Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Department of Medical Insurance, School of Public Health, Southeast University, Nanjing, China.
GuiZhou Healthcare Associated Infection Training Base, Center for Infectious Diseases, GuiZhou Provincial People's Hospital, Guiyang, China.
PLoS One. 2019 Jul 15;14(7):e0219456. doi: 10.1371/journal.pone.0219456. eCollection 2019.
To develop and validate an interactive nomogram to predict healthcare-associated infections (HCAIs) in the intensive care unit (ICU).
A multicenter retrospective study was conducted to review 2017 data from six hospitals in Guizhou Province, China. A total of 1,782 ICU inpatients were divided into either a training set (n = 1,189) or a validation set (n = 593). The patients' demographic characteristics, basic clinical features from the previous admission, and their need for bacterial culture during the current admission were extracted from electronic medical records of the hospitals to predict HCAI. Univariate and multivariable analyses were used to identify independent risk factors of HCAI in the training set. The multivariable model's performance was evaluated in both the training set and the validation set, and an interactive nomogram was constructed according to multivariable regression model. Moreover, the interactive nomogram was used to predict the possibility of a patient developing an HCAI based on their prior admission data. Finally, the clinical usefulness of the interactive nomogram was estimated by decision analysis using the entire dataset.
The nomogram model included factor development (local economic development levels), length of stay (LOS; days of hospital stay), fever (days of persistent fever), diabetes (history of diabetes), cancer (history of cancer) and culture (the need for bacterial culture). The model showed good calibration and discrimination in the training set [area under the curve (AUC), 0.871; 95% confidence interval (CI), 0.848-0.894] and in the validation set (AUC, 0.862; 95% CI, 0.829-0.895). The decision curve demonstrated the clinical usefulness of our interactive nomogram.
The developed interactive nomogram is a simple and practical instrument for quantifying the individual risk of HCAI and promptly identifying high-risk patients.
开发并验证一种用于预测重症监护病房（ICU）中医疗保健相关性感染（HCAI）的交互式列线图。
进行了一项多中心回顾性研究，以分析中国贵州省六家医院 2017 年的数据。将 1782 名 ICU 住院患者分为训练集（n = 1189）和验证集（n = 593）。从医院的电子病历中提取患者的人口统计学特征、上次入院的基本临床特征以及本次入院时进行细菌培养的需求，以预测 HCAI。在训练集中，使用单变量和多变量分析来确定 HCAI 的独立危险因素。在训练集和验证集中评估多变量模型的性能，并根据多变量回归模型构建交互式列线图。此外，根据患者的既往入院数据，使用交互式列线图预测患者发生 HCAI 的可能性。最后，使用整个数据集的决策分析来评估交互式列线图的临床实用性。
该列线图模型包括因素发展（当地经济发展水平）、住院时间（LOS；住院天数）、发热（持续发热天数）、糖尿病（糖尿病史）、癌症（癌症史）和培养（细菌培养的需求）。该模型在训练集（曲线下面积（AUC），0.871；95%置信区间（CI），0.848-0.894）和验证集（AUC，0.862；95%CI，0.829-0.895）中均显示出良好的校准和区分度。决策曲线表明了我们的交互式列线图的临床实用性。
所开发的交互式列线图是一种用于量化 HCAI 个体风险和及时识别高危患者的简单实用工具。
