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重症患者高钾血症风险评估模型的建立

Establishment of a hyperkalemia assessment model for critically ill patients

  • 摘要:
    目的  探讨重症监护室(ICU)住院患者高钾血症发生的影响因素,并建立风险评估模型。
    方法  回顾性收集2019年10月至2020年12月郑州大学第一附属医院ICU收治的4 963例住院患者的临床资料,根据血清钾水平分为正常血钾组(n=4 535,血钾3.5~5.5 mmol/L)和高钾血症组(n=428,血钾>5.5 mmol/L),再按7∶3将患者随机分为模型训练集(n=3 474)和模型验证集(n=1 489)。通过logistic回归分析创建预测高钾血症发生的风险评估模型,绘制ROC曲线,评估模型价值,并在验证集中进行验证。
    结果  多因素logistic回归分析显示,男性、ICU住院≥5 d、估算肾小球滤过率(eGFR) < 90 mL·min﹣1·(1.73 m2﹣1、APACHEⅡ评分>12分、糖尿病、急性呼吸窘迫综合征(ARDS)、肝硬化、感染性休克、多器官功能障碍综合征(MODS)为重症患者发生高钾血症的独立相关因素。风险评估模型在训练集中的ROC曲线下面积(AUC)为0.777,评分为5分时的灵敏度为76.4%、特异度为66.6%;模型在验证集中的AUC为0.777,灵敏度80.5%、特异度67.8%。
    结论  该风险评估模型有助于评估ICU重症患者高钾血症发生与否,可指导临床进行早期预防与干预。

     

    Abstract:
    Objective  To explore risk factors of hyperkalemia among patients in intensive care unit (ICU) and to establish a risk assessment model for evaluating hyperkalemia.
    Methods  The clinical data of 4 963 hospitalized patients admitted to ICU in the First Affiliated Hospital of Zhengzhou University from October 2019 to December 2020 were retrospectively collected. According to serum potassium level, patients were divided into a normal serum potassium of 3.5-5.5 mmol/L group (n=4 535) and a hyperkalemia group (n=428) with serum potassium level above 5.5 mmol/L. All Patients were then randomly divided into a training set (n=3 474) and a validation set (n=1 489) in a 7:3 ratio. Logistic regression analysis was used to construct a risk assessment model to evaluate the occurrence of hyperkalemia in ICU patients. ROC curve was used to evaluate the value of the model, and the model was validated in the validation set.
    Results  Multivariate logistic analysis showed that male, ICU length of stay ≥ 5 days, estimated glomerular filtration rate (eGFR) < 90 mL·min-1·(1.73 m2)-1, APACHE Ⅱ score>12, diabetes, acute respiratory distress syndrome (ARDS), cirrhosis, septic shock, multiple organ dysfunction syndrome (MODS) were independent related factors for hyperkalemia in severe patients. The area under the ROC curve (AUC) of this assessment model in the training set was 0.777, the cut-off value was 5, with 76.4% of the sensitivity and 66.6% of specificity; the validation set suggests that the model has the AUC of 0.777, with 80.5% of sensitivity, and 67.8% of specificity.
    Conclusions  This risk assessment model is helpful to assess occurrence of hyperkalemia in ICU patients, which offers an alternative approach for the prevention and intervention of hyperkalemia.

     

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