Risk predict model using multi-drug resistant organism infection from Neuro-ICU patients: a retrospective cohort study

Jiang, Hu; Pu, Hengping; Huang, Nanqu

doi:10.1038/s41598-023-42522-2

Cited by 1 publication

(1 citation statement)

References 42 publications

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“…Wang et al employed data from 688 ICU patients, utilizing Lasso and stepwise regression to extract nine independent MDRO infection risk factors for a backpropagation neural network (BPNN) model, validated externally with an AUC of 0.811 [ 39 ]. Jiang et al analyzed data from 297 neuro ICU patients, finding tracheal intubation, arterial blood pressure monitoring, fever, antibiotic use, and pneumonia as independent MDRO infection risk factors through binary logistic regression [ 40 ]. While these studies highlight relevant risk factors, implementing these models directly in hospitals poses challenges.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data

Li,

Cao,

Wang

et al. 2024

Antimicrob Resist Infect Control

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Background Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO proliferation. Timely identification of patients at high risk for MDRO can aid in curbing transmission, enhancing patient outcomes, and maintaining the cleanliness of the ICU environment. This study focused on developing a machine learning (ML) model to identify patients at risk of MDRO during the initial phase of their ICU stay. Methods Utilizing patient data from the First Medical Center of the People’s Liberation Army General Hospital (PLAGH-ICU) and the Medical Information Mart for Intensive Care (MIMIC-IV), the study analyzed variables within 24 h of ICU admission. Machine learning algorithms were applied to these datasets, emphasizing the early detection of MDRO colonization or infection. Model efficacy was evaluated by the area under the receiver operating characteristics curve (AUROC), alongside internal and external validation sets. Results The study evaluated 3,536 patients in PLAGH-ICU and 34,923 in MIMIC-IV, revealing MDRO prevalence of 11.96% and 8.81%, respectively. Significant differences in ICU and hospital stays, along with mortality rates, were observed between MDRO positive and negative patients. In the temporal validation, the PLAGH-ICU model achieved an AUROC of 0.786 [0.748, 0.825], while the MIMIC-IV model reached 0.744 [0.723, 0.766]. External validation demonstrated reduced model performance across different datasets. Key predictors included biochemical markers and the duration of pre-ICU hospital stay. Conclusions The ML models developed in this study demonstrated their capability in early identification of MDRO risks in ICU patients. Continuous refinement and validation in varied clinical contexts remain essential for future applications.

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Section: Discussionmentioning

confidence: 99%