Varkila Meri R J, Lancia Giacomo, van Smeden Maarten, Bonten Marc J M, Spitoni Cristian, Cremer Olaf L
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Department of Intensive Care Medicine, University Medical Center Utrecht, Utrecht, Netherlands.
BMC Med Inform Decis Mak. 2025 Jul 21;25(1):273. doi: 10.1186/s12911-025-03031-6.
Nosocomial infections are a major cause of morbidity and mortality in the ICU. Earlier identification of these complications may facilitate better clinical management and improve outcomes. We developed a dynamic prediction model that leveraged high-frequency longitudinal data to estimate infection risk 48 h ahead of clinically overt deterioration.
We used electronic health record data from consecutive adults who had been treated for > 48 h in a mixed tertiary ICU in the Netherlands enrolled in the Molecular Diagnosis and Risk Stratification of Sepsis (MARS) cohort from 2011 to 2018. All infectious episodes were prospectively adjudicated. ICU-acquired infection (ICU-AI) risk was estimated using a Cox landmark model with high-resolution vital sign data processed via a convolutional neural network (CNN).
We studied 32,178 observation days in 4444 patients and observed 1197 infections, yielding an overall infection risk of 3.5% per ICU day. Discrimination of the composite model was moderate with c-index values varying between 0.64 (95%CI: 0.58-0.69) and 0.72 (95%CI: 0.66-0.78) across timepoints, with some overestimation of ICU-AI risk overall (mean calibration slope 0.58). Compared to 38 common features of infection, a CNN risk score derived from five vital sign signals consistently ranked as a strong predictor of ICU-AI across all time points but did not substantially change risk prediction of ICU-AI.
A dynamic modelling approach that incorporates machine learning of high-frequency vital sign data shows promise as a continuous bedside index of infection risk. Further validation is needed to weigh added complexity and interpretability of the deep learning model against potential benefits for clinical decision support in the ICU.
医院感染是重症监护病房（ICU）发病和死亡的主要原因。尽早识别这些并发症可能有助于更好的临床管理并改善预后。我们开发了一种动态预测模型，该模型利用高频纵向数据在临床明显恶化前48小时估计感染风险。
我们使用了来自荷兰一家综合性三级ICU连续治疗超过48小时的成年患者的电子健康记录数据，这些患者纳入了2011年至2018年的脓毒症分子诊断和风险分层（MARS）队列。所有感染事件均进行前瞻性判定。使用Cox标志性模型估计ICU获得性感染（ICU-AI）风险，该模型利用通过卷积神经网络（CNN）处理的高分辨率生命体征数据。
我们研究了4444例患者的32178个观察日，观察到1197例感染，ICU每日总体感染风险为3.5%。复合模型的辨别力中等，各时间点的c指数值在0.64（95%CI：0.58-0.69）和0.72（95%CI：0.66-0.78）之间变化，总体上对ICU-AI风险有一些高估（平均校准斜率0.58）。与38个常见感染特征相比，从五个生命体征信号得出的CNN风险评分在所有时间点始终被列为ICU-AI的强预测指标，但并未实质性改变ICU-AI的风险预测。
一种结合高频生命体征数据机器学习的动态建模方法显示出有望作为感染风险的连续床边指标。需要进一步验证，以权衡深度学习模型增加的复杂性和可解释性与对ICU临床决策支持的潜在益处。
