Diagnostic model for spontaneous bacterial peritonitis in cirrhotic patients with ascites: a multicenter cohort study.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Xuehong Yin,Enqiang Qin,Rui Song,Xuli Bao,Jinling Dong,Wei Hou,Wei Hua,Bo Tu,Yuening Zhang,Qinghua Meng
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Abstract

INTRODUCTION Spontaneous bacterial peritonitis (SBP) is a potentially life-threatening complication of cirrhotic ascites. Early diagnosis and treatment of SBP are essential to improve the survival rates and prognosis of patients. We aimed to identify the predictors of SBP to establish a new noninvasive early diagnostic tool. METHODS We screened 1618 patients who underwent paracentesis due to cirrhotic ascites between January 2017 and December 2018 in three hospitals. We evaluated the symptomatic, clinical, and laboratory parameters to identify the predictors of SBP. The primary diagnostic model was displayed as a nomogram. RESULTS The model included abdominal pain, diarrhea, white blood cell count, neutrophil percentage, procalcitonin, C-reactive protein, lactate dehydrogenase, glucose, and Model for End-stage Liver Disease score. The model's diagnostic performance was good, with an AUC of 0.84 [95% confidence interval (CI), 0.81-0.87] in the training cohort. In the validation cohort, the diagnostic ability was also good, with AUCs of 0.87 (95% CI, 0.83-0.91) and 0.90 (95% CI, 0.87-0.94) for inner and outer validation queues, respectively. Moreover, the decision curve analysis confirmed the clinical utility of the nomogram model. In addition, we developed a Microsoft Excel calculation model to allow convenient adoption of the model in clinical practice. CONCLUSION We developed good performing diagnostic models for SBP.
肝硬化腹水患者自发性细菌性腹膜炎的诊断模型:一项多中心队列研究。
简介:自发性细菌性腹膜炎(SBP)是肝硬化腹水的一种可能危及生命的并发症。早期诊断和治疗 SBP 对提高患者的生存率和预后至关重要。我们旨在确定 SBP 的预测因素,以建立一种新的无创早期诊断工具。方法我们筛查了 2017 年 1 月至 2018 年 12 月期间在三家医院接受腹腔穿刺术的 1618 名肝硬化腹水患者。我们评估了症状、临床和实验室参数,以确定 SBP 的预测因素。结果该模型包括腹痛、腹泻、白细胞计数、中性粒细胞百分比、降钙素原、C 反应蛋白、乳酸脱氢酶、葡萄糖和终末期肝病模型评分。该模型的诊断性能良好,在训练队列中的 AUC 为 0.84 [95% 置信区间 (CI),0.81-0.87]。在验证队列中,诊断能力也很好,内部和外部验证队列的AUC分别为0.87(95% CI,0.83-0.91)和0.90(95% CI,0.87-0.94)。此外,决策曲线分析证实了提名图模型的临床实用性。此外,我们还开发了 Microsoft Excel 计算模型,以便在临床实践中方便地采用该模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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