Establishment and Validation of a Non-Invasive Diagnostic Nomogram to Identify Spontaneous Bacterial Peritonitis in Patients With Decompensated Cirrhosis

Frontiers in Medicine

Chao Tan¹  Tiantian Wang²  Qian Xu²  Shoushu Xiang²  Yuanjiu Wen³  Juntao Tan⁴  Chen Yang⁵  Wenlong Zhao⁵ 

[1] Cancer Hospital, Chongqing University, Chongqing, China;College of Medical Informatics, Chongqing Medical University, Chongqing, China;Department of General Medicine, Army Medical University (Third Military Medical University), Chongqing, China;Department of Medical Administration, People's Hospital of Chongqing Banan District, Chongqing, China;Medical Data Science Academy, Chongqing Medical University, Chongqing, China;

关键词: decompensated cirrhosis;    spontaneous bacterial peritonitis;    nomogram;    diagnostic;    non-invasive;   

DOI  :  10.3389/fmed.2021.797363

来源: DOAJ

【 摘 要 】

BackgroundSpontaneous bacterial peritonitis (SBP) is a common and life-threatening infection in patients with decompensated cirrhosis (DC), and it is accompanied with high mortality and morbidity. However, early diagnosis of spontaneous bacterial peritonitis (SBP) is not possible because of the lack of typical symptoms or the low patient compliance and positivity rate of the ascites puncture test. We aimed to establish and validate a non-invasive diagnostic nomogram to identify SBP in patients with DC.MethodData were collected from 4,607 patients with DC from July 2015 to December 2019 in two tertiary hospitals in Chongqing, China (A and B). Patients with DC were divided into the SBP group (995 cases) and the non-SBP group (3,612 cases) depending on whether the patients had SBP during hospitalization. About 70% (2,685 cases) of patients in hospital A were randomly selected as the traindata, and the remaining 30% (1,152 cases) were used as the internal validation set. Patients in hospital B (770 cases) were used as the external validation set. The univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to screen variables, and logistic regression was used to determine independent predictors to construct a nomogram to identify patients with SBP. Area under curve (AUC), calibration curve, and dynamic component analysis (DCA) were carried out to determine the effectiveness of the nomogram.ResultThe nomogram was composed of seven variables, namely, mean red blood cell hemoglobin concentration (odds ratio [OR] = 1.010, 95% CI: 1.004–1.016), prothrombin time (OR = 1.038, 95% CI: 1.015–1.063), lymphocyte percentage (OR = 0.955, 95% CI: 0.943–0.967), prealbumin (OR = 0.990, 95% CI: 0.987–0.993), total bilirubin (OR = 1.003 95% CI: 1.002–1.004), abnormal C-reactive protein (CRP) level (OR = 1.395, 95% CI: 1.107–1.755), and abnormal procalcitonin levels (OR = 1.975 95% CI: 1.522–2.556). Good discrimination of the model was observed in the internal and external validation sets (AUC = 0.800 and 0.745, respectively). The calibration curve result indicated that the nomogram was well-calibrated. The DCA curve of the nomogram presented good clinical application ability.ConclusionThis study identified the independent risk factors of SBP in patients with DC and used them to construct a nomogram, which may provide clinical reference information for the diagnosis of SBP in patients with DC.

【 授权许可】

Unknown