Department of Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands.
Department of Internal Medicine, Alrijne Hospital, Leiderdorp, The Netherlands.
Int J Clin Pract. 2020 Nov;74(11):e13601. doi: 10.1111/ijcp.13601. Epub 2020 Jul 14.
A cornerstone in the management of Staphylococcus aureus bacteraemia (SAB) is the differentiation between a complicated and an uncomplicated SAB course. The ability to early and accurately identify patients with - and without - complicated bacteraemia may optimise the utility of diagnostics and prevent unnecessary prolonged antibiotic therapy.
Development and validation of a prediction score in SAB using demographic, clinical, and laboratory data from two independent Dutch cohorts; estimating the risk of complicated disease at the time of the first positive blood culture. Models were developed using logistic regression and evaluated by c-statistics, ie area under the ROC-curve, and negative predictive values (NPV).
The development- and validation cohorts included 150 and 183 patients, respectively. The most optimal prediction model included: mean arterial pressure, signs of metastatic infection on physical examination, leucocyte count, urea level and time to positivity of blood cultures (c-statistic 0.82, 95% CI 0.74-0.89). In the validation cohort, the c-statistic of the prediction score was 0,77 (95% CI 0.69-0.84). The NPV for complicated disease for patients with a score of ≤2 was 0.83 (95% CI 0.68-0.92), with a negative likelihood ratio of 0.14 (95% CI 0.06-0.31).
The early SAB risk score helps to identify patients with high probability of uncomplicated SAB. However, the risk score's lacked absolute discriminative power to guide decisions on the management of all patients with SAB on its own. The heterogenicity of the disease and inconsistency in definitions of complicated SAB are important challenges in the development of clinical rules to guide the management of SAB.
金黄色葡萄球菌菌血症(SAB)管理的基石在于区分复杂和不复杂的 SAB 病程。能够早期准确识别具有和不具有复杂菌血症的患者,可能优化诊断的效用并防止不必要的延长抗生素治疗。
使用来自两个独立荷兰队列的人口统计学、临床和实验室数据,在 SAB 中开发和验证预测评分;估计首次阳性血培养时复杂疾病的风险。使用逻辑回归开发模型,并通过 C 统计量(ROC 曲线下面积)和阴性预测值(NPV)进行评估。
开发和验证队列分别包括 150 名和 183 名患者。最优化的预测模型包括:平均动脉压、体检时转移性感染的迹象、白细胞计数、尿素水平和血培养阳性时间(C 统计量 0.82,95%CI 0.74-0.89)。在验证队列中,预测评分的 C 统计量为 0.77(95%CI 0.69-0.84)。评分≤2 的患者发生复杂疾病的 NPV 为 0.83(95%CI 0.68-0.92),阴性似然比为 0.14(95%CI 0.06-0.31)。
早期 SAB 风险评分有助于识别具有高概率不复杂 SAB 的患者。然而,风险评分缺乏绝对判别能力,无法单独指导所有 SAB 患者的管理决策。疾病的异质性和复杂 SAB 的定义不一致是开发指导 SAB 管理的临床规则的重要挑战。