Department of Infectious Diseases, Taicang First People's Hospital, Taicang, 215400, Jiangsu Province, China.
Department of Infectious Diseases, Taicang First People's Hospital, Taicang, 215400, Jiangsu Province, China.
Ann Hepatol. 2022 Jul-Aug;27(4):100713. doi: 10.1016/j.aohep.2022.100713. Epub 2022 Apr 29.
Infection is a common complication of liver failure. Serum inflammatory markers used to diagnose infection have sufficient diagnostic sensitivity but low specificity. This study aimed to improve the early diagnosis of infections in liver failure patients by developing a diagnostic model and evaluating its predictive ability.
A retrospective analysis of clinical data from liver failure patients. Cases were divided into infected and non-infected groups according to their clinical diagnosis. Nine infection-related predictors (age, body temperature, neutrophil ratio (NE%), procalcitonin (PCT), C-reactive protein (CRP), lactic acid (Lac), serum albumin (Alb), model of end-stage liver disease (MELD) score, and sequential organ failure assessment (SOFA) score) were included in multivariate logistic regression analysis. The diagnostic model was validated, and the receiver operating characteristic (ROC) curve was used to analyze its predictive accuracy.
In the model group, multivariate logistic regression analysis showed that age, body temperature, PCT, CRP, Lac, and SOFA score were independent predictors of infection associated with liver failure (P < 0.05). The area under the ROC curve (AUC) of the model was 0.899 (95% confidence interval [CI] 0.846-0.939), and the sensitivity and specificity were 86.2% and 80.4%, respectively. The AUC for the validation group was 0.953 (95% CI 0.899-0.983), and the sensitivity and specificity were 91.7% and 84.2%, respectively.
This study reports a model for early diagnosis of infection in liver failure patients. The model had high overall accuracy and showed good reproducibility and reliability in patients from different centers in the same region.
感染是肝功能衰竭的常见并发症。用于诊断感染的血清炎症标志物具有足够的诊断敏感性，但特异性较低。本研究旨在通过开发诊断模型并评估其预测能力，提高肝功能衰竭患者感染的早期诊断能力。
对肝功能衰竭患者的临床数据进行回顾性分析。根据临床诊断将病例分为感染组和非感染组。将年龄、体温、中性粒细胞比例（NE%）、降钙素原（PCT）、C 反应蛋白（CRP）、乳酸（Lac）、血清白蛋白（Alb）、终末期肝病模型（MELD）评分和序贯器官衰竭评估（SOFA）评分等 9 个感染相关预测因素纳入多变量逻辑回归分析。对诊断模型进行验证，并使用受试者工作特征（ROC）曲线分析其预测准确性。
在模型组中，多变量逻辑回归分析显示，年龄、体温、PCT、CRP、Lac 和 SOFA 评分是与肝功能衰竭相关感染的独立预测因素（P<0.05）。模型的 ROC 曲线下面积（AUC）为 0.899（95%置信区间 0.846-0.939），灵敏度和特异度分别为 86.2%和 80.4%。验证组的 AUC 为 0.953（95%置信区间 0.899-0.983），灵敏度和特异度分别为 91.7%和 84.2%。
本研究报告了一种用于早期诊断肝功能衰竭患者感染的模型。该模型具有较高的整体准确性，在同一地区不同中心的患者中表现出良好的可重复性和可靠性。
