作者：曾景,林月雄,金廷荣,李雪松

单位：

516001 广东省湛江市，广东医科大学 通信作者：李雪松，E-mail：lxs75cedar@163.com

Units：

Guangdong Medical University, Zhanjiang 516001, China Corresponding author: LI Xuesong, E-mail: lxs75cedar@163.com

关键词：

猝死,心脏; 心搏骤停; 心肺复苏术; 自主循环恢复; 神经功能; 预后; 预测模型;

Keywords：

Death, sudden, cardiac; Sudden cardiac arrest; Cardiopulmonary resuscitation; Return of spontaneouscirculation; Neurological function; Prognosis; Prediction model

CLC：

DOI：

10.12114/j.issn.1008-5971.2021.00.257

Funds：

摘要：

背景心肺复苏(CPR)是抢救心搏骤停(CA)患者的重要方法,但多数患者抢救后可能出现神经功能预后不良,加强此类患者脑功能监测并寻找合适的神经功能预后判断方法对患者的康复具有重要意义。目的 基于脑功能监测指标构建预测CA行CPR后自主循环恢复(ROSC)患者神经功能预后不良的多因素Logistic回归模型。方法选取惠州市第三人民医院2017年11月至2020年3月收治的CA行CPR后ROSC患者93例为研究对象。收集患者基线资料、脑血流参数、颈内静脉球血氧饱和度(SjvO2)及脑动脉-静脉血氧含量差(a-vDO2)、脑氧代谢率(CMRO2)。根据患者转出ICU时格拉斯哥-匹兹堡脑功能表现计分系统评分,将其分为预后良好组(1～2分,n=36)和预后不良组(3～4分,n=57)。采用多因素Logistic回归分析探讨CA行CPR后ROSC患者神经功能预后不良的影响因素,并构建多因素Logistic回归模型;绘制CBF、a-vDO2、CMRO2以及多因素Logistic回归模型预测CA行CPR后ROSC患者神经功能预后不良的受试者工作特征(ROC)曲线。结果 预后不良组ROSC时间长于预后良好组,急性生理学与慢性健康状况评分系统Ⅱ(APACHEⅡ)评分高于预后良好组,ICU入住时间短于预后良好组,出ICU时格拉斯哥昏迷量表(GCS)评分低于预后良好组(P <0.05)。预后不良组左、右侧收缩期血流速度(Vs)、舒张期血流速度(Vd)、平均血流速度(Vm)、脑血流量(CBF)低于预后良好组,搏动指数(PI)、阻力指数(RI)高于预后良好组(P <0.05)。预后不良组SjvO2高于预后良好组,a-vDO2、CMRO2低于预后良好组(P <0.05)。多因素Logistic回归分析结果显示,左侧CBF、a-vDO2、CMRO2是CA行CPR后ROSC患者神经功能预后不良的影响因素(P <0.05)。将左侧CBF、a-vDO2、CMRO2分别作为协变量X1、X2、X3,构建多因素Logistic回归模型,其表达式为:P=1/{1+Exp〔-(-8.735+0.553X1+0.062X2+0.117X3)〕}。CBF、a-vDO2、CMRO2以及多因素Logistic回归模型预测CA行CPR后ROSC患者神经功能预后不良的曲线下面积分别为0.664〔95%CI(0.448,0.887),P=0.035〕、0.603〔95%CI(0.395,0.818),P=0.047〕、0.712〔95%CI(0.513,0.918),P=0.013〕、0.856〔95%CI(0.713,0.985),P=0.002〕,最佳临界值分别为5.5 ml/min、28.1 ml/L、155.8μmol·100 g-1·min-1、0.267,灵敏度分别为73.68%、64.91%、78.95%、85.96%,特异度分别为72.22%、63.89%、83.33%、91.67%,正确率分别为73.12%、64.52%、80.65%、88.17%。结论本研究基于脑功能监测指标构建的多因素Logistic回归模型对CA行CPR后ROSC患者神经功能预后不良具有较高的预测价值,值得临床推广使用。

Abstract：

【Abstract】 Background Cardiopulmonary resuscitation (CPR) is an important method to rescue patients with cardiacarrest (CA) , but most patients may have poor neurological prognosis after rescue. Strengthening the monitoring of brain functionof such patients and finding suitable methods for judging the prognosis of neurological function are of great significance to therehabilitation of patients. Objective To construct a prediction model of multivariate Logistic regression for poor prognosis ofneurological function in patients with return of spontaneous circulation (ROSC) after CPR in CA. Methods A total of 93 patientswith ROSC after CPR in CA treated in Huizhou Third People's Hospital from November 2017 to March 2020 were selected asthe research objects. The baseline data, cerebral blood flow parameters, jugular venous bulb oxygen saturation (SjvO2) , arteriovenous oxygen content difference (a-vDO2) and cerebral oxygen metabolism rate (CMRO2) were collected. According to the scoreof Glasgow Pittsburgh brain function performance scoring system when patients were transferred out of ICU, they were divided intogood prognosis group (1-2 points, n=36) and poor prognosis group (3-4 points, n=57) . Multivariate Logistic regression analysiswas used to explore the influencing factors of poor prognosis of neurological function in ROSC patients after CPR in CA, and aprediction model of multivariate Logistic regression was constructed. The receiver operating characteristic (ROC) curve of CBF,a-vDO2, CMRO2 and prediction model of multivariate Logistic regression predicting the poor prognosis of neurological function inROSC patients after CPR in CA was drawn. Results The ROSC time in the poor prognosis group was longer than that in the goodprognosis group, the score of acute physiology and chronic health evaluation scoring system Ⅱ (APACHE Ⅱ ) was higher thanthat in the good prognosis group, the ICU stay time was shorter than that in the good prognosis group, and the Glasgow Coma Scale(GCS) score when leaving ICU was lower than that in the good prognosis group (P < 0.05) . The left and right systolic velocity (Vs) ,diastolic velocity (Vd) , mean velocity (Vm) and cerebral blood flow (CBF) in the poor prognosis group were lower than those in thegood prognosis group, and the pulsitility index (PI) and resistence index (RI) were higher than those in the good prognosis group(P < 0.05) . SjvO2 in poor prognosis group was higher than that in good prognosis group, and a-vDO2 and CMRO2 were lower thanthose in good prognosis group (P < 0.05) . Multivariate Logistic regression analysis showed that left CBF, a-vDO2 and CMRO2were the influencing factors of poor prognosis of neurological function in ROSC patients after CPR in CA (P < 0.05) . Left CBF,a-vDO2 and CMRO2 were taken as covariates X1,X2 and X3, respectively, and constructed the prediction model of multivariateLogistic regression. It's expression wasP=1/{1+Exp [- (-18.735+0.553X1+0.062X2+0.117X3) ] } . The areas under the curveof CBF, a-vDO2, CMRO2 and prediction model of multivariate Logistic regression for predicting the poor prognosis of neurologicalfunction in patients with ROSC after CPR in CA were 0.664 [95%CI(0.448, 0.887) ,P=0.035] , 0.603 [95%CI (0.395, 0.818) ,P=0.047] , 0.712 [95%CI(0.513, 0.918) , P=0.013] and 0.856 [95%CI (0.713, 0.985) ,P=0.002] , respectively. The optimumcritical values were 5.5 ml/min, 28.1 ml/L and 155.8 μmol?100 g-1?min-1 and 0.267, respectively. The sensitivity was 73.68%,64.91%, 78.95% and 85.96%, the specificity was 72.22%, 63.89%, 83.33% and 91.67%, and the accuracy was 73.12%, 64.52%,80.65% and 88.17%, respectively. Conclusion In this study, the prediction model of multivariate Logistic regression constructedbased on brain function monitoring indicators has a high predictive value for the poor prognosis of neurological function in ROSCpatients after CPR in CA, which is worthy of clinical promotion.

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