脓毒症急性肾损伤短期预后个体化预测模型列线图的建立及验证

目的分析脓毒症并发急性肾损伤患者(SA-AKI)短期不良预后的影响因素,建立个体化预测SA-AKI患者短期不良预后风险的列线图模型。方法选取2017年1月1日至2019年6月1日重庆市梁平区人民医院急诊重症监护室收治的SA-AKI患者共363例为研究对象,通过电话或门诊复查等方式随访,并根据患者30 d内预后分为存活组(251例)和死亡组(112例)。采用单因素和多因素Logistic回归分析SA-AKI患者30 d死亡的影响因素,利用R软件建立预测SA-AKI短期不良预后的列线图模型。结果单因素分析显示年龄(Z=-3.268、P=0.005)、真菌感染(χ^2=12.318、P=0.002)、APACHEⅡ得分(t=9.587、P=0.001)、SOFA得分(t=6.328、P=0.001)、未及时诊断AKI(χ^2=12.587、P=0.001)、抗凝血酶Ⅲ(Z=-8.332、P=0.001)和AKI分期(Z=-7.009、P=0.001)7个因素可能是脓毒症急并发性肾损伤患者在30 d内死亡的相关危险因素。多因素Logistic回归分析显示:年龄(65~79岁:P=0.041、OR=3.573、95%CI:1.054~12.112;>79岁:P=0.028、OR=3.681、95%CI:1.215~11.984)、真菌感染(P=0.018、OR=4.684、95%CI:1.356~15.367)、APACHEⅡ评分(P=0.001、OR=1.265、95%CI:1.164~1.532)、SOFA评分(P=0.001、OR=1.291、95%CI:1.117~1.532)、抗凝血酶-Ⅲ(60%~80%:P=0.001、OR=1.291、95%CI:1.117~1.532;<60%:P=0.001、OR=15.329、95%CI:4.387~55.321)和AKI分期(2期:P=0.022、OR=2.998、95%CI:1.039~8.325;3期:P=0.001、OR=9.367、95%CI:5.235~22.327)均为SA-AKI患者30 d内死亡的独立影响因素。对列线图模型进行验证,其初始一致性指数(C-index)为0.943,经1000次的模型内部验证后一致性指数(C-index)为0.945,校正曲线显示该列线图模型具有良好的区分度及一致性,可为SA-AKI患者的预后评估提供针对性指导。结论基于年龄、真菌感染、APACHEⅡ评分、SOFA评分、抗凝血酶-Ⅲ和AKI分期6个因素为SA-AKI的独立影响因素,构建个体化预测SA患者并发KAI风险的列线图模型,可较为准确地预测SA-AKI患者短期不良预后发生的风险。

Establishment and analysis of the prognostic nomogram for sepsis-associated acute kidney injury in short-term

ObjectiveTo investigate the influencing factors of short-term adverse prognosis in patients with sepsis complicated with acute kidney injury (SA-AKI), and to establish a personalized line chart model to predict the short-term adverse prognosis risk of patients with SA-AKI. MethodsTotal of 363 patients with sepsis who were admitted to the Emergency Intensive Care Unit of Chongqing Liangping District People’s Hospital from January 1st, 2017 to June 1st, 2019 were collected; Follow-ups were conducted by telephone or outpatient review, and patients were divided into survival group (251 cases) and death group (112 cases) based on their prognosis within 30 days. Single-factor and multi-factor Logistic regression were used to analyze the influencing factors of SA-AKI patients’ death within 30-day, and R software was used to establish a line chart model to predict the short-term adverse prognosis of SA-AKI. ResultsUnivariate analysis showed that age (Z =-3.268, P = 0.005), fungal infection (χ² = 12.318, P = 0.002), APACHE Ⅱ score (t = 9.587, P = 0.001), SOFA score (t = 6.328, P = 0.001), AKI failure to diagnose in time (χ² = 12.587, P = 0.001), antithrombin Ⅲ (Z =-8.332, P = 0.001) and AKI (Z =-7.009, P = 0.00) stage may be associated risk factors for death in patients with acute concurrent renal injury in sepsis within 30 d. Multivariate Logistic regression analysis showed that age (65-79 years old: P = 0.041, OR = 3.573, 95%CI: 1.054-12.112; > 79 years old: P = 0.028, OR = 3.681, 95%CI: 1.215-11.984), fungal infection (P = 0.018, OR = 4.684, 95%CI: 1.356-15.367), APACHEⅡ score (P = 0.001, OR = 1.265, 95%CI: 1.164-1.532), SOFA score (P = 0.001, OR = 1.291, 95%CI: 1.117-1.532), antithrombin-Ⅲ (60%~80%: P = 0.001, OR = 1.291, 95%CI: 1.117-1.532; < 60%: P = 0.001, OR = 15.329, 95%CI: 4.387-55.321), and AKI stage (2 period: P = 0.022, OR = 2.998, 95%CI: 1.039-8.325; 3 period: P = 0.001, OR = 9.367, 95%CI: 5.235-22.327) were all independent related factors for death within 30 days of patients with SA-AKI. The collinear map model validation showed that its initial consistency index (C-index) was 0.943; After 1 000 internal verifications of the model, the consistency index (C-index) was 0.945. The calibration curve showed that the collinear map model had a good differentiation and consistency, which could provide targeted guidance for prognosis evaluation of patients with SA-AKI. ConclusionsThis study was based on 6 independent influence factors including age, fungal infection, APACHE Ⅱ score, SOFA score, antithrombin-Ⅲ and AKI stage, to construct an individualized line map model to predict the concurrent KAI risk of patients with SA, which could accurately predict the risk of short-term adverse prognosis of patients with SA-AKI.