切换至 "中华医学电子期刊资源库"

中华危重症医学杂志(电子版) ›› 2026, Vol. 19 ›› Issue (03) : 224 -233. doi: 10.3877/cma.j.issn.1674-6880.2026.03.005

论著

心脏术后肾损伤患者乳酸轨迹对预后的影响
林欢, 林志坚, 黄建忠, 陈凯利, 张诗函, 黄巧文()   
  1. 363000 福建漳州,福建医科大学附属漳州市医院麻醉科
  • 收稿日期:2025-11-09 出版日期:2026-06-30
  • 通信作者: 黄巧文
  • 基金资助:
    福建省自然科学基金项目(2022J011472); 福建医科大学附属漳州市医院博士工作站项目(PDB202316)

Impact of lactate trajectories on prognosis in patients with kidney injury following cardiac surgery

Huan Lin, Zhijian Lin, Jianzhong Huang, Kaili Chen, Shihan Zhang, Qiaowen Huang()   

  1. Department of Anesthesiology, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou 363000, China
  • Received:2025-11-09 Published:2026-06-30
  • Corresponding author: Qiaowen Huang
引用本文:

林欢, 林志坚, 黄建忠, 陈凯利, 张诗函, 黄巧文. 心脏术后肾损伤患者乳酸轨迹对预后的影响[J/OL]. 中华危重症医学杂志(电子版), 2026, 19(03): 224-233.

Huan Lin, Zhijian Lin, Jianzhong Huang, Kaili Chen, Shihan Zhang, Qiaowen Huang. Impact of lactate trajectories on prognosis in patients with kidney injury following cardiac surgery[J/OL]. Chinese Journal of Critical Care Medicine(Electronic Edition), 2026, 19(03): 224-233.

目的

分析心脏术后急性肾损伤(AKI)患者入住ICU后72 h内乳酸动态轨迹特征,探讨其与预后的关联。

方法

基于美国重症监护医学信息数据库Ⅳ(2008年至2022年)开展回顾性队列研究,纳入1 297例心脏术后AKI患者。采用群组轨迹模型(GBTM)识别乳酸变化轨迹,运用多因素Cox与logistic回归分析轨迹分型与住院病死率、30 d主要肾脏不良事件(MAKE30)等结局的关联,并通过受试者工作特征(ROC)曲线评估预测效能。

结果

共识别3种乳酸轨迹:低水平稳定组(743例)、部分清除组(452例)和反弹高乳酸组(102例)。与低水平稳定组相比,部分清除组与反弹高乳酸组的住院病死率、MAKE30发生率、机械通气(≥ 2 d)及ICU住院时长(≥ 7 d)均显著升高(P均< 0.05)。多因素Cox回归分析显示,反弹高乳酸组住院病死率风险为低水平稳定组的12.40倍[调整后风险比= 12.40,95%置信区间(CI)(6.04,25.46),P < 0.001]。多因素logistic回归分析表明,反弹高乳酸组发生MAKE30的风险为低水平稳定组的4.01倍[调整后比值比= 4.01,95%CI(2.19,7.35),P < 0.001]。乳酸轨迹模型预测住院死亡的曲线下面积为0.869,优于单次乳酸检测的0.835(P = 0.017)。

结论

心脏术后AKI患者早期乳酸动态轨迹是住院死亡及其他不良结局的强独立危险因素。早期密切监测乳酸变化对提示不良结局至关重要。

Objective

To analyze the dynamic lactate trajectory characteristics within 72 hours after ICU admission in patients with acute kidney injury (AKI) following cardiac surgery, and to explore their association with clinical outcomes.

Methods

A retrospective cohort study was conducted using the Medical Information Mart for Intensive Care-Ⅳ database (2008-2022), enrolling 1 297 postoperative AKI patients. Group-based trajectory modeling (GBTM) was employed to identify lactate trajectory patterns. Multivariable Cox proportional hazards regression and logistic regression models were used to analyze the association between trajectory groups and clinical outcomes including in-hospital mortality and major adverse kidney events at 30 days (MAKE30). Predictive performance was evaluated using receiver operating characteristic (ROC) curves.

Results

Three distinct lactate trajectories were identified: a low-level stable group (n = 743), a partial clearance group (n = 452), and a rebound hyperlactate group (n = 102). Compared with the low-level stable group, both the partial clearance group and the rebound hyperlactate group showed significantly higher rates of in-hospital mortality, MAKE30 incidence, mechanical ventilation lasting ≥ 2 days, and ICU stay of ≥ 7 days (all P < 0.05). Multivariate Cox regression analysis showed that the risk of in-hospital mortality in the rebound hyperlactate group was 12.40 times of the low-level stable group [adjusted hazard ratio = 12.40, 95% confidence interval (CI) (6.04, 25.46), P < 0.001]. Multivariate logistic regression analysis indicated that the risk of MAKE30 in the rebound hyperlactate group was 4.01 times of the low-level stable group [adjusted odds ratio = 4.01, 95%CI (2.19, 7.35), P < 0.001]. The lactate trajectory model demonstrated an area under the curve (AUC) of 0.869 for predicting in-hospital mortality, which was superior to that of a single lactate measurement (AUC = 0.835, P = 0.017).

Conclusions

Early dynamic lactate trajectories represent strong independent predictors of in-hospital mortality and other adverse outcomes in AKI patients with post-cardiac surgery. Early monitoring of lactate changes holds significant clinical value for identifying their adverse outcomes.

图1 不同组别心脏术后AKI患者乳酸随时间变化轨迹注:AKI.急性肾损伤
表1 基于GBTM分析生成的不同乳酸轨迹组AKI患者基线特征比较
变量 低水平稳定组(n = 743) 部分清除组(n = 452) 反弹高乳酸组(n = 102) F/χ2/Z P
人口学特征          
年龄(岁, ± s 70 ± 11 70 ± 12 64 ± 15ab 10.758 < 0.001
男性[例(%)] 511(68.8) 305(67.5) 68(66.7) 0.331 0.847
BMI(kg/m2 ± s 31 ± 7 31 ± 6 31 ± 8 1.492 0.225
生命体征          
MAP(mmHg, ± s 74 ± 7 73 ± 8 74 ± 9 1.733 0.177
SpO2(%, ± s 97.5 ± 1.6 97.7 ± 2.0 96.5 ± 3.6ab 16.904 < 0.001
实验室指标          
血糖(mmol/L, ± s 7.5 ± 1.3 8.2 ± 1.9a 8.8 ± 2.3ab 42.421 < 0.001
HCT(%, ± s 27 ± 5 25 ± 5a 26 ± 7 14.063 < 0.001
WBC(× 109/L, ± s 17 ± 6 18 ± 8a 17 ± 8 7.497 < 0.001
血钾(mmol/L, ± s 4.7 ± 0.6 4.7 ± 0.6 4.9 ± 0.8ab 5.357 < 0.001
eGFR[mL·min·(1.73m2-1 ± s] 75 ± 27 70 ± 25a 66 ± 30a 8.347 < 0.001
肌酐[μmol/L,MP25P75)] 79.6(61.9,106.1) 88.4(70.7,114.9) 106.1(79.6,141.4)ab 21.232 < 0.001
乳酸(mmol/L, ± s 2.7 ± 1.0 5.7 ± 2.4a 6.9 ± 4.5ab 415.326 < 0.001
合并症与评分          
心力衰竭[例(%)] 323(43.5) 233(51.5)a 56(54.9)a 10.001 0.007
肾脏疾病[例(%)] 218(29.3) 146(32.3) 28(27.5) 1.571 0.456
糖尿病[例(%)] 314(42.3) 144(31.9)a 34(33.3) 13.912 < 0.001
高血压[例(%)] 620(83.4) 385(85.2) 79(77.5) 3.641 0.162
脓毒症[例(%)] 438(59.0) 306(67.7)a 81(79.4)ab 21.237 < 0.001
CCI(分, ± s 6.1 ± 2.3 6.3 ± 2.3 6.1 ± 2.7 1.146 0.318
SOFA评分(分, ± s 6.6 ± 2.7 8.1 ± 3.1a 8.2 ± 3.9a 44.777 < 0.001
治疗措施          
血管活性药物[例(%)] 503(67.7) 349(77.2)a 67(65.7)b 13.751 0.001
输血[例(%)] 283(38.1) 276(61.1)a 54(52.9)a 60.938 < 0.001
输液量[mL,MP25P75)] 1 034(0,2 484) 1 378(0,3 391)a 797(-245,2 956) 6.843 0.033
利尿剂[例(%)] 348(46.8) 187(41.4) 38(37.3) 5.556 0.062
CRRT[例(%)] 44(5.9) 58(12.8)a 44(43.1)ab 126.068 < 0.001
机械通气[例(%)] 594(79.9) 396(87.6)a 75(73.5)b 16.793 < 0.001
AKI分期[例(%)]       75.943 < 0.001
1期 146(19.7) 80(17.7)a 13(12.7)ab    
2期 462(62.2) 263(58.2) 31(30.4)    
3期 135(18.2) 109(24.1) 58(56.9)    
手术类型[例(%)]       20.453 < 0.001
CABG类 372(50.1) 178(39.4)a 39(38.2)a    
瓣膜类 225(30.3) 160(35.4) 29(28.4)    
其它类 146(19.7) 114(25.2) 34(33.3)    
表2 基于GBTM分析生成的不同乳酸轨迹组AKI患者的临床结局比较
表3 心脏术后AKI患者GBTM模型轨迹参数(n = 1 297)
图2 Boruta算法筛选心脏术后AKI患者住院死亡预测因子的重要性排序注:AKI.急性肾损伤;SpO2.血氧饱和度;SOFA.序贯器官衰竭评估;HCT.红细胞压积;CCI. Charlson合并症指数;CRRT.连续性肾脏替代疗法;纵坐标为变量重要性评分,分值越高表示该变量对住院死亡结局的影响越大;横坐标为候选变量;图中shadowMin、shadowMean、shadowMax分别代表随机生成的影子变量的最小、平均和最大重要性阈值;绿色代表具有重要影响的变量;红色代表重要性较低的变量;蓝色代表随机生成的"影子"变量,用于确定重要性的基准阈值;图中单独标注的圆圈代表离群值
表4 心脏术后AKI患者住院病死率的单因素logistic回归分析
图3 不同乳酸轨迹组心脏术后AKI患者28 d住院生存曲线注:AKI.急性肾损伤;曲线上的"+"标记符号代表删失
表5 不同乳酸轨迹对心脏术后AKI患者住院病死率的多因素Cox回归分析
表6 不同乳酸轨迹对心脏术后AKI患者发生MAKE30多因素logistic回归分析
表7 不同乳酸轨迹心脏术后AKI患者ICU病死率的Cox回归分析
表8 不同乳酸轨迹组心脏术后AKI患者其它临床结局的多因素logistic回归分析
图4 乳酸轨迹模型与单次乳酸对心脏术后AKI患者住院死亡预测的ROC曲线比较注:AKI.急性肾损伤;ROC.受试者工作特征
表9 乳酸轨迹对心脏术后AKI患者住院死亡风险的亚组分析
亚组 总数 事件数 HR(95%CI 交互P
年龄       0.508
<65岁        
部分清除组 138 8 2.09(0.55 ~ 7.97)  
反弹高乳酸组 45 15 6.39(1.79 ~ 22.74)  
≥ 65岁        
部分清除组 314 24 2.94(1.23 ~ 7.05)  
反弹高乳酸组 57 25 17.22(6.98 ~ 42.47)  
性别       0.068
女性        
部分清除组 147 9 1.96(0.48 ~ 7.91)  
反弹高乳酸组 34 16 26.66(6.93 ~ 102.52)  
男性        
部分清除组 305 23 3.17(1.38 ~ 7.28)  
反弹高乳酸组 68 24 9.37(3.92 ~ 22.37)  
SOFA评分       0.811
<6分        
部分清除组 128 8 3.67(1.01 ~ 13.25)  
反弹高乳酸组 29 7 45.32(8.47 ~ 242.46)  
≥6分        
部分清除组 324 24 2.70(1.14 ~ 6.43)  
反弹高乳酸组 73 33 11.76(4.86 ~ 28.44)  
AKI分期       0.454
1期        
部分清除组 80 3 1.75(0.18 ~ 17.39)  
反弹高乳酸组 13 5 18.03(1.76 ~ 184.21)  
2期        
部分清除组 263 9 1.57(0.52 ~ 4.75)  
反弹高乳酸组 31 6 11.36(2.95 ~ 43.84)  
3期        
部分清除组 109 20 5.45(1.58 ~ 18.78)  
反弹高乳酸组 58 29 18.26(5.38 ~ 62.01)  
手术类型       0.922
CABG类        
部分清除组 178 14 4.73(1.70 ~ 13.17)  
反弹高乳酸组 39 17 20.96(7.61 ~ 57.76)  
瓣膜类        
部分清除组 160 9 2.40(0.74 ~ 7.81)  
反弹高乳酸组 29 6 9.40(2.61 ~ 33.86)  
其他类        
部分清除组 114 9 4.63(1.00 ~ 21.52)  
反弹高乳酸组 34 17 33.78(7.77 ~ 146.78)  
1
Zhang M, Zeng J, Ge Y, et al. Risk factors and prognosis of post-surgical acute kidney injury in elderly patients based on the MIMIC-IV database [J]. Eur J Med Res, 2025, 30 (1): 491.
2
Bai YX, Wang ZH, Lv Y, et al. Association between frailty and acute kidney injury after cardiac surgery: unraveling the moderation effect of body fat through an international, retrospective, multicohort study [J]. Int J Surg, 2025, 111 (1): 761-770.
3
Scurt FG, Bose K, Mertens PR, et al. Cardiac surgery-associated acute kidney injury [J]. Kidney360, 2024, 5 (6): 909-926.
4
Maeda A, Chaba A, Inokuchi R, et al. Carboxyhemoglobin as potential biomarker for cardiac surgery associated acute kidney injury [J]. J Cardiothorac Vasc Anesth, 2024, 38 (10): 2221-2230.
5
Lin F, Pan Q, Chen Y, et al. Sex-related differences in clinical characteristics and in-hospital outcomes of patients in acute type A aortic dissection [J]. BMC Surg, 2024, 24 (1): 302.
6
Deng J, Zhong Q. Analysis of prognostic factors for in-hospital mortality in patients with unplanned reexploration after cardiovascular surgery [J]. J Cardiothorac Surg, 2022, 17 (1): 82.
7
Wang Z, Zhang L, Xu F, et al. The association between continuous renal replacement therapy as treatment for sepsis-associated acute kidney injury and trend of lactate trajectory as risk factor of 28-day mortality in intensive care units [J]. BMC Emerg Med, 2022, 22 (1): 32.
8
Fang Y, Zhang Y, Shen X, et al. Utilization of lactate trajectory models for predicting acute kidney injury and mortality in patients with hyperlactatemia: insights across three independent cohorts [J]. Ren Fail, 2025, 47 (1): 2474205.
9
Curko-Cofek B, Jenko M, Taleska Stupica G, et al. The crucial triad: endothelial glycocalyx, oxidative stress, and inflammation in cardiac surgery—exploring the molecular connections[J]. Int J Mol Sci, 2024, 25 (20): 10891.
10
Kant S, Banerjee D, Sabe SA, et al. Microvascular dysfunction following cardiopulmonary bypass plays a central role in postoperative organ dysfunction[J]. Front Med (Lausanne), 2023, 10: 1110532.
11
Chen DX, Zhang YY, Xiong XL, et al. Association between intraoperative lactate levels and acute kidney injury after on-pump cardiac surgery: a retrospective cohort study across two centers [J]. BMC Surg, 2025, 25 (1): 324.
12
Li F, Luo J, Guo L, et al. Tissue oxygen saturation combined with serum lactic acid can predict cardiac surgery-associated acute kidney injury [J]. J Cardiothorac Vasc Anesth, 2026, 40 (1): 187-194.
13
Li H, Ren Q, Shi M, et al. Lactate metabolism and acute kidney injury[J]. Chin Med J (Engl), 2025, 138 (8): 916-924.
14
Cheng Y, Guo L. Lactate metabolism and lactylation in kidney diseases: insights into mechanisms and therapeutic opportunities [J]. Ren Fail, 2025, 47 (1): 2469746.
15
Poston JT, Koyner JL. Sepsis associated acute kidney injury[J]. BMJ, 2019, 364: k4891.
16
Khwaja A. KDIGO clinical practice guidelines for acute kidney injury[J]. Nephron Clin Pract, 2012, 120 (4): c179-c184.
17
Luo XQ, Zhang NY, Deng YH, et al. Major adverse kidney events in hospitalized older patients with acute kidney injury: machine learning-based model development and validation study [J]. J Med Internet Res, 2025, 27: e52786.
18
Sorensen CLB, Plana-Ripoll O, Bültmann U, et al. Developmental trajectories in mental health through adolescence and adulthood: does socio-economic status matter?[J]. Epidemiol Psychiatr Sci, 2025, 34: e33.
19
Chen AX, Simpson SQ, Pallin DJ. Sepsis guidelines[J]. N Engl J Med, 2019, 380 (14): 1369-1371.
20
Ekanmian G, Lunghi C, Vasiliadis HM, et al. Trajectories of antidiabetic medication adherence in older adults and the effect of depression and anxiety symptoms[J]. Endocr Pract, 2025, 31 (10): 1247-1255.
21
许振琦,彭烨,易伟,等.中心静脉-动脉血二氧化碳分压差与动脉-中心静脉血氧含量差比值及乳酸清除率在创伤性休克患者液体复苏中的价值探讨[J/OL].中华危重症医学杂志(电子版)202518(3):197-203.
22
Dogan S, Aslan S, Borta T, et al. Is there an effect of initial and 24-hour blood gas lactate and methemoglobin levels on predicting mortality of patients in the intensive care unit?[J]. Life (Basel), 2025, 15 (3): 373.
23
张霞,张瑞,郑志波,等.紫草素调控乳酸化修饰和线粒体功能改善脓毒症心肌病小鼠的预后[J/OL].中华危重症医学杂志(电子版)202417(4):275-284.
24
Mao Y, Zhang J, Zhou Q, et al. Hypoxia induces mitochondrial protein lactylation to limit oxidative phosphorylation[J]. Cell Res, 2024, 34 (1): 13-30.
25
Huynh KW, Pamenter ME. Lactate inhibits naked mole-rat cardiac mitochondrial respiration [J]. J Comp Physiol B, 2022, 192 (3-4): 501-511.
26
San-Millan I, Sparagna GC, Chapman HL, et al. Chronic lactate exposure decreases mitochondrial function by inhibition of fatty acid uptake and cardiolipin alterations in neonatal rat cardiomyocytes [J]. Front Nutr, 2022, 9: 809485.
27
Fang Y, Li Z, Yang L, et al. Emerging roles of lactate in acute and chronic inflammation [J]. Cell Commun Signal, 2024, 22 (1): 276.
28
Li X, Yang Y, Zhang B, et al. Lactate metabolism in human health and disease [J]. Signal Transduct Target Ther, 2022, 7 (1): 305.
29
Gupta GS. The lactate and the lactate dehydrogenase in inflammatory diseases and major risk factors in COVID-19 patients [J]. Inflammation, 2022, 45 (6): 2091-2123.
[1] 郭慧, 孔琦, 程芸, 张思然. 经颅多普勒超声脑血流动力学参数对前循环小动脉闭塞型脑卒中短期预后的预测价值[J/OL]. 中华医学超声杂志(电子版), 2026, 23(03): 236-243.
[2] 江伟东, 陈博. 早发性可切除胃癌的临床病理特征及预后列线图模型的建立[J/OL]. 中华普通外科学文献(电子版), 2026, 20(04): 230-237.
[3] 张彬, 王敏, 郑鹏, 冯犁, 李鑫, 赵高平. 早期胃癌淋巴结转移的危险因素及预后分析[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(04): 337-342.
[4] 李若隐, 罗义, 张雪琳, 雷李凤, 李思丽. MDSCs相关基因在乳腺癌中表达特征分析与风险预测模型构建[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(04): 366-373.
[5] 李春艳, 郎广碧, 周亚, 陈晓龙, 胡明冬. 胸部SMARCA4缺失型非小细胞肺癌与SMARCA4缺失型未分化肿瘤的组织病理学特征、影像学表现及预后分析[J/OL]. 中华肺部疾病杂志(电子版), 2026, 19(04): 534-541.
[6] 王倩, 韩星, 朱宁, 陈雷, 张耐. 肺动脉造影测定肺动脉扩张性在急性肺栓塞患者风险分层中的应用研究[J/OL]. 中华肺部疾病杂志(电子版), 2026, 19(04): 593-599.
[7] 陈玥, 吴丙琳, 冯妮娜, 杨勤秦, 苗雄伟, 尚苗苗, 马静, 王欢, 任巧微. 中性粒细胞/淋巴细胞比值预测免疫治疗联合化疗晚期NSCLC患者预后的临床意义[J/OL]. 中华肺部疾病杂志(电子版), 2026, 19(04): 607-614.
[8] 张群, 邱炳峰, 陶鸿杰. 血清肿瘤标志物及支气管肺泡灌洗液炎症细胞预测社区获得性肺炎患者预后的临床意义[J/OL]. 中华肺部疾病杂志(电子版), 2026, 19(04): 676-679.
[9] 李旭柯, 熊培尧, 杨子良, 敖玉凤, 徐立. 肿瘤包绕型血管在接受仑伐替尼治疗肝癌患者生存预后中的预测价值[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(03): 346-354.
[10] 叶金宝, 梁声强, 马建新, 林小强, 张海森. 小野寺预后营养指数对肝癌复发预测价值[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(03): 372-378.
[11] 贾宇飞, 王哲学, 王飞, 蔡昂书, 姚占胜. 中性粒细胞与淋巴细胞比值(NLR)和结直肠癌幸存者全因死亡的关联研究:基于NHANES 1999—2018数据的分析[J/OL]. 中华结直肠疾病电子杂志, 2026, 15(03): 224-231.
[12] 杨卫红, 郭向飞. 糖尿病合并慢性肾脏病患者血清前白蛋白和白蛋白/球蛋白比值与肾功能及预后的相关性研究[J/OL]. 中华肾病研究电子杂志, 2026, 15(03): 151-157.
[13] 黄霞, 张和平, 何永成. 机器学习驱动的IgA肾病精准治疗:疗效评估、预后预测及临床转化[J/OL]. 中华肾病研究电子杂志, 2026, 15(03): 166-169.
[14] 姜秀文, 戴璇, 周旻, 张圆, 何媛, 顾乃刚. 慢阻肺病并发肾损伤的机制及早期诊断生物标志物的研究进展[J/OL]. 中华临床医师杂志(电子版), 2026, 20(05): 374-380.
[15] 齐洪武, 徐泽雨. 脑脓肿的临床诊治进展[J/OL]. 中华临床医师杂志(电子版), 2026, 20(04): 327-331.
阅读次数
全文


摘要


AI


AI小编
你好!我是《中华医学电子期刊资源库》AI小编,有什么可以帮您的吗?