Department of Critical Care Medicine of Liver Disease, Beijing You-An Hospital, Capital Medical University.
Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital.
Eur J Gastroenterol Hepatol. 2024 Nov 1;36(11):1319-1328. doi: 10.1097/MEG.0000000000002841. Epub 2024 Sep 11.
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.
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.
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.
We developed good performing diagnostic models for SBP.
自发性细菌性腹膜炎（SBP）是肝硬化腹水的一种潜在危及生命的并发症。早期诊断和治疗 SBP 对于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，以建立一种新的非侵入性早期诊断工具。
我们筛选了 2017 年 1 月至 2018 年 12 月期间在三所医院因肝硬化腹水接受腹腔穿刺术的 1618 名患者。我们评估了症状、临床和实验室参数，以确定 SBP 的预测因素。主要诊断模型以列线图的形式呈现。
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、血糖和终末期肝病评分。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，0.81-0.87）。在验证队列中，该诊断能力也较好，内部验证队列的 AUC 为 0.87（95%置信区间，0.83-0.91），外部验证队列的 AUC 为 0.90（95%置信区间，0.87-0.94）。此外，决策曲线分析证实了列线图模型的临床实用性。此外，我们开发了一个 Microsoft Excel 计算模型，以便在临床实践中方便地采用该模型。
我们开发了 SBP 的诊断性能良好的模型。
