摘要
目的构建预测脓毒症肝损伤(SALD)老年患者入院30 d内死亡的列线图模型并验证,以识别高危人群并改善预后。方法采用回顾性队列研究方法,分析从美国重症监护医学信息数据库Ⅳ(MIMIC-Ⅳ)中提取2008至2019年首次入住贝斯以色列女执事医疗中心重症监护病房(ICU)且诊断为SALD的老年患者数据,包括基本信息、疾病严重程度评分、基础疾病、感染灶、24 h生命体征、实验室指标初始值、24 h并发症以及预后相关指标。患者按7:3的比例随机分配至建模组和验证组,建模组采用LASSO回归法及Logistic回归分析筛选30 d死亡的独立危险因素。构建列线图预测模型,绘制受试者工作特征曲线(ROC曲线)、校准曲线和决策曲线(DCA)对模型进行评估,并使用验证组对模型进行验证。结果共630例SALD老年患者纳入研究,其中建模组441例,验证组189例。建模组的牛津急性疾病严重程度评分〔OASIS;优势比(OR)=1.060,95%置信区间(95%CI)为1.034~1.086〕、24 h脉搏血氧饱和度(SpO2;OR=0.876,95%CI为0.797~0.962)、初始红细胞平均体积(MCV;OR=1.043,95%CI为1.009~1.077)、初始红细胞分布宽度(RDW;OR=1.237,95%CI为1.123~1.362)、初始血糖(OR=1.008,95%CI为1.004~1.013)和初始天冬氨酸转氨酶(AST;OR=1.000,95%CI为1.000~1.001)是患者30 d死亡的独立危险因素(均P<0.05)。基于上述变量构建列线图模型,ROC曲线显示,建模组中该模型的曲线下面积(AUC)=0.757(95%CI为0.712~0.803),敏感度为65.05%,特异度为74.90%;验证组中该模型的AUC=0.712(95%CI为0.631~0.792),敏感度为58.67%,特异度为81.58%。建模组和验证组的校准曲线显示,拟合曲线与标准曲线均接近;Hosmer-Lemeshow检验显示,建模组χ^(2)=6.729、P=0.566,验证组χ^(2)=13.889、P=0.085,提示该模型能很好地拟合观察数据。DCA曲线显示,当建模组阈值概率为16%~94%,验证组阈值概率为27%~99%时,该模型的净获益较好。结论OASIS评分、24 h SpO2、初始MCV、初始RDW、初始血糖和初始AST是SALD老年患者30 d死亡的独立危险因素,基于上述变量构建的列线图具有较好的预测效能。
Objective To develop and validate a nomogram model for predicting 30-day mortality among elderly patients with sepsis-associated liver dysfunction(SALD),to identify high-risk patients and improve prognosis.Methods A retrospective cohort study was conducted using data extracted from the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database for elderly patients with SALD who were first admitted to the intensive care unit(ICU)of Beth Israel Deaconess Medical Center between 2008 and 2019,including basic characteristics,severity scores,underlying diseases,infection foci,24-hour vital signs,initial laboratory indicators,24-hour complications,and prognosis related indicators.Patients were randomly assigned to training group and validation group in a ratio of 7:3.The training group used the LASSO regression analysis,as well as multivariate Logistic regression analysis to screen for independent risk factors for 30-day mortality.A nomogram prediction model was constructed,and receiver operator characteristic curve(ROC curve),calibration curves,and decision curve analysis(DCA)were used to evaluate the model,and validate the model using the validation cohort.Results A total of 630 elderly patients with SLAD were included in the study,including 441 in the training group and 189 in the validation group.Oxford acute severity of illness score(OASIS)for training group[odds ratio(OR)=1.060,95%confidence interval(95%CI)was 1.034-1.086],24-hour pulse oxygen saturation(SpO_(2);OR=0.876,95%CI was 0.797-0.962),initial mean corpuscular volume(MCV;OR=1.043,95%CI was 1.009-1.077),initial red blood cell distribution width(RDW;OR=1.237,95%CI was 1.123-1.362),initial blood glucose(OR=1.008,95%CI was 1.004-1.013),and initial aspartate aminotransferase(AST;OR=1.000,95%CI was 1.000-1.001)were independent risk factors for 30-day mortality in patients(all P<0.05).Based on the above variables,a nomogram model was constructed,and the ROC curve showed that the area under the curve(AUC)of the model in the training group was 0.757(95%CI was 0.712-0.803),with a sensitivity of 65.05%and a specificity of 74.90%;the AUC of the model in the validation group was 0.712(95%CI was 0.631-0.792),with a sensitivity of 58.67%and a specificity of 81.58%.The calibration curves of the training and validation groups show that both the fitted curves were close to the standard curves.The Hosmer-Lemeshow test:the training group(χ^(2)=6.729,P=0.566),the validation group(χ^(2)=13.889,P=0.085),indicating that the model can fit the observed data well.The DCA curve shows that when the threshold probability of the training group was 16%to 94%and the threshold probability of the validation group was 27%to 99%,the net benefit of the model was good.Conclusions OASIS,24-hour SpO_(2),initial MCV,initial RDW,initial blood glucose and initial AST are independent risk factors for 30-day mortality in elderly patients with SALD.The nomogram based on these six variables demonstrates good predictive performance.
作者
张北源
贺琛哲
秦子梦
陈鸣
虞文魁
苏婷
Zhang Beiyuan;He Chenzhe;Qin Zimeng;Chen Ming;Yu Wenkui;Su Ting(Department of Critical Care Medicine,Nanjing Drum Tower Hospital,the Affiliated Hospital of Nanjing University Medical School,Nanjing 210008,China;Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University,Nanjing 210008,China)
出处
《中华危重病急救医学》
北大核心
2025年第9期802-808,共7页
Chinese Critical Care Medicine
基金
国家自然科学基金(82402553)
南京市医学科技发展资金资助项目(YKK24102)。
关键词
脓毒症肝损伤
老年
危险因素
预测模型
列线图
预后
Sepsis-associated liver dysfunction
Elderly
Risk factor
Predictive model
Nomogram
Prognosis
