摘要目的:探讨影响危重症患者院内心脏骤停(IHCA)的相关因素，基于改良早期预警评分(MEWS)建立高级改良早期预警评分(AMEWS)模型并探讨该模型对危重症患者发生IHCA的预测作用。方法:回顾性收集2019年1月至2023年3月清华大学附属垂杨柳医院收治的1 230例危重症患者，以患者是否发生IHCA为终点进行分组，其中IHCA组患者362例，NO-IHCA组患者868例。对两组患者临床资料进行比较，利用受试者工作特征(ROC)曲线得出MEWS、预警症状、心电图、乳酸(LAC)、血钾(K)、肌钙蛋白(TNI)、脑钠肽(BNP)、左室射血分数(LVEF)、C反应蛋白(CRP)预测IHCA发生的最佳截断值，多因素logistic回归分析筛选出影响危重症患者发生IHCA的独立危险因素并构建AMEWS模型。结果:IHCA组LAC、K、TNI、BNP、CRP、MEWS高于NO-IHCA组，LVEF低于NO-IHCA组，预警症状、心电图差异具有统计学意义(均 P<0.05)。ROC曲线分析得出MEWS、LAC、K、TNI、BNP、LVEF、CRP预测IHCA发生的最佳截断值分别为4分、2.20 mmol/L、4.40 mmol/L、0.10 ng/ml、361 pg/ml、50%、5 mg/L。多因素logistic回归分析结果显示，MEWS、预警症状、心电图、LAC、TNI、BNP、LVEF、CRP是危重症患者发生IHCA的独立影响因素(均 P<0.05)。基于多因素logistic回归分析获得的危险因素构建AMEWS模型，随着预警症状和心电图不同类型的改变，MEWS分数、LAC水平、TNI水平、BNP水平、CRP水平增加，LVEF分数下降，列线图模型相应评分增加，对应危重症患者IHCA发生风险增加。AMEW模型中，0～9分为低危组(患者只需留观，不需住院)，10～14分为中危组(病情变化危险增大，需住院治疗)，≥15分为高危组(死亡危险明显增加，需入住ICU接受治疗)。 结论:MEWS、预警症状、心电图、LAC、TNI、BNP、LVEF、CRP是影响危重症患者发生IHCA的相关因素，基于MEWS建立的AMEWS模型对危重症患者发生IHCA具有较高的预测价值。

abstractsObjective:To explore the relevant factors affecting in-hospital cardiac arrest (IHCA) in critically ill patients, establish an advanced modified early warning score (AMEWS) model based on the Modified Early Warning Score (MEWS), and explore the predictive effect of this model on the occurrence of IHCA in critically ill patients.Methods:A retrospective collection was conducted on 1230 critically ill patients admitted to the Chuiyangliu Hospital affiliated to Tsinghua University from January 2019 to March 2023. The patients were divided into two groups based on whether they developed IHCA or not, with 362 patients in the IHCA group and 868 patients in the NO-IHCA group. The clinical data of two groups of patients were compared, and the receiver operating characteristic (ROC) curve was used to obtain the optimal cutoff values for predicting the occurrence of IHCA, including MEWS, warning symptoms, electrocardiogram, lactate (LAC), blood potassium (K), troponin (TNI), brain natriuretic peptide (BNP), left ventricular ejection fraction (LVEF), and C-reactive protein (CRP). Multivariate logistic regression analysis was used to screen for independent risk factors that affected the occurrence of IHCA in critically ill patients, and an AMEWS model was constructed.Results:LAC, K, TNI, BNP, CRP, and MEWS in the IHCA group were higher than those in the NO-IHCA group, while LVEF was lower than that in the NO-IHCA group. The differences in warning symptoms and electrocardiogram were statistically significant (all P<0.05). The ROC curve analysis showed that the optimal cutoff values for MEWS, LAC, K, TNI, BNP, LVEF, and CRP to predict the occurrence of IHCA were 4 points, 2.20 mmol/L, 4.40 mmol/L, 0.10 ng/ml, 361 pg/ml, 50%, and 5 mg/L, respectively. The results of multiple logistic regression analysis showed that MEWS, warning symptoms, electrocardiogram, LAC, TNI, BNP, LVEF, and CRP were independent influencing factors for the occurrence of IHCA in critically ill patients (all P<0.05). Based on the risk factors obtained from multiple logistic regression analysis, an AMEWS model was constructed. With changes in warning symptoms and different types of electrocardiograms, MEWS score, LAC level, TNI level, BNP level, CRP level increased, LVEF score decreased, and the corresponding score of the column chart model increased, corresponding to an increased risk of IHCA in critically ill patients. In the AMEW model, patients with a score of 0-9 were classified as low-risk group (patients only needed to stay under observation and did not need to be hospitalized), those with a score of 10-14 are classified as medium risk group (the risk of disease changes increased and hospitalization was required), and those with a score of ≥15 were classified as high-risk group [the risk of death significantly increased and required intensive care unit (ICU) treatment]. Conclusions:MEWS, warning symptoms, electrocardiogram, LAC, TNI, BNP, LVEF, and CRP are related factors affecting the occurrence of IHCA in critically ill patients. The AMEWS model established based on MEWS has high predictive value for the occurrence of IHCA in critically ill patients.