PDF(Pubmed)
Abstract：
BACKGROUND: Colonization of carbapenem-resistant Enterobacterale (CRE) is considered as one of vital preconditions for infection, with corresponding high morbidity and mortality. It is important to construct a reliable prediction model for those CRE carriers with high risk of infection.
METHODS: A retrospective cohort study was conducted in two Chinese tertiary hospitals for patients with CRE colonization from 2011 to 2021. Univariable analysis and the Fine-Gray sub-distribution hazard model were utilized to identify potential predictors for CRE-colonized infection, while death was the competing event. A nomogram was established to predict 30-day and 60-day risk of CRE-colonized infection.
RESULTS: 879 eligible patients were enrolled in our study and divided into training (n = 761) and validation (n = 118) group, respectively. There were 196 (25.8%) patients suffered from subsequent CRE infection. The median duration of subsequent infection after identification of CRE colonization was 20 (interquartile range [IQR], 14-32) days. Multisite colonization, polymicrobial colonization, catheterization and receiving albumin after colonization, concomitant respiratory diseases, receiving carbapenems and antimicrobial combination therapy before CRE colonization within 90 days were included in final model. Model discrimination and calibration were acceptable for predicting the probability of 60-day CRE-colonized infection in both training (area under the curve [AUC], 74.7) and validation dataset (AUC, 81.1). Decision-curve analysis revealed a significantly better net benefit in current model. Our prediction model is freely available online at https://ken-zheng.shinyapps.io/PredictingModelofCREcolonizedInfection/ .
CONCLUSIONS: Our nomogram has a good predictive performance and could contribute to early identification of CRE carriers with a high-risk of subsequent infection, although external validation would be required.
METHODS: A retrospective cohort study was conducted in two Chinese tertiary hospitals for patients with CRE colonization from 2011 to 2021. Univariable analysis and the Fine-Gray sub-distribution hazard model were utilized to identify potential predictors for CRE-colonized infection, while death was the competing event. A nomogram was established to predict 30-day and 60-day risk of CRE-colonized infection.
RESULTS: 879 eligible patients were enrolled in our study and divided into training (n = 761) and validation (n = 118) group, respectively. There were 196 (25.8%) patients suffered from subsequent CRE infection. The median duration of subsequent infection after identification of CRE colonization was 20 (interquartile range [IQR], 14-32) days. Multisite colonization, polymicrobial colonization, catheterization and receiving albumin after colonization, concomitant respiratory diseases, receiving carbapenems and antimicrobial combination therapy before CRE colonization within 90 days were included in final model. Model discrimination and calibration were acceptable for predicting the probability of 60-day CRE-colonized infection in both training (area under the curve [AUC], 74.7) and validation dataset (AUC, 81.1). Decision-curve analysis revealed a significantly better net benefit in current model. Our prediction model is freely available online at https://ken-zheng.shinyapps.io/PredictingModelofCREcolonizedInfection/ .
CONCLUSIONS: Our nomogram has a good predictive performance and could contribute to early identification of CRE carriers with a high-risk of subsequent infection, although external validation would be required.
摘要：
背景：耐碳青霉烯的肠杆菌（CRE）的定植被认为是感染的重要前提之一，相应的高发病率和高死亡率。对于那些感染风险较高的CRE携带者，构建可靠的预测模型非常重要。
方法：2011年至2021年，在中国两家三级医院对CRE定植患者进行了回顾性队列研究。单变量分析和Fine-Gray子分布风险模型用于确定CRE定植感染的潜在预测因子。而死亡是竞争事件。建立列线图来预测CRE定植感染的30天和60天风险。
结果：879名符合条件的患者被纳入我们的研究，并分为培训组（n=761）和验证组（n=118），分别。有196名(25.8%)患者遭受随后的CRE感染。确定CRE定植后，随后感染的中位持续时间为20（四分位距[IQR]，14-32）天。多点定植，多微生物定植，导管插入和定植后接受白蛋白，伴随的呼吸道疾病，在90天内CRE定植前接受碳青霉烯类抗生素和抗菌药物联合治疗纳入最终模型.模型区分和校准对于预测两种训练中60天CRE定植感染的概率是可接受的(曲线下面积[AUC],74.7)和验证数据集(AUC，81.1).决策曲线分析显示，当前模型的净收益明显更好。我们的预测模型可在https://ken-zheng在线免费获得。shinyapps.io/PredictingModelofCREcolonizedInfection/.
结论：我们的列线图具有良好的预测性能，可能有助于早期识别具有随后感染高风险的CRE携带者，尽管需要外部验证。
方法：2011年至2021年，在中国两家三级医院对CRE定植患者进行了回顾性队列研究。单变量分析和Fine-Gray子分布风险模型用于确定CRE定植感染的潜在预测因子。而死亡是竞争事件。建立列线图来预测CRE定植感染的30天和60天风险。
结果：879名符合条件的患者被纳入我们的研究，并分为培训组（n=761）和验证组（n=118），分别。有196名(25.8%)患者遭受随后的CRE感染。确定CRE定植后，随后感染的中位持续时间为20（四分位距[IQR]，14-32）天。多点定植，多微生物定植，导管插入和定植后接受白蛋白，伴随的呼吸道疾病，在90天内CRE定植前接受碳青霉烯类抗生素和抗菌药物联合治疗纳入最终模型.模型区分和校准对于预测两种训练中60天CRE定植感染的概率是可接受的(曲线下面积[AUC],74.7)和验证数据集(AUC，81.1).决策曲线分析显示，当前模型的净收益明显更好。我们的预测模型可在https://ken-zheng在线免费获得。shinyapps.io/PredictingModelofCREcolonizedInfection/.
结论：我们的列线图具有良好的预测性能，可能有助于早期识别具有随后感染高风险的CRE携带者，尽管需要外部验证。
