AIM: To examine the feasibility and validity of electronic generation of quality metrics in the intensive care unit(ICU).METHODS: This minimal risk observational study was performed at an academic tertiary hospital. T...AIM: To examine the feasibility and validity of electronic generation of quality metrics in the intensive care unit(ICU).METHODS: This minimal risk observational study was performed at an academic tertiary hospital. The Critical Care Independent Multidisciplinary Program at Mayo Clinic identified and defined 11 key quality metrics. These metrics were automatically calculated using ICU Data Mart, a near-real time copy of all ICU electronic medical record(EMR) data. The automatic report was compared with data from a comprehensive EMR review by a trained investigator. Data was collected for 93 randomly selected patients admitted to the ICU during April 2012(10% of admitted adult population). This study was approved by the Mayo Clinic Institution Review Board.RESULTS: All types of variables needed for metric calculations were found to be available for manual and electronic abstraction, except information for availability of free beds for patient-specific time-frames. There was 100% agreement between electronic and manual data abstraction for ICU admission source, admission service, and discharge disposition. The agreement between electronic and manual data abstraction of the time of ICU admission and discharge were 99% and 89%. The time of hospital admission and discharge were similar for both the electronically and manually abstracted datasets. The specificity of the electronically-generated report was 93% and 94% for invasive and non-invasive ventilation use in the ICU. One false-positive result for each type of ventilation was present. The specificity for ICU and in-hospital mortality was 100%. Sensitivity was 100% for all metrics.CONCLUSION: Our study demonstrates excellent accuracy of electronically-generated key ICU quality metrics. This validates the feasibility of automatic metric generation.展开更多
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
Clinical decision support(CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors(ME) and adverse drug events(ADEs). Critically ill patients a...Clinical decision support(CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors(ME) and adverse drug events(ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.展开更多
目的建立脓毒症相关性脑病(sepsis-associated encephalopathy,SAE)患者的死亡风险预测模型并进行验证。方法提取2008—2019年重症医学信息数据库(Medical Information Mart for Intensive Care-Ⅳ,MIMIC-Ⅳ)中2767例SAE患者的临床数据...目的建立脓毒症相关性脑病(sepsis-associated encephalopathy,SAE)患者的死亡风险预测模型并进行验证。方法提取2008—2019年重症医学信息数据库(Medical Information Mart for Intensive Care-Ⅳ,MIMIC-Ⅳ)中2767例SAE患者的临床数据作为训练集,构建死亡风险预测模型,并对其预测准确性及拟合优度进行评价。回顾性收集2018年6月至2021年6月合肥市第二人民医院重症医学科的134例SAE患者临床数据纳入验证集,代入预测模型进行外部验证。结果多因素Logistic回归分析显示,年龄、使用血管活性药物、格拉斯哥昏迷评分、国际标准化比值、动脉血氧分压、血乳酸、血尿素氮、白蛋白是SAE患者院内死亡的独立影响因素(P<0.05)。使用影响因素构建预测模型,受试者操作特征(receiver operator characteristic,ROC)曲线下面积为0.765,95%置信区间为74.45%~78.53%,Hosmer-Lemeshow检验显示预测模型的拟合优度良好(χ^(2)=13.090,P=0.109)。将预测模型代入验证集,敏感度为80.00%,特异性为61.60%,准确性为76.87%,ROC曲线下面积为0.757,提示模型外部数据验证中区分度良好。结论构建的预测模型可用于预测SAE患者的院内死亡风险,有助于临床及时调整治疗策略,降低死亡率,改善预后。展开更多
目的构建可预测心脏骤停患者住院期间死亡风险的机器学习模型,并对其进行解释。方法提取美国重症监护医学信息数据库Ⅳ(Medical Information Mart for Intensive Care databaseⅣ,MIMIC-Ⅳ)2.0中心脏骤停患者转入ICU 24 h内首次临床资...目的构建可预测心脏骤停患者住院期间死亡风险的机器学习模型,并对其进行解释。方法提取美国重症监护医学信息数据库Ⅳ(Medical Information Mart for Intensive Care databaseⅣ,MIMIC-Ⅳ)2.0中心脏骤停患者转入ICU 24 h内首次临床资料及住院期间转归,基于机器学习算法构建6种可预测心脏骤停患者院内死亡风险的模型,包括XGBoost模型、轻量级梯度提升机(light gradient boosting machine,LGBM)模型、决策树(decision tree,DT)模型、K近邻(K-nearest neighbor,KNN)模型、Logistic回归模型、随机森林(random forest,RF)模型。采用受试者操作特征(receiver operator characteristic,ROC)曲线、临床决策曲线及校准曲线对模型进行评价,并采用Shapley加性解释(Shapley additive explanation,SHAP)算法评估不同临床特征对最优模型的影响,以增加模型的可解释性。结果共1465例符合纳入与排除标准的心脏骤停患者入选本研究。其中住院期间存活773例、死亡692例。经筛选,共纳入82个临床特征用于机器学习模型构建。模型评价结果显示,相较于其余5种模型,LGBM模型预测心脏骤停患者院内死亡的曲线下面积(area under the curve,AUC)更高[0.834(95%CI:0.688~0.894)],且相对于Logistic回归模型、XGBoost模型,其对死亡风险的预测准确性更高(校准度:0.166),临床决策性能更优,整体性能最佳。SHAP算法分析显示,对LGBM模型输出结果影响最大的3个临床特征分别为格拉斯哥睁眼反应评分、碳酸氢盐水平、白细胞计数。结论基于大型公共医疗卫生数据库建立的可预测心脏骤停患者住院期间死亡风险的机器学习模型中,LGBM模型性能最优,其可辅助临床进行更高效的疾病管理和更精准的医疗干预。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
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
文摘AIM: To examine the feasibility and validity of electronic generation of quality metrics in the intensive care unit(ICU).METHODS: This minimal risk observational study was performed at an academic tertiary hospital. The Critical Care Independent Multidisciplinary Program at Mayo Clinic identified and defined 11 key quality metrics. These metrics were automatically calculated using ICU Data Mart, a near-real time copy of all ICU electronic medical record(EMR) data. The automatic report was compared with data from a comprehensive EMR review by a trained investigator. Data was collected for 93 randomly selected patients admitted to the ICU during April 2012(10% of admitted adult population). This study was approved by the Mayo Clinic Institution Review Board.RESULTS: All types of variables needed for metric calculations were found to be available for manual and electronic abstraction, except information for availability of free beds for patient-specific time-frames. There was 100% agreement between electronic and manual data abstraction for ICU admission source, admission service, and discharge disposition. The agreement between electronic and manual data abstraction of the time of ICU admission and discharge were 99% and 89%. The time of hospital admission and discharge were similar for both the electronically and manually abstracted datasets. The specificity of the electronically-generated report was 93% and 94% for invasive and non-invasive ventilation use in the ICU. One false-positive result for each type of ventilation was present. The specificity for ICU and in-hospital mortality was 100%. Sensitivity was 100% for all metrics.CONCLUSION: Our study demonstrates excellent accuracy of electronically-generated key ICU quality metrics. This validates the feasibility of automatic metric generation.
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。
基金Supported by The Agency for Healthcare Research and Quality,No.R18HS02420-01
文摘Clinical decision support(CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors(ME) and adverse drug events(ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.
文摘目的建立脓毒症相关性脑病(sepsis-associated encephalopathy,SAE)患者的死亡风险预测模型并进行验证。方法提取2008—2019年重症医学信息数据库(Medical Information Mart for Intensive Care-Ⅳ,MIMIC-Ⅳ)中2767例SAE患者的临床数据作为训练集,构建死亡风险预测模型,并对其预测准确性及拟合优度进行评价。回顾性收集2018年6月至2021年6月合肥市第二人民医院重症医学科的134例SAE患者临床数据纳入验证集,代入预测模型进行外部验证。结果多因素Logistic回归分析显示,年龄、使用血管活性药物、格拉斯哥昏迷评分、国际标准化比值、动脉血氧分压、血乳酸、血尿素氮、白蛋白是SAE患者院内死亡的独立影响因素(P<0.05)。使用影响因素构建预测模型,受试者操作特征(receiver operator characteristic,ROC)曲线下面积为0.765,95%置信区间为74.45%~78.53%,Hosmer-Lemeshow检验显示预测模型的拟合优度良好(χ^(2)=13.090,P=0.109)。将预测模型代入验证集,敏感度为80.00%,特异性为61.60%,准确性为76.87%,ROC曲线下面积为0.757,提示模型外部数据验证中区分度良好。结论构建的预测模型可用于预测SAE患者的院内死亡风险,有助于临床及时调整治疗策略,降低死亡率,改善预后。
文摘目的构建可预测心脏骤停患者住院期间死亡风险的机器学习模型,并对其进行解释。方法提取美国重症监护医学信息数据库Ⅳ(Medical Information Mart for Intensive Care databaseⅣ,MIMIC-Ⅳ)2.0中心脏骤停患者转入ICU 24 h内首次临床资料及住院期间转归,基于机器学习算法构建6种可预测心脏骤停患者院内死亡风险的模型,包括XGBoost模型、轻量级梯度提升机(light gradient boosting machine,LGBM)模型、决策树(decision tree,DT)模型、K近邻(K-nearest neighbor,KNN)模型、Logistic回归模型、随机森林(random forest,RF)模型。采用受试者操作特征(receiver operator characteristic,ROC)曲线、临床决策曲线及校准曲线对模型进行评价,并采用Shapley加性解释(Shapley additive explanation,SHAP)算法评估不同临床特征对最优模型的影响,以增加模型的可解释性。结果共1465例符合纳入与排除标准的心脏骤停患者入选本研究。其中住院期间存活773例、死亡692例。经筛选,共纳入82个临床特征用于机器学习模型构建。模型评价结果显示,相较于其余5种模型,LGBM模型预测心脏骤停患者院内死亡的曲线下面积(area under the curve,AUC)更高[0.834(95%CI:0.688~0.894)],且相对于Logistic回归模型、XGBoost模型,其对死亡风险的预测准确性更高(校准度:0.166),临床决策性能更优,整体性能最佳。SHAP算法分析显示,对LGBM模型输出结果影响最大的3个临床特征分别为格拉斯哥睁眼反应评分、碳酸氢盐水平、白细胞计数。结论基于大型公共医疗卫生数据库建立的可预测心脏骤停患者住院期间死亡风险的机器学习模型中,LGBM模型性能最优,其可辅助临床进行更高效的疾病管理和更精准的医疗干预。
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。
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。
