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Abstract：
BACKGROUND: Spontaneous bacterial peritonitis (SBP) is a common complication in patients with cirrhosis. The diagnosis of SBP is still mostly based on ascites cultures and absolute ascites polymorphonuclear (PMN) cell count, which restricts the widely application in clinical settings. This study aimed to identify reliable and easy-to-use biomarkers for both diagnosis and prognosis of cirrhotic patients with SBP.
METHODS: We conducted a retrospective study including 413 cirrhotic patients from March 2013 to July 2022 in the First Affiliated Hospital of Guangxi Medical University. Patients\' clinical characteristics and laboratory indices were collected and analyzed. Two machine learning methods (Xgboost and LASSO algorithms) and a logistic regression analysis were adopted to screen and validate the indices associated with the risk of SBP. A predictive model was constructed and validated using the estimated area under curve (AUC). The indices related to the survival of cirrhotic patients were also analyzed.
RESULTS: A total of 413 cirrhotic patients were enrolled in the study, of whom 329 were decompensated and 84 were compensated. 52 patients complicated and patients with SBP had a poorer Child-Pugh score (P < 0.05). Patients with SBP had a greater proportion of malignancies than those without SBP(P < 0.05). The majority of laboratory test indicators differed significantly between patients with and without SBP (P < 0.05). Albumin, neutrophil-to-lymphocyte ratio (NLR), and ferritin-to-neutrophil ratio (FNR) were found to be independently associated with SBP in decompensated cirrhotic patients using LASSO algorithms, and logistic regression analysis. The model established by the three indices showed a high predictive value with an AUC of 0.808. Furthermore, increased neutrophils, ALP, and C-reactive protein-to-albumin ratio (CAR) were associated with the shorter survival time of patients with decompensated cirrhosis, and the combination of these indices showed a greater predictive value for cirrhotic patients.
CONCLUSIONS: The present study identified FNR as a novel index in the diagnosis of SBP in decompensated patients with cirrhosis. A model based on neutrophils, ALP and CAR showed high performance in predicting the prognosis of patients with decompensated cirrhosis.
METHODS: We conducted a retrospective study including 413 cirrhotic patients from March 2013 to July 2022 in the First Affiliated Hospital of Guangxi Medical University. Patients\' clinical characteristics and laboratory indices were collected and analyzed. Two machine learning methods (Xgboost and LASSO algorithms) and a logistic regression analysis were adopted to screen and validate the indices associated with the risk of SBP. A predictive model was constructed and validated using the estimated area under curve (AUC). The indices related to the survival of cirrhotic patients were also analyzed.
RESULTS: A total of 413 cirrhotic patients were enrolled in the study, of whom 329 were decompensated and 84 were compensated. 52 patients complicated and patients with SBP had a poorer Child-Pugh score (P < 0.05). Patients with SBP had a greater proportion of malignancies than those without SBP(P < 0.05). The majority of laboratory test indicators differed significantly between patients with and without SBP (P < 0.05). Albumin, neutrophil-to-lymphocyte ratio (NLR), and ferritin-to-neutrophil ratio (FNR) were found to be independently associated with SBP in decompensated cirrhotic patients using LASSO algorithms, and logistic regression analysis. The model established by the three indices showed a high predictive value with an AUC of 0.808. Furthermore, increased neutrophils, ALP, and C-reactive protein-to-albumin ratio (CAR) were associated with the shorter survival time of patients with decompensated cirrhosis, and the combination of these indices showed a greater predictive value for cirrhotic patients.
CONCLUSIONS: The present study identified FNR as a novel index in the diagnosis of SBP in decompensated patients with cirrhosis. A model based on neutrophils, ALP and CAR showed high performance in predicting the prognosis of patients with decompensated cirrhosis.
摘要：
背景：自发性细菌性腹膜炎（SBP）是肝硬化患者的常见并发症。SBP的诊断仍然主要基于腹水培养和绝对腹水多形核（PMN）细胞计数，这限制了其在临床中的广泛应用。本研究旨在确定可靠且易于使用的生物标志物，用于诊断和预后肝硬化患者的SBP。
方法：我们进行了回顾性研究，包括2013年3月至2022年7月广西医科大学第一附属医院413例肝硬化患者。收集并分析患者的临床特征和实验室指标。采用两种机器学习方法（Xgboost和LASSO算法）和逻辑回归分析来筛选和验证与SBP风险相关的指标。使用估计的曲线下面积(AUC)构建并验证预测模型。还分析了与肝硬化患者生存相关的指标。
结果：本研究共纳入413例肝硬化患者，其中329人失代偿，84人获得补偿。52例合并SBP的患者Child-Pugh评分较差(P<0.05)。SBP患者的恶性肿瘤比例高于无SBP患者（P<0.05）。有和没有SBP的患者之间的大多数实验室检查指标显着差异（P<0.05）。白蛋白,中性粒细胞与淋巴细胞比率（NLR），和铁蛋白中性粒细胞比率（FNR）被发现是独立相关的失代偿期肝硬化患者使用LASSO算法，和逻辑回归分析。3个指标建立的模型具有较高的预测价值,AUC为0.808。此外，嗜中性粒细胞增多,ALP,和C反应蛋白白蛋白比（CAR）与失代偿期肝硬化患者的生存时间较短有关，这些指标的组合对肝硬化患者显示出更大的预测价值。
结论：本研究将FNR确定为肝硬化失代偿期患者SBP诊断的新指标。基于中性粒细胞的模型，ALP和CAR在预测失代偿期肝硬化患者的预后方面表现出良好的表现。
方法：我们进行了回顾性研究，包括2013年3月至2022年7月广西医科大学第一附属医院413例肝硬化患者。收集并分析患者的临床特征和实验室指标。采用两种机器学习方法（Xgboost和LASSO算法）和逻辑回归分析来筛选和验证与SBP风险相关的指标。使用估计的曲线下面积(AUC)构建并验证预测模型。还分析了与肝硬化患者生存相关的指标。
结果：本研究共纳入413例肝硬化患者，其中329人失代偿，84人获得补偿。52例合并SBP的患者Child-Pugh评分较差(P<0.05)。SBP患者的恶性肿瘤比例高于无SBP患者（P<0.05）。有和没有SBP的患者之间的大多数实验室检查指标显着差异（P<0.05）。白蛋白,中性粒细胞与淋巴细胞比率（NLR），和铁蛋白中性粒细胞比率（FNR）被发现是独立相关的失代偿期肝硬化患者使用LASSO算法，和逻辑回归分析。3个指标建立的模型具有较高的预测价值,AUC为0.808。此外，嗜中性粒细胞增多,ALP,和C反应蛋白白蛋白比（CAR）与失代偿期肝硬化患者的生存时间较短有关，这些指标的组合对肝硬化患者显示出更大的预测价值。
结论：本研究将FNR确定为肝硬化失代偿期患者SBP诊断的新指标。基于中性粒细胞的模型，ALP和CAR在预测失代偿期肝硬化患者的预后方面表现出良好的表现。
