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Predicting the occurrence of multidrug-resistant organism colonization or infection in ICU patients: development and validation of a novel multivariate prediction model.
Antimicrobial Resistance & Infection Control ( IF 5.5 ) Pub Date : 2020-05-19 , DOI: 10.1186/s13756-020-00726-5
Li Wang 1 , Xiaolong Huang 2 , Jiating Zhou 2 , Yajing Wang 1 , Weizhang Zhong 1 , Qing Yu 1 , Weiping Wang 1 , Zhiqiao Ye 1 , Qiaoyan Lin 1 , Xing Hong 1 , Ping Zeng 1 , Minwei Zhang 2
Affiliation  

BACKGROUND Multidrug-resistant organisms (MDROs) have emerged as an important cause of poor prognoses of patients in the intensive care unit (ICU). This study aimed to establish an easy-to-use nomogram for predicting the occurrence of MDRO colonization or infection in ICU patients. METHODS In this study, we developed a nomogram based on predictors in patients admitted to the ICU in the First Affiliated Hospital of Xiamen University from 2016 to 2018 using univariate and multivariate logistic regression analysis. We externally validated this nomogram in patients from another hospital over a similar period, and assessed its performance by calculating the area under the receiver operating characteristic (ROC) curve (AUC) and performing a decision curve analysis. RESULTS 331 patients in the primary cohort and 181 patients in the validation cohort were included in the statistical analysis. Independent factors derived from the primary cohort to predict MDRO colonization or infection were male sex, higher C-reactive protein (CRP) levels and higher Pitt bacteremia scores (Pitt scores), which were all assembled in the nomogram. The nomogram yielded good discrimination with an AUC of 0.77 (95% CI 0.70-0.84), and the range of threshold probabilities of decision curves was approximately 30-95%. CONCLUSION This easy-to-use nomogram is potentially useful for predicting the occurrence of MDRO colonization or infection in ICU patients.

中文翻译:

预测ICU患者中多药耐药菌定植或感染的发生:新型多变量预测模型的开发和验证。

背景技术多重耐药性生物(MDRO)已经成为重症监护病房(ICU)患者预后不良的重要原因。这项研究旨在建立一个易于使用的列线图,以预测ICU患者MDRO定植或感染的发生。方法在本研究中,我们使用单因素和多因素logistic回归分析,基于2016年至2018年厦门大学第一附属医院ICU入院患者的预测因子开发了诺模图。我们从外部验证了另一家医院在相似时期内的诺模图,并通过计算接收器工作特征(ROC)曲线(AUC)下的面积并执行决策曲线分析来评估其性能。结果统计分析中包括了主要队列中的331例患者和验证队列中的181例患者。从主要人群中预测MDRO定植或感染的独立因素是男性,较高的C反应蛋白(CRP)水平和较高的Pitt菌血症评分(Pitt评分),这些均在诺模图中汇总。列线图产生良好的辨别力,AUC为0.77(95%CI为0.70-0.84),决策曲线的阈值概率范围约为30-95%。结论该易于使用的列线图可能有助于预测ICU患者MDRO定植或感染的发生。较高的C反应蛋白(CRP)水平和较高的Pitt菌血症评分(Pitt评分),均在诺模图中汇总。列线图产生良好的辨别力,AUC为0.77(95%CI为0.70-0.84),决策曲线的阈值概率范围约为30-95%。结论该易于使用的列线图可能有助于预测ICU患者MDRO定植或感染的发生。较高的C反应蛋白(CRP)水平和较高的Pitt菌血症评分(Pitt评分),均在诺模图中汇总。列线图产生良好的辨别力,AUC为0.77(95%CI为0.70-0.84),决策曲线的阈值概率范围约为30-95%。结论该易于使用的列线图可能有助于预测ICU患者MDRO定植或感染的发生。
更新日期:2020-05-19
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