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重症监护病房急性肾损伤患者早期预后模型的建立与分析

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目的 建立重症监护病房(ICU)急性肾损伤(AKI)患者进展为AKI 3 期(肾衰竭)的预判模型,从而协助医生早期及时决策是否提前干预治疗.方法 采用回顾性分析,选择 2018 年 1 月至 2023 年 5 月入住河南省直第三人民医院重症医学科 38 例AKI患者.记录患者入院时急性生理学与慢性健康状况评分Ⅱ(APACHEⅡ),住院期间血肌酐(SCr)、血尿素氮(BUN)和每日尿量数据,以及连续性肾脏替代治疗(CRRT)干预节点时间.基于临床采集的实际病例病理数据,经清洗、筛选、归一标准化后,建立标准化肌酐值比率均值多项式拟合模型作为判断进展为AKI 3 期的第一标准,建立标准化肌酐值比率指数拟合模型作为判断进展为AKI 3 期的第二标准.结果 共纳入 38 例AKI患者,男性 25 例,女性 13 例;年龄(58.45±12.94)岁;入院时APACHEⅡ评分(24.13±4.17)分;治疗干预节点(4.42±0.95)d.基于双回归模型的综合应用,可以较少的统计数据样本量,对标准化肌酐值比率数据进行统计模型拟合,给出患者标准化肌酐值比率相对于第n天的散点指数非线性回归模型,y=1.2462x1.1649,其中自变量x为第n天,y是对应的标准化肌酐值比率,该模型R2= 0.860 1,统计拟合度较合理.辅以给出患者标准化肌酐值比率统计均值相对于第n天的二次非线性回归模型,y=-0.260 6x2+3.010 7x-1.612,同样地,自变量x为第n天,y是对应的标准化肌酐值比率,该模型R2=0.998 9,统计拟合度非常完美.以健康状态下基准SCr值为 66 μmol/L的患者为例,双回归模型预测结果显示,该患者会在 3~5 d进展至AKI 3 期.基于此模型,对其他早期干预肾损伤患者SCr值进行预测分析得到相同的结果.结论 该模型可以较为很好地预测患者进展至AKI 3 期(肾衰竭)的时间区间,从而协助重症医生对AKI患者尽早进行干预,阻止病情进展.
Establishment and analysis of an early prognosis model of patients with acute kidney injury in intensive care unit
Objective To establish a predictive model for the progression of acute kidney injury(AKI)to stage 3 AKI(renal failure)in the intensive care unit(ICU),so as to assist physicians to make early and timely decisions on whether to intervene in advance.Methods A retrospective analysis was conducted.Thirty-eight patients with AKI admitted to the intensive care medicine of the Third People's Hospital of Henan Province from January 2018 to May 2023 were enrolled.Patient data including acute physiology and chronic health evaluationⅡ(APACHEⅡ)upon admission,serum creatinine(SCr),blood urea nitrogen(BUN),daily urine output during hospitalization,and the timing of continuous renal replacement therapy(CRRT)intervention were recorded.Based on clinically collected pathological data,standardized creatinine value ratio mean polynomial fitting models were established as the first criterion for judging the progression to stage 3 AKI after data cleansing,screening,and normalization.Additionally,standardized creatinine value ratio index fitting models were established as the second criterion for predicting progression to stage 3 AKI.Results A total of 38 AKI patients were included,including 25 males and 13 females.The average age was(58.45±12.94)years old.The APACHEⅡ score was 24.13±4.17 at admission.The intervention node was(4.42±0.95)days.Using a dual regression model approach,statistical modeling was performed with a relatively small sample size of statistical data samples,yielding a scatter index non-linear regression model for standardized creatinine value ratio data relative to day"n",with y = 1.2462x1.1649 and an R2 of 0.860 1,indicating reasonable statistical fitting.Additionally,a quadratic non-linear regression model was obtained for the mean standardized creatinine value ratio relative to day"n",with y =-0.2606x2+3.010 7x-1.612 and an R2 of 0.998 9,indicating an excellent statistical fit.For example,using a baseline SCr value of 66 μmol/L for a healthy individual,the dual regression model predicted that the patient would progress to stage 3 AKI within 3-5 days.This prediction was consistent when applied to other early intervention renal injury patients.Conclusion The established model effectively predicts the time interval of the progression of AKI to stage 3 AKI(renal failure),which assist intensive care physicians to intervene AKI as early as possible to prevent disease progression.

Mathematical modelingSevere caseAcute kidney injury

耿玉安、王聪梅、许智晶、齐路、师延刚、苏世琼、王恺、刘瑞芳

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河南省直第三人民医院重症医学科,郑州 450006

淄博市中心医院重症医学科,山东淄博 255000

数学建模 重症 急性肾损伤

河南省医学科技攻关计划联合共建项目河南省医学科技攻关计划联合共建项目山东省医疗卫生科技发展计划项目

2018020580LHGJ20230671202010000131

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(2)
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