,95%CI:1.83~2.24,P<0.05)。结论SII对危重病患者院内死亡有预测价值,其简便易得可用于危重病患者的危险分层。展开更多
Background:To identify the distinct trajectories of the Sequential Organ Failure Assessment(SOFA)scores at 72 h for patients with sepsis in the Medical Information Mart for Intensive Care(MIMIC)-IV database and determ...Background:To identify the distinct trajectories of the Sequential Organ Failure Assessment(SOFA)scores at 72 h for patients with sepsis in the Medical Information Mart for Intensive Care(MIMIC)-IV database and determine their effects on mortality and adverse clinical outcomes.Methods:A retrospective cohort study was carried out involving patients with sepsis from the MIMIC-IV database.Group-based trajectory modeling(GBTM)was used to identify the distinct trajectory groups for the SOFA scores in patients with sepsis in the intensive care unit(ICU).The Cox proportional hazards regression model was used to investigate the relationship between the longitudinal change trajectory of the SOFA score and mortality and adverse clinical outcomes.Results:A total of 16,743 patients with sepsis were included in the cohort.The median survival age was 66 years(interquartile range:54-76 years).The 7-day and 28-day in-hospital mortality were 6.0%and 17.6%,respectively.Five different trajectories of SOFA scores according to the model fitting standard were determined:group 1(32.8%),group 2(30.0%),group 3(17.6%),group 4(14.0%)and group 5(5.7%).Univariate and multivariate Cox regression analyses showed that,for different clinical outcomes,trajectory group 1 was used as the reference,while trajectory groups 2-5 were all risk factors associated with the outcome(P<0.001).Subgroup analysis revealed an interaction between the two covariates of age and mechanical ventilation and the different trajectory groups of patients’SOFA scores(P<0.05).Conclusion:This approach may help identify various groups of patients with sepsis,who may be at different levels of risk for adverse health outcomes,and provide subgroups with clinical importance.展开更多
文摘目的:鉴于脓毒症的高发病率和高病死率,早期识别高风险患者并及时干预至关重要,而现有死亡风险预测模型在操作、适用性和预测长期预后等方面均存在不足。本研究旨在探讨脓毒症患者死亡的危险因素,构建近期和远期死亡风险预测模型。方法:从美国重症监护医学信息数据库IV(Medical Information Mart for Intensive Care-IV,MIMIC-IV)中选取符合脓毒症3.0诊断标准的人群,按7?3的比例随机分为建模组和验证组,分析患者的基线资料。采用单因素Cox回归分析和全子集回归确定脓毒症患者死亡的危险因素并筛选出构建预测模型的变量。分别用时间依赖性曲线下面积(area under the curve,AUC)、校准曲线和决策曲线评估模型的区分度、校准度和临床实用性。结果:共纳入14240例脓毒症患者,28 d和1年病死率分别为21.45%(3054例)和36.50%(5198例)。高龄、女性、高感染相关器官衰竭评分(sepsis-related organ failure assessment,SOFA)、高简明急性生理学评分(simplified acute physiology score II,SAPS II)、心率快、呼吸频率快、脓毒症休克、充血性心力衰竭、慢性阻塞性肺疾病、肝脏疾病、肾脏疾病、糖尿病、恶性肿瘤、高白细胞计数(white blood cell count,WBC)、长凝血酶原时间(prothrombin time,PT)、高血肌酐(serum creatinine,SCr)水平均为脓毒症死亡的危险因素(均P<0.05)。由PT、呼吸频率、体温、合并恶性肿瘤、合并肝脏疾病、脓毒症休克、SAPS II及年龄8个变量构建的模型,其28 d和1年生存的AUC分别为0.717(95%CI 0.710~0.724)和0.716(95%CI 0.707~0.725)。校准曲线和决策曲线表明该模型具有良好的校准度及较好的临床应用价值。结论:基于MIMIC-IV建立的脓毒症患者近期和远期死亡风险预测模型有较好的识别能力,对患者预后风险评估及干预治疗具有一定的临床参考意义。
文摘目的探讨平均动脉压(mean arterial pressure,MAP)变异度与重症患者的重症医学科(intensive care unit,ICU)病死率之间的关系。方法回顾性分析重症监护医学信息数据库(Medical Information Mart for Intensive Care,MIMIC)-Ⅲ中38852例入ICU的重症患者的临床资料,计算入ICU后24 h内记录的MAP的变异系数作为MAP变异度,采用一般线性回归观察入ICU 24 h内MAP变异度与重症患者ICU病死率之间的相关性,并采用受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under curve,AUC)评估MAP变异度预测重症患者ICU病死率的能力。结果入ICU 24 h的MAP变异度与ICU病死率之间有很好的相关性(R2=0.860,P<0.001),MAP变异度越大,ICU病死率越高。24h的MAP变异程度预测ICU病死率的AUC为0.61。结论重症患者入ICU 24 h内的MAP变异度与ICU病死率有很好的相关性,MAP变异度越大,ICU病死率越高;MAP变异度能够为简单快速预测危重患者的ICU病死率提供一定的信息。
基金the National Social Science Foundation of China(No.16BGL183).
文摘Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.
文摘目的 利用入住重症监护病房(intensive care unit, ICU)时的血压变异度(CV-MAP)及心率变异度(CV-HR)构建预测模型,预测ICU患者院内死亡的风险。方法 回顾性分析在美国重症监护医学信息数据库Ⅲ(medical information mart for intensive care, MIMICⅢ)中年龄≥18岁,且首次入住ICU患者的临床资料。通过多因素Logistic分析筛选危险因素并构建评分系统,采用受试者工作特征(receiver operator characteristic, ROC)曲线和校准曲线评估模型区分度和校准度,采用临床决策曲线评估模型实际应用价值。结果 共筛选符合标准的患者38 824例,院内死亡患者4075例(住院病死率为10.5%)。从危险因素中选择年龄、是否合并肝脏疾病、是否合并血液系统恶性肿瘤、是否合并转移癌、住院类型、24 h心率变异系数、24 h血压变异系数、是否使用血管活性药、是否接受镇痛治疗、是否接受镇静治疗、是否接受有创机械通气构建简化预测模型。模型预测院内死亡的ROC曲线下面积(AUC)为0.743(95%CI 0.735~0.750,P<0.001),Hosmer-Lemeshow检验χ^(2)=4.978,P=0.083。使用Bootstrap法进行1000次重复采样进行内部验证,校正曲线判断预测值与实际值一致性较好。决策曲线分析提示,在高阈值风险0.1~0.6时,预测模型具有较高的实用价值。结论 基于CV-MAP及CV-HR建立ICU患者院内死亡风险预测模型具有较好的临床预测价值,有助于识别高危患者。
文摘目的:研究血红蛋白-红细胞分布宽度比值(hemoglobin/red blood cell distribution width ratio,HRR)与冠脉搭桥手术(coronary artery bypass grafting,CABG)后急性肾损伤(acute kidney injury,AKI)发生风险的相关性。方法:选择重症监护数据集(Medical Information Mart for Intensive Care Database,MIMIC)-Ⅳ中所有CABG术后患者作为研究对象,根据是否发生AKI分为2组。比较分析2组一般资料,并将有统计学差异的变量纳入logistic单因素回归分析,以单因素分析中P<0.05的变量纳入多因素logistic回归分析。Logistic回归模型评估HRR对CABG术后AKI风险的预测价值。亚组分析采用层次回归模型。结果:共5 623例患者纳入研究,AKI组4 342例,非AKI组1 281例。AKI组患者入院时HRR水平明显低于非AKI组(P<0.001)。多因素回归模型显示HRR是预测CABG术后AKI风险的独立危险因子(OR=0.92,95%CI=0.88~0.96,P<0.001)。Logistic回归模型发现,在模型Ⅲ中(调整潜在混杂因素),HRR水平降低仍然是CABG术后发生AKI的独立影响因素。亚组分析发现HRR与AKI发生的相关性在大多数协变量中相似。结论:低HRR水平是CABG术后AKI发生风险的独立危险因素。
文摘目的评价急诊滞留时间(Emergency Department Length of Stay,ED-LOS)与需要入住重症监护病房(Intensive Care Unit,ICU)的脓毒症患者预后之间的关系。方法此回顾性队列研究从重症监护医学信息数据库(Medical Information Mark for Intensive Care,MIMIC Ⅲ)提取出需要从急诊科(Emergency Department,ED)直接转入ICU的脓毒症患者的相关信息,以ED-LOS是否大于4 h将研究人群分成两组,比较两组患者结局指标的差异。倾向性评分匹配(Propensity Score Matching,PSM)用于平衡两组间的基线特征。多因素logistic回归分析探讨不同变量对院内死亡率、机械通气及肾脏替代治疗等临床结局的影响;绘制两组患者的28天Kaplan-Meier生存曲线,并进行log-rank检验。结果共有4 918例患者纳入分析,进行1:1 PSM后,两组均有1 895例患者,两组患者的院内死亡率及总住院时间无统计学差异。而与ED-LOS≤4 h组患者相比,>4 h组有更多的患者需要进行机械通气(29.4%vs. 39.9%,P <0.001)以及肾脏替代治疗(8.6%vs.9.8%,P=0.022)。多因素logistic回归分析提示:ED-LOS并未增加或降低院内死亡率(OR=1.016,95%CI:0.988-1.045,P=0.258);而ED-LOS降低了患者需要进行机械通气(机械通气:OR=0.912,95%CI:0.888-0.936,P=0.000)及肾脏替代治疗(OR=0.963,95%CI:0.954-0.972,P=0.021)的风险。结论 ED-LOS与脓毒症患者的院内死亡率及总住院时间并不存在相关性;但ED-LOS的延长,使得更多的脓毒症患者需要进行呼吸支持及肾脏支持治疗。
文摘目的探讨系统免疫炎症指数(systemic immune inflammation index,SII)对危重病患者院内死亡风险的预测价值。方法提取美国重症监护医学信息数据库-Ⅳ(Medical Information Mart for Intensive Care-Ⅳ,MIMIC-Ⅳ)数据库中危重病患者的基本信息和临床资料,包括人口统计学资料、生命体征、血常规、Logistic器官功能障碍系统评分(Logistic organ dysfunction score,Lods)、牛津急性疾病严重程度评分(Oxford acute severity of illness score,Oasis)、简化急性生理评分(simplified acute physiology score,Saps-Ⅱ)、急性生理学评分-Ⅲ(acute physiology score-Ⅲ,APS-Ⅲ)、序贯器官衰竭评分(sequential organ failure score,SOFA)及结局指标,主要结局指标为院内死亡,次要结局指标为住院时长、连续性肾脏替代治疗(continuous renal replacement therapy,CRRT)、机械通气率及1年病死率。根据院内死亡事件将患者分为两组,比较组间差异。根据SII三分位进一步将患者分成3组进行组间比较,Logistic回归模型分析患者院内死亡风险。结果共计32450例危重病患者纳入研究,其中3765例发生院内死亡,病死率11.6%。①与生存组相比,死亡组患者SII更高,差异具有统计学意义(P<0.05)。②SII三分位分组(<817、817~2151、>2151)院内病死率分别为8.4%、10.2%、16.3%,差异具有统计学意义。③进一步Logistic回归模型分析显示,随着组别增加,患者的死亡风险逐渐增加(第一组为参考组,第二组OR=1.38,95%CI:1.24~1.54,第三组OR=2.03,95%CI:1.83~2.24,P<0.05)。结论SII对危重病患者院内死亡有预测价值,其简便易得可用于危重病患者的危险分层。
文摘Background:To identify the distinct trajectories of the Sequential Organ Failure Assessment(SOFA)scores at 72 h for patients with sepsis in the Medical Information Mart for Intensive Care(MIMIC)-IV database and determine their effects on mortality and adverse clinical outcomes.Methods:A retrospective cohort study was carried out involving patients with sepsis from the MIMIC-IV database.Group-based trajectory modeling(GBTM)was used to identify the distinct trajectory groups for the SOFA scores in patients with sepsis in the intensive care unit(ICU).The Cox proportional hazards regression model was used to investigate the relationship between the longitudinal change trajectory of the SOFA score and mortality and adverse clinical outcomes.Results:A total of 16,743 patients with sepsis were included in the cohort.The median survival age was 66 years(interquartile range:54-76 years).The 7-day and 28-day in-hospital mortality were 6.0%and 17.6%,respectively.Five different trajectories of SOFA scores according to the model fitting standard were determined:group 1(32.8%),group 2(30.0%),group 3(17.6%),group 4(14.0%)and group 5(5.7%).Univariate and multivariate Cox regression analyses showed that,for different clinical outcomes,trajectory group 1 was used as the reference,while trajectory groups 2-5 were all risk factors associated with the outcome(P<0.001).Subgroup analysis revealed an interaction between the two covariates of age and mechanical ventilation and the different trajectory groups of patients’SOFA scores(P<0.05).Conclusion:This approach may help identify various groups of patients with sepsis,who may be at different levels of risk for adverse health outcomes,and provide subgroups with clinical importance.
