Shang Na, Liu Huizhen, Wang Na, Li Junyu, Wang Yahui, Liu Lushan, Guo Shubin
Department of Emergency Medicine, China Rehabilitation Research Center Beijing Bo'ai Hospital, Beijing 100068, China.
Emergency Medicine Clinical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing 100020, China. Corresponding author: Guo Shubin, Email:
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2021 Dec;33(12):1409-1413. doi: 10.3760/cma.j.cn121430-20210618-0913.
To establish a clinical diagnostic scoring system for septic cardiomyopathy (SCM) and evaluate its diagnostic efficacy.
A prospective cohort study was performed. Patients with sepsis and septic shock admitted to the department of emergency of China Rehabilitation Research Center were enrolled from January 2019 to December 2020. The baseline information, medical history, heart rate (HR), mean arterial pressure (MAP), body temperature and respiratory rate (RR) on admission were recorded. Laboratory indexes such as white blood cell count (WBC), hypersensitivity C-reactive protein (hs-CRP), N-terminal pro-brain natriuretic peptide (NT-proBNP), and blood lactic acid (Lac) were measured. Transthoracic echocardiography was conducted within 24 hours and on the 7th after admission. Sequential organ failure assessment (SOFA) score, acute physiology and chronic health evaluation II (APACHE II), and nutritional risk screening 2002 scale (NRS2002) were also assessed. The patients were divided into two groups according to whether SCM occurred or not. The risk factors of SCM were screened by univariate and multivariate Logistic regression. The cut-off value of continuous index was determined by receiver operator characteristic curve (ROC curve) and discretized concerning clinical data. The regression coefficient β was used to establish the corresponding score, and the clinical diagnostic score system of SCM was established. The diagnostic value of the model was evaluated by ROC curve.
In total, 147 patients were enrolled in the study and the incidence of SCM was 28.6% (42/147). Univariate Logistic regression analysis showed the risk factors of SCM included: (1) continuous indicators: age, NT-proBNP, RR, MAP, Lac, NRS2002, SOFA, APACHE II; (2) discrete indicators: shock, use of vasoactive drugs, history of coronary heart disease, acute kidney injury (AKI). Multivariate Logistic regression analysis after discretization of above continuous index showed that age ≥ 87 years old, NT-proBNP ≥ 3 000 ng/L, RR ≥ 30 times/min, Lac ≥ 3 mmol/L and SOFA ≥ 10 points were independent risk factors for SCM [age ≥ 87 years: odds ratio (OR) = 3.491, 95% confidence interval (95%CI) was 1.371-8.893, P = 0.009; NT-proBNP ≥ 3 000 ng/L: OR = 2.708, 95%CI was 1.093-6.711, P = 0.031; RR ≥ 30 times/min: OR = 3.404, 95%CI was 1.356-8.541, P = 0.009; Lac ≥ 3 mmol/L: OR = 3.572, 95%CI was 1.460-8.739, P = 0.005; SOFA ≥ 10 points: OR = 8.693, 95%CI was 2.541-29.742, P = 0.001]. The clinical diagnostic score system of SCM was established successfully, which was composed of age ≥ 87 years old (1 point), NT-proBNP ≥ 3 000 ng/L (1 point), RR ≥ 30 times/min (1 point), Lac ≥ 3.0 mmol/L (1 point), SOFA ≥ 10 points (2 points), and the total score was 6 points. ROC curve analysis showed the cut-off value of the scoring system for diagnosing SCM was 3 points, the area under ROC curve (AUC) was 0.833, 95%CI was 0.755-0.910, P < 0.001, with the sensitivity of 71.4%, and specificity of 86.7%.
The clinical diagnostic scoring system has good diagnostic efficacy for SCM and contributes to early identification of SCM for clinicians.
建立脓毒症性心肌病(SCM)的临床诊断评分系统并评估其诊断效能。
进行一项前瞻性队列研究。选取2019年1月至2020年12月在中国康复研究中心急诊科住院的脓毒症和脓毒性休克患者。记录入院时的基线信息、病史、心率(HR)、平均动脉压(MAP)、体温和呼吸频率(RR)。检测白细胞计数(WBC)、超敏C反应蛋白(hs-CRP)、N末端脑钠肽前体(NT-proBNP)和血乳酸(Lac)等实验室指标。入院后24小时内及第7天进行经胸超声心动图检查。同时评估序贯器官衰竭评估(SOFA)评分、急性生理与慢性健康状况评分系统II(APACHE II)和营养风险筛查2002量表(NRS2002)。根据是否发生SCM将患者分为两组。通过单因素和多因素Logistic回归筛选SCM的危险因素。通过受试者工作特征曲线(ROC曲线)确定连续指标的截断值,并结合临床数据进行离散化处理。利用回归系数β建立相应评分,构建SCM的临床诊断评分系统。通过ROC曲线评估模型的诊断价值。
共纳入147例患者,SCM发生率为28.6%(42/147)。单因素Logistic回归分析显示,SCM的危险因素包括:(1)连续指标:年龄、NT-proBNP、RR、MAP、Lac、NRS2002、SOFA、APACHE II;(2)离散指标:休克、使用血管活性药物、冠心病史、急性肾损伤(AKI)。对上述连续指标进行离散化处理后多因素Logistic回归分析显示,年龄≥87岁、NT-proBNP≥3000 ng/L、RR≥30次/分钟、Lac≥3 mmol/L及SOFA≥10分为SCM的独立危险因素[年龄≥87岁:比值比(OR)=3.491,95%置信区间(95%CI)为1.371 - 8.893,P = 0.009;NT-proBNP≥3000 ng/L:OR = 2.708,95%CI为1.093 - 6.711,P = 0.031;RR≥30次/分钟:OR = 3.404,95%CI为1.356 - 8.541,P = 0.009;Lac≥3 mmol/L:OR = 3.572,95%CI为1.460 - 8.739,P = 0.005;SOFA≥10分:OR = 8.693,95%CI为2.541 - 29.742,P = 0.001]。成功建立SCM的临床诊断评分系统,该系统由年龄≥87岁(1分)、NT-proBNP≥3000 ng/L(1分)、RR≥30次/分钟(1分)、Lac≥3.0 mmol/L(1分)、SOFA≥10分(2分)组成,总分6分。ROC曲线分析显示,该评分系统诊断SCM的截断值为3分,ROC曲线下面积(AUC)为0.833,95%CI为0.755 - 0.910,P < 0.001,灵敏度为71.4%,特异度为86.7%。
该临床诊断评分系统对SCM具有良好的诊断效能,有助于临床医生早期识别SCM。