Establishment and evaluation of clinical diagnostic scoring system for septic cardiomyopathy
10.3760/cma.j.cn121430-20210618-0913
- VernacularTitle:脓毒性心肌病临床诊断评分体系的构建及效能评估
- Author:
Na SHANG
1
;
Huizhen LIU
;
Na WANG
;
Junyu LI
;
Yahui WANG
;
Lushan LIU
;
Shubin GUO
Author Information
1. 中国康复研究中心北京博爱医院急诊科，北京 100068
- Keywords:
Septic cardiomyopathy;
Clinical diagnostic scoring system;
Diagnostic efficacy
- From:
Chinese Critical Care Medicine
2021;33(12):1409-1413
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a clinical diagnostic scoring system for septic cardiomyopathy (SCM) and evaluate its diagnostic efficacy.Methods: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 evaluationⅡ(APACHEⅡ), 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.Results: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, APACHEⅡ; 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%. Conclusion:The clinical diagnostic scoring system has good diagnostic efficacy for SCM and contributes to early identification of SCM for clinicians.
