Appropriate antibiotic prescribing is key to combating antimicrobial resistance. Upper respiratory tract infections (URTIs) are common reasons for emergency department (ED) visits and antibiotic use. Differentiating between bacterial and viral infections is not straightforward. We aim to provide an evidence-based clinical decision support tool for antibiotic prescribing using prediction models developed from local data. Seven hundred-fifteen patients with uncomplicated URTI were recruited and analysed from Singapore’s busiest ED, Tan Tock Seng Hospital, from June 2016 to November 2018. Confirmatory tests were performed using the multiplex polymerase chain reaction (PCR) test for respiratory viruses and point-of-care test for C-reactive protein. Demographic, clinical and laboratory data were extracted from the hospital electronic medical records. Seventy percent of the data was used for training and the remaining 30% was used for validation. Decision trees, LASSO and logistic regression models were built to predict when antibiotics were not needed. The median age of the cohort was 36 years old, with 61.2% being male. Temperature and pulse rate were significant factors in all 3 models. The area under the receiver operating curve (AUC) on the validation set for the models were similar. (LASSO: 0.70 [95% CI: 0.62–0.77], logistic regression: 0.72 [95% CI: 0.65–0.79], decision tree: 0.67 [95% CI: 0.59–0.74]). Combining the results from all models, 58.3% of study participants would not need antibiotics. The models can be easily deployed as a decision support tool to guide antibiotic prescribing in busy EDs.

中文翻译：

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指导抗生素处方的风险预测模型：对急诊科无并发症上呼吸道感染成年患者的研究

适当的抗生素处方是对抗抗菌素耐药性的关键。上呼吸道感染 (URTI) 是急诊 (ED) 就诊和使用抗生素的常见原因。区分细菌和病毒感染并不简单。我们的目标是使用根据本地数据开发的预测模型为抗生素处方提供基于证据的临床决策支持工具。2016 年 6 月至 2018 年 11 月，从新加坡最繁忙的急诊室陈笃生医院招募了 715 名患有无并发症的 URTI 患者并进行了分析。使用多重聚合酶链式反应 (PCR) 检测呼吸道病毒和定点检测进行了确认检测。 -C反应蛋白护理测试。从医院电子病历中提取人口统计、临床和实验室数据。70% 的数据用于训练，其余 30% 用于验证。建立决策树、LASSO 和逻辑回归模型来预测何时不需要抗生素。该队列的平均年龄为 36 岁，其中 61.2% 为男性。温度和脉搏率是所有 3 个模型中的重要因素。模型验证集的受试者工作曲线下面积 (AUC) 相似。（LASSO：0.70 [95% CI：0.62–0.77]，逻辑回归：0.72 [95% CI：0.65–0.79]，决策树：0.67 [95% CI：0.59–0.74]）。综合所有模型的结果，58.3% 的研究参与者不需要抗生素。这些模型可以轻松部署为决策支持工具，以指导繁忙的急诊室的抗生素处方。