Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City, UT 84132, USA.
CIWEC Hospital Director, CIWEC Hospital, Kathmandu 44600, Nepal.
J Travel Med. 2022 Jul 14;29(4). doi: 10.1093/jtm/taac012.
Clinicians and travellers often have limited tools to differentiate bacterial from non-bacterial causes of travellers' diarrhoea (TD). Development of a clinical prediction rule assessing the aetiology of TD may help identify episodes of bacterial diarrhoea and limit inappropriate antibiotic use. We aimed to identify predictors of bacterial diarrhoea among clinical, demographic and weather variables, as well as to develop and cross-validate a parsimonious predictive model.
We collected de-identified clinical data from 457 international travellers with acute diarrhoea presenting to two healthcare centres in Nepal and Thailand. We used conventional microbiologic and multiplex molecular methods to identify diarrheal aetiology from stool samples. We used random forest and logistic regression to determine predictors of bacterial diarrhoea.
We identified 195 cases of bacterial aetiology, 63 viral, 125 mixed pathogens, 6 protozoal/parasite and 68 cases without a detected pathogen. Random forest regression indicated that the strongest predictors of bacterial over viral or non-detected aetiologies were average location-specific environmental temperature and red blood cell on stool microscopy. In 5-fold cross-validation, the parsimonious model with the highest discriminative performance had an area under the receiver operator curve of 0.73 using 3 variables with calibration intercept -0.01 (standard deviation, SD 0.31) and slope 0.95 (SD 0.36).
We identified environmental temperature, a location-specific parameter, as an important predictor of bacterial TD, among traditional patient-specific parameters predictive of aetiology. Future work includes further validation and the development of a clinical decision-support tool to inform appropriate use of antibiotics in TD.
临床医生和旅行者通常缺乏工具来区分旅行者腹泻（TD）的细菌和非细菌性病因。开发一种评估 TD 病因的临床预测规则可能有助于识别细菌性腹泻发作并限制不合理使用抗生素。我们旨在确定临床、人口统计学和天气变量中与细菌性腹泻相关的预测因素，并开发和交叉验证一个简单的预测模型。
我们从尼泊尔和泰国的两个医疗中心就诊的 457 名患有急性腹泻的国际旅行者中收集了去识别化的临床数据。我们使用常规微生物学和多重分子方法从粪便样本中确定腹泻病因。我们使用随机森林和逻辑回归来确定细菌性腹泻的预测因素。
我们确定了 195 例细菌病因、63 例病毒病因、125 例混合病原体病因、6 例原虫/寄生虫病因和 68 例未检测到病原体病因。随机森林回归表明，细菌病因与病毒病因或未检测到病原体病因相比，最强的预测因素是平均特定地理位置的环境温度和粪便显微镜检查中的红细胞。在 5 折交叉验证中，具有最高判别性能的简约模型使用了 3 个变量，其中包括校准截距-0.01（标准差 0.31）和斜率 0.95（标准差 0.36），其接收者操作特征曲线下面积为 0.73。
我们确定了环境温度（一个特定地理位置的参数）作为细菌性 TD 的重要预测因素，这是预测病因的传统患者特定参数之一。未来的工作包括进一步验证和开发一种临床决策支持工具，以告知 TD 中抗生素的合理使用。
