关于腹膜结核(TBP)的现有文献相对不足。大多数报告来自单个中心,没有评估死亡率的预测因素。在这项国际研究中,我们调查了一系列TBP患者的临床病理特征,并确定了与死亡率相关的关键特征.2010年至2022年在13个国家的38个医疗中心检测到的TBP患者被纳入该回顾性队列。参与的医生填写了一份在线问卷来报告研究数据。在这项研究中,包括208例TBP患者。TBP病例的平均年龄为41.4±17.5岁。106名患者(50.9%)为女性。19名患者(9.1%)感染艾滋病毒,45人(21.6%)患有糖尿病,30人(14.4%)患有慢性肾功能衰竭,12(5.7%)患有肝硬化,7人(3.3%)有恶性肿瘤,21例(10.1%)有免疫抑制药物使用史.在所有病例中,共有34例(16.3%)患者死亡,死亡可归因于TBP。建立了先锋死亡率预测模型,并建立了HIV阳性,肝硬化,腹痛,弱点,恶心和呕吐,腹水,在腹膜活检样本中分离结核分枝杆菌,结核病复发,高龄,高血清肌酐和ALT水平,异烟肼使用时间减少与死亡率显著相关(p<0.05)。这是第一个关于TBP的国际研究,也是迄今为止最大的案例系列。我们建议使用死亡率预测模型将允许早期识别可能死于TBP的高危患者。
Existing literature about peritoneal tuberculosis (TBP) is relatively insufficient. The majority of reports are from a single center and do not assess predictive factors for mortality. In this international
study, we investigated the clinicopathological characteristics of a large series of patients with TBP and determined the key features associated with mortality. TBP patients detected between 2010 and 2022 in 38 medical centers in 13 countries were included in this retrospective cohort. Participating physicians filled out an online questionnaire to report
study data. In this
study, 208 patients with TBP were included. Mean age of TBP cases was 41.4 ± 17.5 years. One hundred six patients (50.9%) were females. Nineteen patients (9.1%) had HIV infection, 45 (21.6%) had diabetes mellitus, 30 (14.4%) had chronic renal failure, 12 (5.7%) had cirrhosis, 7 (3.3%) had malignancy, and 21 (10.1%) had a history of immunosuppressive medication use. A total of 34 (16.3%) patients died and death was attributable to TBP in all cases. A pioneer mortality predicting model was established and HIV positivity, cirrhosis, abdominal pain, weakness, nausea and vomiting, ascites, isolation of Mycobacterium tuberculosis in peritoneal biopsy samples, TB relapse, advanced age, high serum creatinine and ALT levels, and decreased duration of isoniazid use were significantly related with mortality (p < 0.05). This is the first international
study on TBP and is the largest case series to date. We suggest that using the mortality predicting model will allow early identification of high-risk patients likely to die of TBP.