Li Haoran, Chen Xi, Feng Hui, Liu Fangyi, Yu Jie, Liang Ping
Department of Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China.
Department of Ultrasound, The Third Central Hospital of Tianjin, Tianjin, People's Republic of China.
Infect Drug Resist. 2024 Sep 29;17:4237-4249. doi: 10.2147/IDR.S468251. eCollection 2024.
The diagnosis of liver abscess (LA) caused by and depends on ultrasonography, but it is difficult to distinguish the overlapping features. Valuable ultrasonic (US) features were extracted to distinguish -LA and -LA and establish the relevant prediction model.
We retrospectively analyzed seven clinical features, three laboratory indicators and 11 US features of consecutive patients with LA from April 2013 to December 2023. Patients with LA were randomly divided into training group (n=262) and validation group (n=174) according to a ratio of 6:4. Univariate logistic regression and LASSO regression were used to establish prediction models. The performance of the model was evaluated using area under the curve(AUC), calibration curves, and decision curve analysis (DCA), and subsequently validated in the validation group.
A total of 436 participants (median age: 55 years; range: 42-68 years; 144 women) were evaluated, including 369 participants with -LA and 67 with -LA, respectively. A total of 11 predictors by LASSO regression analysis, which included gender, age, the liver background, internal gas bubble, echogenic debris, wall thickening, whether the inner wall is worm-eaten, temperature, diabetes mellitus, hepatobiliary surgery and neutrophil(NEUT). The performance of the Nomogram prediction model distinguished between -LA and -LA was 0.80, 95% confidence interval [CI] (0.73-0.87). In the validation group, the AUC of GNB was 0.79, 95% CI (0.69-0.89).
A model for predicting the risk of -LA was established to help diagnose pathogenic organism of LA earlier, which could help select sensitive antibiotics before the results of drug-sensitive culture available, thereby shorten the treatment time of patients.
由[具体病原体1]和[具体病原体2]引起的肝脓肿（LA）的诊断依赖于超声检查，但难以区分其重叠特征。提取有价值的超声（US）特征以区分[病原体1] - LA和[病原体2] - LA并建立相关预测模型。
回顾性分析2013年4月至2023年12月连续的LA患者的七个临床特征、三个实验室指标和11个US特征。LA患者按6:4的比例随机分为训练组（n = 262）和验证组（n = 174）。采用单因素逻辑回归和LASSO回归建立预测模型。使用曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估模型性能，随后在验证组中进行验证。
共评估了436名参与者（中位年龄：55岁；范围：42 - 68岁；144名女性），其中分别有369名[病原体1] - LA参与者和67名[病原体2] - LA参与者。通过LASSO回归分析共得到11个预测因子，包括性别、年龄、肝脏背景、内部气泡、回声性碎屑、壁增厚、内壁是否呈虫蚀状、体温、糖尿病、肝胆手术史和中性粒细胞（NEUT）。列线图预测模型区分[病原体1] - LA和[病原体2] - LA的性能为0.80，95%置信区间[CI]（0.73 - 0.87）。在验证组中，广义朴素贝叶斯（GNB）的AUC为0.79，95% CI（0.69 - 0.89）。
建立了一种预测[病原体1] - LA风险的模型，有助于更早地诊断LA的致病病原体，这可以在药敏培养结果出来之前帮助选择敏感抗生素，从而缩短患者的治疗时间。
