首页|基于液体复苏完成时间及液体负平衡量建立脓毒性休克患者预后预测模型

基于液体复苏完成时间及液体负平衡量建立脓毒性休克患者预后预测模型

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目的 探究液体复苏完成时间及液体负平衡量与脓毒性休克患者预后的关系,尝试构建基于液体复苏完成时间及液体负平衡量的预测模型,并验证该模型对脓毒性休克患者预后的预测效能。方法 选择无锡市人民医院 2020 年 4 月至 2023 年 4 月收治的脓毒性休克患者,记录患者一般资料(性别、年龄、体质量指数、感染部位)、入院时病理学指标、入院时与液体复苏24h后急性生理学与慢性健康状况评分Ⅱ(APACHEⅡ)差值和序贯器官衰竭评分(SOFA)差值,以及液体复苏完成时间和液体负平衡量。采用多因素Logistic分析筛查脓毒性休克患者预后的影响因素,并建立列线图模型。采用Bootstrap法对模型进行内部验证;采用一致性指数、校准曲线、受试者工作特征曲线(ROC曲线)评估模型的准确度和预测效能。结果 共纳入 96 例脓毒性休克患者,28 d死亡38例,存活58例。与生存组比较,死亡组入院时与液体复苏24h后APACHEⅡ评分差值、SOFA评分差值及 1~3 h完成液体复苏比例、每日液体负平衡量-500~-250 mL比例更低,差异均有统计学意义(均P<0。05)。多因素Logistic分析显示,液体复苏完成时间是脓毒性休克患者预后的危险因素[优势比(OR)=26。285,95%可信区间(95%CI)为 9。984~76。902,P<0。05],入院时与液体复苏 24h后APACHEⅡ评分差值(OR=0。045,95%CI为 0。015~0。131)、SOFA评分差值(OR=0。056,95%CI为 0。019~0。165)和液体负平衡量(OR=0。043,95%CI为 0。015~0。127)是脓毒性休克患者预后的保护因素(均P<0。05)。模型验证结果显示,一致性指数为 0。681(95%CI为 0。596~0。924),表明区分度良好。校准曲线显示,校准曲线与理想曲线拟合度良好。ROC曲线显示,列线图模型预测脓毒性休克患者死亡的敏感度为 83。7%,特异度为 97。2%,ROC曲线下面积(AUC)为 0。931(95%CI为 0。846~0。985),表明模型预测效能较好。结论 液体复苏完成时间和液体负平衡量与脓毒性休克患者的预后有关,且列线图模型有助于提高对脓毒性休克患者死亡风险的甄别。
Establishing a prognostic prediction model for patients with septic shock based on the completion time of fluid resuscitation and the negative fluid balance volumes
Objective To explore the relationship between the completion time of fluid resuscitation as well as negative fluid balance volumes and the prognosis of patients with septic shock,and to try to construct a prediction model based on the completion time of fluid resuscitation and negative fluid balance volumes,and to verify the predictive efficacy of the model on the prognosis of patients with septic shock.Methods Patients with septic shock admitted to Wuxi People's Hospital from April 2020 to April 2023 were selected.The general data(gender,age,body mass index,infection site),pathological indicators on admission,the difference of acute physiology and chronic health evaluationⅡ(APACHEⅡ)and sequential organ failure assessment(SOFA)between admission and 24 hours after fluid resuscitation,the completion time of fluid resuscitation and negative fluid balance volume were recorded.Multivariate Logistic analysis was used to screen the influencing factors of the prognosis of patients with septic shock,and a nomogram model was established.Bootstrap method was used for internal validation of the model.The consistency index,calibration curve and receiver operator characteristic curve(ROC curve)were used to evaluate the accuracy and prediction efficiency of the model.Results A total of 96 patients with septic shock were enrolled,38 patients died and 58 patients survived at 28 days.Compared with the survival group,the difference of APACHEⅡscore,SOFA score,the proportion of fluid resuscitation completed within 1 to 3 hours,and the proportion of negative fluid balance volume-500 to-250 mL per day in the death group were lower,and the differences were statistically significant(all P<0.05).Multivariate Logistic analysis showed that the completion time of fluid resuscitation was a risk factor for the prognosis of patients with septic shock[odds ratio(OR)= 26.285,95%confidence interval(95%CI)was 9.984-76.902,P<0.05].The difference of APACHEⅡscore(OR = 0.045,95%CI was 0.015-0.131),SOFA score(OR = 0.056,95%CI was 0.019-0.165)between admission and 24 hours after fluid resuscitation,and negative fluid balance volume(OR=0.043,95%CIwas 0.015-0.127)were protective factors for the prognosis of patients with septic shock(all P<0.05).The model validation results showed that the consistency index was 0.681(95%CI was 0.596-0.924),indicating good discrimination.The calibration curve showed that the calibration curve fitted well with the ideal curve.The ROC curve showed that the sensitivity of the nomogram model for predicting the death of patients with septic shock was 83.7%,the specificity was 97.2%,and the area under the ROC curve(AUC)was 0.931(95%CI was 0.846-0.985),indicating that the model had good prediction efficiency.Conclusion The completion time of fluid resuscitation and negative fluid balance volumes are related to the prognosis of septic shock patients,and the alignment diagram model improve the identification of the risk of death in septic shock patients.

Septic shockLiquid resuscitationNegative balanceAlignment diagramPrediction model

钱际银、张晶

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南京医科大学附属无锡人民医院急诊科,江苏无锡 214023

脓毒性休克 液体复苏 负平衡量 列线图 预测模型

江苏省医院协会医院急诊管理专项研究项目

JSYGY-2-2021-JZ56

2024

中华危重病急救医学
中华医学会

中华危重病急救医学

CSTPCD北大核心
影响因子:3.049
ISSN:2095-4352
年,卷(期):2024.36(3)
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