Department of General Internal Medicine, St. Luke's International Hospital, Tokyo, Japan.
Division of General Internal Medicine, Department of Internal Medicine, Tokyo Medical University Ibaraki Medical Center, Japan.
J Infect Chemother. 2023 Jan;29(1):7-14. doi: 10.1016/j.jiac.2022.09.002. Epub 2022 Sep 9.
Cefmetazole is used as the first-line treatment for intra-abdominal infections. However, only a few studies have investigated the risk factors for cefmetazole treatment failure.
This study aimed to develop a decision tree-based predictive model to assess the effectiveness of cefmetazole in initial intra-abdominal infection treatment to improve the clinical treatment strategies.
This retrospective cohort study included adult patients who were unexpectedly hospitalized due to intra-abdominal infections between 2003 and 2020 and initially treated with cefmetazole. The primary outcome was clinical intra-abdominal infection improvement. The chi-square automatic interaction detector decision tree analysis was used to create a predictive model for clinical improvement after cefmetazole treatment.
Among 2,194 patients, 1,807 (82.4%) showed clinical improvement post-treatment; their mean age was 48.7 (standard deviation: 18.8) years, and 1,213 (55.3%) patients were men. The intra-abdomせinal infections were appendicitis (n = 1,186, 54.1%), diverticulitis (n = 334, 15.2%), and pancreatitis (n = 285, 13.0%). The chi-square automatic interaction detector decision tree analysis identified the intra-abdominal infection type, C-reactive protein level, heart rate, and body temperature as predictive factors by categorizing patients into seven groups. The area under the receiver operating characteristic curve was 0.71 (95% confidence interval: 0.68-0.73).
This predictive model is easily understandable visually and may be applied in clinical practice.
头孢美唑被用作治疗腹腔内感染的一线药物。然而，仅有少数研究调查了头孢美唑治疗失败的风险因素。
本研究旨在建立基于决策树的预测模型，以评估头孢美唑在初始腹腔内感染治疗中的有效性，从而改善临床治疗策略。
这是一项回顾性队列研究，纳入了 2003 年至 2020 年间因腹腔内感染而意外住院并最初接受头孢美唑治疗的成年患者。主要结局是腹腔内感染的临床改善。采用卡方自动交互检测决策树分析，建立头孢美唑治疗后临床改善的预测模型。
在 2194 名患者中，1807 名（82.4%）在治疗后表现出临床改善；他们的平均年龄为 48.7 岁（标准差：18.8），1213 名（55.3%）患者为男性。腹腔内感染的类型为阑尾炎（n=1186，54.1%）、憩室炎（n=334，15.2%）和胰腺炎（n=285，13.0%）。卡方自动交互检测决策树分析通过将患者分为七组，确定了腹腔内感染类型、C 反应蛋白水平、心率和体温作为预测因素。受试者工作特征曲线下面积为 0.71（95%置信区间：0.68-0.73）。
该预测模型具有直观易懂的特点，可能适用于临床实践。
