An interactive nomogram to predict healthcare-associated infections in ICU patients: A multicenter study in GuiZhou Province, China

ObjectiveTo develop and validate an interactive nomogram to predict healthcare-associated infections (HCAIs) in the intensive care unit (ICU).MethodsA 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.ResultsThe 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.ConclusionsThe developed interactive nomogram is a simple and practical instrument for quantifying the individual risk of HCAI and promptly identifying high-risk patients.

**目的** 开发并验证一款交互式列线图（nomogram），用于预测重症监护病房（intensive care unit, ICU）内的医疗相关性感染（healthcare-associated infections, HCAIs）。
**方法** 本研究开展多中心回顾性研究，回顾分析中国贵州省6家医院2017年的住院病例数据。最终纳入1782例ICU住院患者，按比例分为训练集（n=1189）与验证集（n=593）。从各医院的电子病历中提取患者人口统计学特征、既往住院基础临床资料，以及本次住院期间的细菌培养需求等信息，用于构建医疗相关性感染预测模型。在训练集中采用单因素及多因素分析筛选医疗相关性感染的独立危险因素。基于多因素回归模型构建交互式列线图，并分别在训练集与验证集中评估该模型的预测性能。随后利用该交互式列线图，基于患者的既往住院资料预测其发生医疗相关性感染的风险。最后采用全数据集进行决策曲线分析，评估该列线图的临床实用性。
**结果** 本研究构建的列线图模型纳入以下危险因素：区域经济发展水平、住院时长（length of stay, LOS；住院天数）、持续发热天数、糖尿病病史、恶性肿瘤病史，以及细菌培养需求。该模型在训练集与验证集中均表现出良好的校准度与区分度：训练集的曲线下面积（area under the curve, AUC）为0.871，95%置信区间（confidence interval, CI）为0.848~0.894；验证集AUC为0.862，95%CI为0.829~0.895。决策曲线分析结果证实了本交互式列线图的临床应用价值。
**结论** 本研究开发的交互式列线图是一款简便实用的工具，可用于量化个体发生医疗相关性感染的风险，及时识别高危患者。